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2019","short":"Issue 1.2","theme":"Fall 2019","special":false,"volume":"1","number":"2","date":"2019-11-01","doi":"10.1162/99608f92.af83430a","cover":"/media/remote/005c6e0e27a154f3-900.webp","toc":[{"heading":"From the Editor-in-Chief","note":null,"section":"editorscolumn","pubs":["hx3pgxjc"]},{"heading":"Panorama","note":"Overviews, Visions, and Debates","section":"panorama","pubs":["2mkokrzb","5irjez4q","iu26pfw1","qkgyckh8","cdkvbb76"]},{"heading":"Cornucopia","note":"Impact, Innovation, and Knowledge Transfer","section":"cornucopia","pubs":["18lm7jrp"]},{"heading":"Stepping Stones","note":"Learning, Teaching, and Communication","section":"stepping-stones","pubs":["rye9y1w0","0mb0zzlc"]},{"heading":"Milestones and Millstones","note":"Foundations, Theories, and Methods","section":"milestones-and-millstones","pubs":["owxwohyz","jduhd7og"]},{"heading":"Diving into Data","note":"Mini Tutorials on Concepts, Methods, and Tools","section":"diving-into-data","pubs":["8fm8lo1e"]},{"heading":"Mining the Past","note":"Brief Histories of Data Science","section":"mining-the-past","pubs":["3adoxb26"]},{"heading":"Bits and Bites","note":null,"section":"bits-and-bites","pubs":["f9kuryi8"]},{"heading":"Also in This Issue","note":null,"section":null,"pubs":["3cfhdct9","tn4j86t1"]}],"count":16},{"id":"72befc2a-54a3-46c5-b8d0-c7d36f72a21d","slug":"volume1issue1","title":"Issue 1.1, Summer 2019","short":"Issue 1.1","theme":"Summer 2019","special":false,"volume":"1","number":"1","date":"2019-07-03","doi":"10.1162/99608f92.72befc2a","cover":null,"toc":[{"heading":"From the Editor-in-Chief","note":null,"section":"editorscolumn","pubs":["jhy4g6eg"]},{"heading":"Panorama","note":"Overviews, Visions, and Debates","section":"panorama","pubs":["pjl0jtkp","4giycvvj","4ovhpe3v","577rq08d","a7gxkn0a"]},{"heading":"Featured Discussion","note":null,"section":null,"pubs":["wot7mkc1","pjebp7io","tgl23krp","djb16hzl","kyzf7fjv","u2econxe","pqddq8u5","wiq01ru6","esfh2s4w","4s0269ne","rim3pvdw","plto69bh","2imtstfu"]},{"heading":"Cornucopia","note":"Impact, Innovation, and Knowledge Transfer","section":"cornucopia","pubs":["xcq8a1v1","ct67j043"]},{"heading":"Stepping Stones","note":"Learning, Teaching, and Communication","section":"stepping-stones","pubs":["z4sb5j9l"]},{"heading":"Milestones and Millstones","note":"Foundations, Theories, and Methods","section":"milestones-and-millstones","pubs":["9mdn32tq","l0jsh9d1"]},{"heading":"Diving into Data","note":"Mini Tutorials on Concepts, Methods, and Tools","section":"diving-into-data","pubs":["9qsbf3hz"]},{"heading":"Mining the Past","note":"Brief Histories of Data Science","section":"mining-the-past","pubs":["0aytgrau"]},{"heading":"Bits and Bites","note":null,"section":"bits-and-bites","pubs":["v9fdn7n7"]},{"heading":"Also in This Issue","note":null,"section":null,"pubs":["4tx7h11w"]}],"count":28}],"masthead":[{"title":"Editorial Office","members":[{"name":"Rebecca McLeod","affiliation":"Managing Director for Harvard Data Science Review","bio":"Rebecca McLeod is the Managing Director of Harvard Data Science Review (HDSR). She oversees HDSR’s business, publishing, and program operations, including business development, production, editorial activities, and marketing.
Rebecca brings decades of experience in scholarly publishing, having held roles in marketing, editorial development, operations, and relationship management at nonprofit and commercial organizations. She spent a substantial part of her career at the MIT Press, including eight years as Journals Director, overseeing the publication, marketing, and sales of more than 35 scholarly journals. Before joining HDSR, she managed strategic publisher accounts at Copyright Clearance Center and Aries Systems.
Rebecca is actively engaged in the scholarly communications community. She currently serves as Past President on the Society for Scholarly Publishing’s (SSP) Board of Directors and as Convenor of the Coalition for Diversity and Inclusion in Scholarly Communications (C4DISC). She has chaired and served on numerous committees for SSP, the Association of University Presses, and the Professional and Scholarly Publishing Division of the Association of American Publishers. She is also a founding member of the Boston Women’s Information Network (BWIN) and a former member of the Board of Directors of the National Information Standards Organization (NISO).
Rebecca holds a BA in American Studies with Departmental Honors from Wesleyan University.
"},{"name":"Amara Deis","affiliation":"Editorial and Administrative Coordinator for Harvard Data Science Review","bio":"Amara Deis is the Editorial and Administrative Coordinator for Harvard Data Science Review (HDSR). In this role, she handles all aspects of HDSR’s daily operations and oversees the journal’s editorial and production processes from proposal submission through publication. Her responsibilities include managing submitted proposals and manuscripts, serving as a point of contact for authors, providing administrative support for the editorial teams, overseeing copyediting and proofreading, typesetting articles, coordinating production schedules, and curating and maintaining website content.
Amara brings experience in academic, interdisciplinary, and literary editing and publishing. Prior to joining HDSR, she served as an editor for Prized Writing, an academic journal that publishes STEM content for public audiences; Writing on the Edge, an interdisciplinary journal focused on higher education, composition, teaching, and writing instruction; and Open Ceilings Magazine, a print university-affiliated literary journal.
Amara holds a Bachelor of Arts in communication, with minors in professional writing and English, from the University of California, Davis.
"},{"name":"Xiao-Li Meng","affiliation":"Department of Statistics, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Xiao-Li Meng, the Founding Editor-in-Chief of Harvard Data Science Review and the Whipple V. N. Jones Professor of Statistics, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by Committee of Presidents of Statistical Societies (COPSS) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. In 2020, he was elected to the American Academy of Arts and Sciences. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files,\" a thought-provoking and entertaining column in the Institute of Mathematical Statistics (IMS) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; expectation–maximization (EM) algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard, where he served as the Chair of the Department of Statistics (2004–2012) and the Dean of Graduate School of Arts and Sciences (2012–2017).
"},{"name":"John Eltinge","affiliation":"Former Research and Methodology, United States Census Bureau, Suitland, Maryland, U.S.A.","bio":"John Eltinge served as the Assistant Director for Research and Methodology, and Scientific Integrity Official, at the United States Census Bureau until his retirement from the federal government on April 30, 2025. Before moving to the Census Bureau in 2016, he served as the Associate Commissioner for Survey Methods Research at the Bureau of Labor Statistics (BLS) for twelve years; and previously served as a senior mathematical statistician at BLS, and as an associate professor with tenure in the Department of Statistics at Texas A&M University. He earned a PhD from the Department of Statistics at Iowa State University; is a fellow of the American Statistical Association; is an elected member of the International Statistical Institute; and is an associate editor for Journal of Official Statistics and for Survey Methodology Journal. He previously received the Founders’ Award of the American Statistical Association; and gave the Roger Herriot Memorial Lecture on Innovation in the Federal Statistical System. In addition, he previously was the President of the Washington Statistical Society; the chair of the Federal Committee on Statistical Methodology; the overall chair of the 2003 Joint Statistical Meetings; an associate editor for The American Statistician; and an associate editor for the Applications and Case Studies Section of Journal of the American Statistical Association. His research interests include scientific integrity and statistical ethics; data quality; design optimization; integration of multiple data sources; imputation; time series; and small domain estimation. In addition, at the 2018 Joint Statistical Meetings, he presented the annual Deming Memorial Lecture, “Improving the Quality and Value of Statistical Information: 14 Questions on Management.\"
Webcast of Deming Memorial Lecture
"},{"name":"Nicholas Horton","affiliation":"Department of Mathematics and Statistics, Amherst College, Massachusetts, U.S.A.","bio":"Nicholas Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. His research involves the development and application of statistical methods with applications in psychiatric epidemiology and substance abuse research. Much of his work in recent years has focused on statistics and data science education. He is a fellow of the American Statistical Association (ASA) and the American Association for the Advancement of Science and recipient of the ASA's Founder’s Award for Distinguished Service. He has held numerous leadership positions including Chair of the Committee of Presidents of Statistical Societies and chair of the ASA Curriculum Guidelines for Undergraduate Programs in Statistical Science. Nick is co-chair of the National Academies Committee on Applied and Theoretical Statistics (CATS). He was a member of the Academies consensus report on \"Data Science for Undergraduates\" and the Guidelines for Assessment and Instruction in Statistics Education revised College report.
"},{"name":"Ralf Herbrich","affiliation":"Hasso-Plattner Institute, Potsdam, Germany","bio":"Ralf Herbrich leads the group on Artificial Intelligence and Sustainability at the Hasso-Plattner Institute in Potsdam since May 2022 where he is also one of the co-managing directors. Previously, he served as Senior Vice President, Builder Platform & Artificial Intelligence at Zalando (2020–2022), Managing Director of the Amazon Development Center in Germany with its locations in Berlin, Dresden, Aachen and Tübingen and was Director of Machine Learning at Amazon in Berlin from (2013–2020). Prior to these roles at Amazon, he led Facebook’s Unified Ranking and Allocation team in 2011. From 2000 to 2011, he served as Director of Microsoft's Future Social Experiences (FUSE) Lab UK and worked for nine years at Microsoft Research Lab in Cambridge, UK. Since 2024, Ralf is also a member of the supervisory board of SAP.
Ralf has published over 100 peer-reviewed papers and holds over 50 patents. He holds a PhD in Statistics from Technical University of Berlin and obtained his Diploma in Computer Science from the same institution. His research interests include Bayesian inference and decision making, natural language processing, computer vision, learning theory, robotics, distributed systems and programming languages. Ralf is one of the inventors of the Drivatars™ system in the Forza Motorsport series as well as the TrueSkill™ ranking and matchmaking system in Xbox Live.
"},{"name":"Frauke Kreuter","affiliation":"Department of Statistics, Ludwig Maximilians University of Munich, Munich, Germany; Social Data Science Center and Joint Program in Survey Methodology, University of Maryland, College Park, Maryland, U.S.A.; Mannheim Center for Data Science, Mannheim University, Mannheim, Germany","bio":"Frauke Kreuter is professor of Statistics and Data Science for the Social Sciences and Humanities at the Ludwig Maximilians University of Munich (Germany) and professor at the Joint Program in Survey Methodology at the University of Maryland. Frauke Kreuter co-founded the Data Science Centers at the University of Maryland (USA) and Mannheim (Germany). She is an elected fellow of the American Statistical Association and the 2020 and 2022 recipient of the Warren Mitofsky Innovators Award of the American Association for Public Opinion Research. In addition to her academic work Dr. Kreuter is the Founder of the International Program for Survey and Data Science, developed in response to the increasing demand from researchers and practitioners for the appropriate methods and right tools to face a changing data environment; Co-Founder of the Coleridge Initiative, whose goal is to accelerate data-driven research and policy around human beings and their interactions for program management, policy development, and scholarly purposes by enabling efficient, effective, and secure access to sensitive data about society and the economy; and Co-Founder of the German language podcast Dig Deep.
"},{"name":"Bin Yu","affiliation":"Department of Statistics, Department of Electrical Engineering and Computer Sciences, and Center for Computational Biology, University of California Berkeley, Berkeley, California, U.S.A.","bio":"Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in Statistics, EECS, and Computational Biology at UC Berkeley. Her recent research focuses on statistical machine learning practice, algorithm, and theory, veridical data science for trustworthy AI, and interdisciplinary data problems in neuroscience, genomics, and precision medicine. She is a member of the U. S. National Academy of Sciences and American Academy of Arts and Sciences. She was a Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, and Rietz Lecturer of the Institute of Mathematical Statistics (IMS) , and won the E. L. Scott Award given by the Committee of Presidents of Statistical Societies (COPSS). She delivered the IMS Wald Lectures and the COPSS Distinguished Achievement Award and Lecture (DAAL) (formerly Fisher) at the Joint Statistical Meetings (JSM) in August, 2023. She holds an Honorary Doctorate from The University of Lausanne. She served on the inaugural scientific advisory board of the UK Turing Institute of Data Science and AI, and is serving on the editorial board of PNAS and as a senior advisor at the Simons Institute for the Theory of Computing at UC Berkeley.
"},{"name":"Dacheng Xiu","affiliation":"Econometrics & Statistics Group, Booth School of Business, University of Chicago, Chicago, Illinois, U.S.A.","bio":"Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He has served as Co-Editor for the Journal of Financial Econometrics and has been on the editorial board as an Associate Editor for many prestigious journals, including the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, and Management Science. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. He has been recognized as one of Poets & Quants’ Best 40-under-40 Business School Professors of 2023. At Booth, he teaches a variety of courses related to FinTech, Big Data, and Statistical Inference to MBA, college, and PhD students. Xiu earned his PhD and MA in applied mathematics from Princeton University.
"}]},{"title":"Associate Editors","members":[{"name":"John Eltinge","affiliation":"Former Research and Methodology, United States Census Bureau, Suitland, Maryland, U.S.A.","bio":"John Eltinge served as the Assistant Director for Research and Methodology, and Scientific Integrity Official, at the United States Census Bureau until his retirement from the federal government on April 30, 2025. Before moving to the Census Bureau in 2016, he served as the Associate Commissioner for Survey Methods Research at the Bureau of Labor Statistics (BLS) for twelve years; and previously served as a senior mathematical statistician at BLS, and as an associate professor with tenure in the Department of Statistics at Texas A&M University. He earned a PhD from the Department of Statistics at Iowa State University; is a fellow of the American Statistical Association; is an elected member of the International Statistical Institute; and is an associate editor for Journal of Official Statistics and for Survey Methodology Journal. He previously received the Founders’ Award of the American Statistical Association; and gave the Roger Herriot Memorial Lecture on Innovation in the Federal Statistical System. In addition, he previously was the President of the Washington Statistical Society; the chair of the Federal Committee on Statistical Methodology; the overall chair of the 2003 Joint Statistical Meetings; an associate editor for The American Statistician; and an associate editor for the Applications and Case Studies Section of Journal of the American Statistical Association. His research interests include scientific integrity and statistical ethics; data quality; design optimization; integration of multiple data sources; imputation; time series; and small domain estimation. In addition, at the 2018 Joint Statistical Meetings, he presented the annual Deming Memorial Lecture, “Improving the Quality and Value of Statistical Information: 14 Questions on Management.\"
Webcast of Deming Memorial Lecture
"},{"name":"Ralf Herbrich","affiliation":"Hasso-Plattner Institute, Potsdam, Germany","bio":"Ralf Herbrich leads the group on Artificial Intelligence and Sustainability at the Hasso-Plattner Institute in Potsdam since May 2022 where he is also one of the co-managing directors. Previously, he served as Senior Vice President, Builder Platform & Artificial Intelligence at Zalando (2020–2022), Managing Director of the Amazon Development Center in Germany with its locations in Berlin, Dresden, Aachen and Tübingen and was Director of Machine Learning at Amazon in Berlin from (2013–2020). Prior to these roles at Amazon, he led Facebook’s Unified Ranking and Allocation team in 2011. From 2000 to 2011, he served as Director of Microsoft's Future Social Experiences (FUSE) Lab UK and worked for nine years at Microsoft Research Lab in Cambridge, UK. Since 2024, Ralf is also a member of the supervisory board of SAP.
Ralf has published over 100 peer-reviewed papers and holds over 50 patents. He holds a PhD in Statistics from Technical University of Berlin and obtained his Diploma in Computer Science from the same institution. His research interests include Bayesian inference and decision making, natural language processing, computer vision, learning theory, robotics, distributed systems and programming languages. Ralf is one of the inventors of the Drivatars™ system in the Forza Motorsport series as well as the TrueSkill™ ranking and matchmaking system in Xbox Live.
"},{"name":"Nicholas Horton","affiliation":"Department of Mathematics and Statistics, Amherst College, Massachusetts, U.S.A.","bio":"Nicholas Horton is Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. His research involves the development and application of statistical methods with applications in psychiatric epidemiology and substance abuse research. Much of his work in recent years has focused on statistics and data science education. He is a fellow of the American Statistical Association (ASA) and the American Association for the Advancement of Science and recipient of the ASA's Founder’s Award for Distinguished Service. He has held numerous leadership positions including Chair of the Committee of Presidents of Statistical Societies and chair of the ASA Curriculum Guidelines for Undergraduate Programs in Statistical Science. Nick is co-chair of the National Academies Committee on Applied and Theoretical Statistics (CATS). He was a member of the Academies consensus report on \"Data Science for Undergraduates\" and the Guidelines for Assessment and Instruction in Statistics Education revised College report.
"},{"name":"Lars Vilhuber","affiliation":"Labor Dynamics Institute, School of Industrial and Labor Relations, and Department of Economics, Cornell University, Ithaca, New York, U.S.A.; American Economic Association","bio":"Lars Vilhuber holds a PhD in Economics (Université de Montréal). His research interests lie in labor economics, statistics with linked and large datasets, and data publication. His work on statistical disclosure limitation issues comes from his profound interest in making data available in a multitude of formats to the broadest possible audience. His experience includes academic and government research positions, and he continues to consult and collaborate with government and statistical agencies in Canada, the United States, and Europe. At Cornell, he is Research Professor in the Industrial and Labor Relations (ILR) School, Executive Director of the Labor Dynamics Institute, and a member of the Economics Faculty. He is Data Editor of the American Economic Association; Managing Editor of the Journal of Privacy and Confidentiality; Chair of the Scientific Advisory Committee of the Centre d’accès sécurisé aux données; Senior Advisor of the New York Federal Statistical Research Data Centers; and an affiliate of the U.S. Census Bureau (Center for Economic Studies).
L. Vilhuber and C. Lagoze, “Making Confidential Data Part of Reproducible Research,” Chance, 2017.
"},{"name":"David Uminsky","affiliation":"Data Science Institute and Department of Computer Science, University of Chicago, Chicago, Illinois, U.S.A.","bio":"David Uminsky joined the University of Chicago in September 2020 as a senior research associate and Executive Director of Data Science. He was previously an associate professor of Mathematics and Executive Director of the Data Institute at University of San Francisco (USF). His research interests are in machine learning, signal processing, pattern formation, and dynamical systems. He was selected in 2015 by the National Academy of Sciences as a Kavli Frontiers of Science Fellow. He is also the founding Director of the BS in Data Science at USF and served as Director of the MS in Data Science program from 2014–2019. During the summer of 2018, David served as the Director of Research for the Mathematical Science Research Institute Undergrad Program on the topic of Mathematical Data Science.
Before joining USF, David was a combined NSF and UC President's Fellow at University of California, Los Angeles, where he was awarded the Chancellor's Award for outstanding postdoctoral research. He holds a PhD in Mathematics from Boston University and a BS in Mathematics from Harvey Mudd College.
"},{"name":"Vonu Thakuriah","affiliation":"Edward J. Bloustein School of Planning and Public Policy and Rutgers Urban and Civic Informatics Lab, Rutgers University, New Brunswick, New Jersey, U.S.A.","bio":"Piyushimita “Vonu” Thakuriah is a Distinguished Professor in Rutgers University, New Brunswick, New Jersey. Her primary research interests are in the data science and policy aspects of transportation, urban informatics, smart cities, and social and economic cyberinfrastructure. Her interdisciplinary methodological work and engagement with government agencies and private industry aim to reduce social and economic disparities while also increasing sustainability, data justice, and data science for the public good. Her research has been funded by the National Science Foundation, UK Research and Innovation, European Commission, US Department of Transportation, and number of other national, state, and local organizations internationally. She has given keynotes and plenaries in leading data science and transportation venues globally. She is currently a member of the National Academy of Sciences Committee on Applied and Theoretical Statistics, and previously served as a member of the Advisory Board of the UK Office of National Statistics Data Science Campus, as well as the European Commission/Eurostat’s New Techniques and Technologies for Statistics Committee. She recently chaired the Canada Foundation for Innovation’s International Expert Committee on smart cities. She was previously the Dean of the Rutgers University’s Bloustein School of Planning and Public Policy, after serving as the Ch2M Endowed Chair Professor of Transport in the University of Glasgow, UK, and as the founding director of the Urban Big Data Centre, a multi-institutional UK-wide data infrastructure for cities research and innovation funded by the UK Research and Innovation. She graduated from the University of Illinois at Chicago with a PhD in Public Policy Analysis and a master’s degree in Urban Planning and Policy. She was a European Commission Marie Curie Fellow and her postdoctoral fellowship was supported by NSF’s Division of Mathematical Sciences at the National Institute of Statistical Sciences.
"},{"name":"Mahlet Tadesse","affiliation":"Department of Mathematics and Statistics, Georgetown University, Washington D.C., U.S.A.","bio":"Mahlet Tadesse is Professor and Chair of the Department of Mathematics and Statistics at Georgetown University. She received her doctorate in Biostatistics from Harvard University and served on the faculty at the University of Pennsylvania as Assistant Professor of Biostatistics, prior to joining Georgetown University. Her research focuses on the development of statistical and computational tools for the analysis of large-scale genomic data. She is particularly interested in stochastic search methods and Bayesian inferential strategies to identify structures and relationships in high-dimensional data sets. Mahlet is an elected member of the International Statistical Institute and an elected fellow of the American Statistical Association.
"},{"name":"Elizabeth A. Stuart","affiliation":"Department of Biostatistics, Department of Health Policy and Management, and Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, U.S.A.","bio":"Elizabeth A. Stuart, PhD is the Frank Hurley and Catharine Dorrier Chair of the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health as well as Bloomberg Professor of American Health, with joint appointments in the Departments of Mental Health and Health Policy and Management. She received her PhD in statistics in 2004 from Harvard University, is a member of the National Academy of Medicine, and is a Fellow of the American Statistical Association (ASA) and the American Association for the Advancement of Science (AAAS). Dr. Stuart has extensive experience in methods for estimating causal effects and dealing with the complications of missing data in experimental and non-experimental studies, particularly as applied to mental health, public policy, and education. She has received research funding for her work from the National Institutes of Health, Patient-Centered Outcomes Research Institute (PCORI), the National Science Foundation, the Institute of Education Sciences, and the WT Grant Foundation. She received the mid-career award from the Health Policy Statistics Section of the ASA, the Gertrude Cox Award for applied statistics, Harvard University’s Myrto Lefkopoulou Award for excellence in Biostatistics, and the Society for Epidemiologic Research Marshall Joffe Epidemiologic Methods award.
"},{"name":"Victoria Stodden","affiliation":"Department of Computer Science, Department of Statistics, Center for Informatics Research in Science and Scholarship, and School of Law, University of Illinois at Urbana-Champaign, Champaign, Illinois, U.S.A.","bio":"Victoria Stodden is an associate professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign, with affiliate appointments in the School of Law, the Department of Computer Science, the Department of Statistics, the Coordinated Science Laboratory, and the National Center for Supercomputing Applications. She is also a faculty affiliate of the Center for Informatics Research in Science and Scholarship (CIRSS) in the School of Information Sciences at the University of Illinois. She completed both her PhD in statistics and her law degree at Stanford University, and graduated magna cum laude from the University of Ottawa.
"},{"name":"Eugene Soltes","affiliation":"Accounting and Management Unit, Harvard Business School, Harvard University, Boston, Massachusetts, U.S.A.","bio":"Eugene Soltes is the McLean Family Professor of Business Administration at Harvard Business School where his work focuses on corporate integrity and risk management. His research utilizes data analytics to identify organizational cultures and compliance systems that can effectively prevent, detect, and respond to reputational and regulatory threats. Professor Soltes teaches graduate-level courses on regulatory environments and risk management, and was the recipient of the Charles M. Williams Award for outstanding teaching.
Professor Soltes is the author of the bestselling book Why They Do It: Inside the Mind of the White-Collar Criminal, which was described by Kirkus Reviews as “a groundbreaking study” on white-collar criminality. Based on years of research and hundreds of hours of interviews, the book refutes popular explanations of why seemingly successful managers engage in misconduct by showing that many make decisions on the basis of intuitions and gut feelings. The trouble, Professor Soltes shows, is that these gut feelings are often poorly suited for the modern business world.
Professor Soltes is a leader in utilizing data analytics to predict, detect, and mitigate issues related to organizational misconduct. He frequently serves as an advisor and consultant to multinational corporations regarding the design of their compliance programs, and is routinely invited to speak to regulators, including the Department of Justice, Securities and Exchange Commission, and the United States Treasury. Drawing on his experience with companies and government agencies, Professor Soltes founded Integrity Lab, which creates integrated, data-driven technology to cultivate organizational integrity and strengthen corporate culture.
Professor Soltes’ contributions have been widely cited by the media including The Wall Street Journal, Financial Times, The New York Times, NPR, and The Economist. Prior to joining the faculty of Harvard Business School, Professor Soltes received his PhD and MBA from the University of Chicago Booth School of Business, and his AM in statistics and AB in economics from Harvard University.
"},{"name":"David Smith","affiliation":"NULab for Texts, Maps and Networks, Northeastern University, Boston, Massachusetts, U.S.A.","bio":"David Smith is a founding member of the NULab for Texts, Maps and Networks, Northeastern's research center for the digital humanities and computational social sciences. He holds an AB in classics (Greek) from Harvard and a PhD in computer science from Johns Hopkins. In between, he worked as a research programmer at the Perseus Project, a widely-used digital library of historical texts, linguistic information, art, and archaeology. Smith's research interests include statistical models of natural language morphology, syntax, semantics, and translation with applications to information retrieval, network analysis, historical linguistics, and the humanities and social sciences. Smith and NULab colleague Ryan Cordell, an associate professor of English, started the Viral Texts project to analyze cultural influence and information circulation in historical print media. With former students Abby Mullen and Jonathan Fitzgerald, they are finishing a book titled Going the Rounds: Virality in Nineteenth-Century Newspapers.
"},{"name":"Nathan Sanders","affiliation":"Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Nathan Sanders is a data scientist focused on creating open technology to help vulnerable communities and all stakeholders participate in the analysis and development of public policy.
Nathan's new book is Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), co-authored with Bruce Schneier. As a Berkman Klein Center Fellow in 2020-2021, Nathan helped create the Harvard Climate Justice Design Fellowship program and the Massachusetts Platform for Legislative Engagement (MAPLE). Nathan has helped build and lead data science teams in industry at Legendary Pictures, WarnerMedia, Flagship Pioneering, and Harvard Business Publishing developing and applying methods in Bayesian inference, natural language processing, computer vision, and deep learning.
In the policy domain, he has built open source applications for participatory oversight of environmental regulation in collaboration; helped public health agencies navigate nationwide public comment about controversial proposed legislation; developed statistical methods for public health analysis modeling long term trends in the rate of mass public shootings; and served as a science policy fellow in the Massachusetts Senate and House of Representative. Nathan is a co-founder of the astrophysical literature digest Astrobites, the multi-lingual association of graduate student science writing collaboratives ScienceBites, and the international science communication workshop series ComSciCon. Nathan did his undergraduate work in Physics and Astrophysics at Michigan State University and earned his PhD in Astronomy and Astrophysics from Harvard University.
"},{"name":"Ruslan Salakhutdinov","affiliation":"Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, U.S.A.","bio":"Ruslan Salakhutdinov is a UPMC Professor of Computer Science in the Department of Machine Learning at Carnegie Mellon University (CMU). He received his PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Departments of Statistics and Computer Science. In 2016 he joined CMU. Ruslan's primary interests lie in deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several top-tier learning conferences including Conference on Neural Information Processing Systems (NIPS) and International Conference on Machine Learning (ICML). He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Google Faculty Award, Nvidia's Pioneers of AI award, and is a Senior Fellow of the Canadian Institute for Advanced Research.
"},{"name":"Cynthia Rudin","affiliation":"Department of Computer Science and Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, U.S.A.","bio":"Cynthia Rudin is Earl D. McLean, Jr. Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, Biostatistics & Bioinformatics at Duke University. She directs the Interpretable Machine Learning Lab, whose goal is to design predictive models that people can understand. Her lab applies machine learning in many areas, such as healthcare, criminal justice, and energy reliability.
Prof. Rudin holds an undergraduate degree from the University at Buffalo and a PhD from Princeton University. She is the recipient of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence. She is also a three-time winner of the INFORMS Innovative Applications in Analytics Award, and a 2022 Guggenheim Fellow. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Association for the Advancement of Artificial Intelligence. She is a member of the National Academy of Sciences, Engineering, and Medicine committee on Facial Recognition Technology and a member of the US National AI Advisory Committee Subcommittee on AI and Law Enforcement (NAIAC-LE). Her work and opinions have been featured in news outlets including the NY Times, Washington Post, Wall Street Journal, the Boston Globe, Businessweek, NPR, The Hill, CNN, and the Raleigh News&Observer.
"},{"name":"Nancy Reid","affiliation":"Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada","bio":"Nancy Reid is University Professor and Canada Research Chair in Statistical Theory and Applications at the University of Toronto. Her research interests include statistical theory, likelihood inference, design of studies, and statistical science in public policy. She has held many professional leadership roles in statistical science, in Canada and abroad. Her main research contributions have been to the field of theoretical statistics. The goal is to use information from noisy data as efficiently and elegantly as possible, and to elucidate general principles for doing so, in order to provide structures for developing new statistical methods in new areas of application. Professor Reid is a Fellow of the Royal Society, the Royal Society of Canada, the American Association for the Advancement of Science, and a Foreign Associate of the National Academy of Sciences. In 2015 she was appointed Officer of the Order of Canada.
\"Statistical Inference, Learning and Models in Big Data.”
\"'Statistical Science and Data Science': Talk Presented at Fisher Memorial Lecture, October 27, 2016\"
Daniel T. O’Brien is Associate Professor in the School of Public Policy and Urban Affairs and the School of Criminology and Criminal Justice at Northeastern University. His primary expertise is in the use of modern digital data sets to better understand urban processes, particularly the social and behavioral dynamics of neighborhoods. He is co-Director of the Boston Area Research Initiative (BARI), in which capacity he has worked extensively to build effective models of research-policy collaboration that help us to better understand and serve cities. His book The Urban Commons (2018; Harvard University Press) captures the intersection of his scholarly and institutional efforts, using the study of custodianship for neighborhood spaces and infrastructure through Boston’s 311 system to illustrate the potential of cross-sector collaborations in urban informatics.
"},{"name":"David Madigan","affiliation":"University of Southern California, Los Angeles, California, U.S.A.","bio":"David Madigan is the Provost and Executive Vice President for Academic Affairs at the University of Southern California. He is also the Shelly and Ofer Nemirovsky Provost’s Chair and Professor of Statistics and Epidemiology. Prior to joining USC, he was Provost at Northeastern University. He received a bachelor’s degree in Mathematics and a PhD in Statistics, both from Trinity College Dublin. He has previously worked for AT&T Inc., Soliloquy Inc., the University of Washington, Rutgers University, and SkillSoft, Inc. He has over 200 publications in such areas as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He has served terms as Editor-in-Chief of Statistical Science and of Statistical Analysis and Data Mining, the American Statistical Association (ASA) Data Science Journal.
"},{"name":"David Leslie","affiliation":"Ethics and Responsible Innovation Research Unit, Alan Turing Institute, London, England, U.K.","bio":"David Leslie is the Director of Ethics and Responsible Innovation Research at The Alan Turing Institute and Professor of Ethics, Technology and Society at Queen Mary University of London. He previously taught at Princeton’s University Center for Human Values, Yale’s programme in Ethics, Politics and Economics and at Harvard’s Committee on Degrees in Social Studies, where he received over a dozen teaching awards including the 2014 Stanley Hoffman Prize for Teaching Excellence. David is the author of the UK Government’s official guidance on the responsible design and implementation of AI systems in the public sector, Understanding artificial intelligence ethics and safety (2019) and a principal co-author of Explaining decisions made with AI (2020), a co-badged guidance on AI explainability published by the UK’s Information Commissioner’s Office and The Alan Turing Institute. After serving as an elected member of the Bureau of the Council of Europe’s (CoE) Ad Hoc Committee on Artificial Intelligence (CAHAI) (2021-2022), he was appointed, in 2022, as Specialist Advisor to the CoE’s Committee on AI where he has led the writing of the zero draft of its Human Rights, Democracy and the Rule of Law Impact Assessment for AI, which will accompany its forthcoming AI Convention. He also serves on UNESCO’s High-Level Expert Group steering the implementation of its Recommendation on the Ethics of Artificial Intelligence. David recent publications include ‘The Ethics of Computational Social Science’, (2023) written for the European Commission Joint Research Centre/Centre for Advanced Studies, ‘Artificial intelligence and the heritage of democratic equity’ (2022) published by The Venice Commission of the Council of Europe, the HDSR articles “Tackling COVID-19 through responsible AI innovation: Five steps in the right direction” (2020) and “The arc of the data scientific universe” (2021) as well as Understanding bias in facial recognition technologies (2020), an explainer published to support a BBC investigative journalism piece that won the 2021 Royal Statistical Society Award for Excellence in Investigative Journalism.
"},{"name":"Sabina Leonelli","affiliation":"Philosophy and History of Science and Technology, Technical University of Munich, Munich, Germany","bio":"Sabina Leonelli holds the Chair of Philosophy and History of Science and Technology at the Technical University of Munich, where she is also Research Director of the Ethical Data Initiative (https://ethicaldatainitiative.org/), Co-Director of the Public Science Lab (https://publicsciencelab.wordpress.com/ ), PI of the Munich Centre for Transformative Technologies and Societal Change (https://transform-cluster.de/) and lead of the ERC Project PHIL_OS (www.opensciencestudies.eu). Until 2024 she was the Director of the Exeter Centre for the Study of the Life Sciences at the University of Exeter, where she holds a Honorary Professorship. She is the president of the International Society for the History, Philosophy and Social Studies of Biology (ISHPSSB) and an elected Fellow of the Academia Europaea, the American Association for the Advancement of Science (AAAS), and the Académie Internationale de Philosophie de la Science. Her research, which includes philosophical, historical and social science methods, concerns: the role of technology and data in knowledge production, and especially how computing and digitalisation efforts are transforming biological research and its social dynamics and roles; and the institutionalisation of Open Science as a window on the methods, epistemology and political economy of contemporary forms of scientific inquiry, particularly in the life and environmental sciences. Her work aims to foster both understanding of these processes and strategies for intervention, including policy-facing advice, advisory roles in scientific projects and educational initiatives to support low-resourced environments and enhance responsible research practices globally.
"},{"name":"Claire Le Goues","affiliation":"Department of Computer Science and Institute for Software Research, Carnegie Mellon University, Pittsburgh, Pennsylvania, U.S.A.","bio":"Claire Le Goues is an associate professor of computer science at Carnegie Mellon University, where her home department is the Institute for Software Research. Her research interests span software engineering and applied program analysis, with particular focus on automatic debugging and quality assurance of messy, real-world software systems. She has received a National Science Foundation (NSF) CAREER Award as well as the Association for Computing Machinery’s Special Interest Group on Software Engineering (ACM SIGSOFT) Early Career Researcher award, and her work on automatic program repair has been recognized by impact awards from both ACM Special Interest Group for Genetic and Evolutionary Computation (SIGEVO) and the International Conference on Software Engineering. Dr. Le Goues holds an undergraduate degree from Harvard College and Masters and PhD degrees from the University of Virginia, all in Computer Science.
"},{"name":"Nicole Lazar","affiliation":"Department of Statistics, Eberly College of Science, Pennsylvania State University, Centre County, Pennsylvania, U.S.A.","bio":"Nicole Lazar is Professor of Statistics at Penn State University. She received her undergraduate training in statistics and psychology at Tel Aviv University, and graduate training in statistics at Stanford University and the University of Chicago. She was on the faculty of Carnegie Mellon University from 1996 until 2004, when she moved to the University of Georgia until 2020. Dr. Lazar has served on the editorial boards of leading journals, including serving at Editor-in-Chief of The American Statistician from 2014 to 2017. In 2019, she served as President of the Caucus for Women in Statistics. She is an Elected Member of the International Statistical Institute, a Fellow of the American Statistical Association, and a Fellow of the Institute of Mathematical Statistics.
"},{"name":"Sallie Ann Keller","affiliation":"Research and Methodology, United States Census Bureau, Suitland, Maryland, U.S.A.","bio":"Dr. Sallie Ann Keller is chief scientist and associate director of the U.S. Census Bureau’s Research and Methodology Directorate. She also holds an endowed distinguished professorship in biocomplexity and faculty appointments in the School of Medicine, Department of Public Health Services; School of Engineering and Applied Science, Department of Engineering Systems and Environment; and School of Data Science at the University of Virginia (UVA).
As chief scientist, Keller will lead the Research and Methodology Directorate’s research centers, each devoted to domains of investigation important to the future of social and economic statistics. The directorate collaborates with teams across the U.S. Census Bureau and with researchers around the country and the world to develop innovative scientific solutions and advances to ensure the Census Bureau remains a leader in economic and social measurement.
Keller is a nationally recognized research scientist whose areas of expertise are social and decision informatics, statistical underpinnings of data science, and data access and confidentiality. She is a leading voice in creating the science of all data and advancing this research across disciplines to benefit society.
Her prior positions include director of the Social and Decision Analytics Division within UVA’s Biocomplexity Institute and Initiative; professor of statistics and director of the Social and Decision Analytics Laboratory within the Biocomplexity Institute of Virginia Tech; academic vice president and provost at University of Waterloo; director of the Institute for Defense Analyses Science and Technology Policy Institute; the William and Stephanie Sick Dean of Engineering at Rice University; head of the Statistical Sciences group at Los Alamos National Laboratory; professor of statistics at Kansas State University; and Statistics Program director at the National Science Foundation.
Keller is an elected member of the U.S. National Academy of Engineering. She has served as a member of the National Academy of Sciences Board on Mathematical Sciences and Their Applications and the Committee on National Statistics, and as chair of the Committee on Applied and Theoretical Statistics. She is a fellow of the American Association for the Advancement of Science, an elected member of the International Statistics Institute, and a fellow and past president of the American Statistical Association. Keller earned her B.S. and M.S. in mathematics from the University of South Florida and her Ph.D. in statistics from Iowa State University.
"},{"name":"Booil Jo","affiliation":"Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, U.S.A.","bio":"Booil Jo, PhD, is Associate Professor of Biostatistics at the Department of Psychiatry and Behavioral Sciences at Stanford University School of Medicine. She has been at the lead in developing pragmatic statistical methods based on the intersection of causal inference and latent variable modeling. She has published on various methodological topics such as treatment noncompliance, handling of nested data such as from cluster randomized trials, causal mediation, missing data, propensity scores, and longitudinal heterogeneity. Her current program of research is focused on developing statistical methods that jointly utilize latent variable modeling, causal inference, and statistical learning with the goal of advancing the field of personalized medicine. She is also actively involved in biostatics education and collaborative work in various fields of psychiatry/mental health research. She has been a leading member of American Statistical Association and Prevention Science Methodology group.
"},{"name":"Rafael Irizarry","affiliation":"Department of Data Science, Dana-Farber Cancer Institute, Harvard T.H Chan School of Public Health, Harvard University, Boston, Massachusetts, U.S.A.; Department of Biostatistics, Harvard T.H Chan School of Public Health, Harvard University, Boston, Massachusetts, U.S.A.","bio":"Rafael Irizarry received his Bachelor’s in Mathematics in 1993 from the University of Puerto Rico and his PhD in Statistics in 1998 from the University of California, Berkeley. His thesis work was on Statistical Models for Music Sound Signals. He joined the faculty of the Department of Biostatistics in the Johns Hopkins Bloomberg School of Public Health in 1998 and was promoted to Professor in 2007. He is now the Chair of the Department of Data Science at the Dana-Farber Cancer Institute and a Professor of Biostatistics at Harvard T.H Chan School of Public Health. Since 1999, Rafael Irizarry’s work has focused on Genomics and Computational Biology applications. Professor Irizarry also develops open source software implementing his statistical methodology. His software tools are widely used and he is one of the leaders and founders of the Bioconductor Project, an open source and open development software project that provides one of the most widely used software tools for the analysis of genomic data.
"},{"name":"Eyke Hüllermeier","affiliation":"Institute of Informatics, University of Munich (LMU), Munich, Germany; Fraunhofer Institute for Mechatronic Systems Design, Paderborn, Germany","bio":"Eyke Hüllermeier is a full professor at the Institute of Informatics at Ludwig Maximilian University of Munich (LMU Munich), Germany, where he heads the Chair of Artificial Intelligence and Machine Learning, and a Chief Scientist at the Fraunhofer Institute for Mechatronic Systems Design. He studied mathematics and business computing, received his PhD in computer science from Paderborn University in 1997, and a Habilitation degree in 2002. Prior to joining LMU, he held professorships at several other German universities and spent two years as a Marie Curie fellow at the Toulouse Institute for Research in Computer Science (IRIT) in Toulouse (France). He has also been a board member of the Heinz Nixdorf Institute and a Director at the Software Innovation Campus Paderborn, Germany. His research interests are centered around methods and theoretical foundations of artificial intelligence, with a specific focus on machine learning and reasoning under uncertainty. Several of his scientific contributions have been recognized with awards; most recently, he was recipient of the L.A. Zadeh Prize 2019 awarded by the The International Fuzzy Systems Association (IFSA) Society for active scientific leadership and excellence in research.
"},{"name":"Sebastien Haneuse","affiliation":"Department of Statistics, University of California Irvine, Irvine, California, U.S.A.","bio":"Sebastien Haneuse is Professor and Chair in the Department of Statistics at the University of California - Irvine. He received his PhD in Biostatistics in 2004 from the University of Washington, and has held positions at Vanderbilt University, the Group Health Research Institute (now Kaiser Permanente Washington Health Research Institute) and Harvard University. His current methodologic research interests cover: handling missing data in electronic health records-based research; outcome-dependent sampling schemes in limited resource settings; and, design and analysis of studies of semi-competing risks. Substantively, Sebastien Haneuse currently collaborates in studies of: maternal health, pregnancy and perinatal outcomes, childhood obesity prevention, the role of fathers in early childhood development, and quality of end-of-life care.
"},{"name":"Ruobin Gong","affiliation":"Department of Statistics, Rutgers University, New Brunswick, New Jersey, U.S.A.","bio":"Ruobin Gong is assistant professor of statistics at Rutgers University. Her research interests lie at the foundations of uncertainty reasoning, theory and methods of statistical inference with random sets, imprecise probability, Dempster-Shafer theory of belief function, and applications in robust and privacy statistics. Ruobin obtained her PhD in statistics from Harvard University (2018) and BSc in cognitive psychology from University of Toronto. She was a Harvard Horizons Scholar (2017) and research fellow for Data Science for Social Good at the University of Chicago (2015).
"},{"name":"Chiara Farronato","affiliation":"Technology and Operations Management Unit and Platform Lab at Harvard AI Institute, Harvard Business School Harvard Business School, Cambridge, Massachusetts, U.S.A.","bio":"Chiara Farronato is Glenn and Mary Jane Creamer Associate Professor of Business Administration in the Technology and Operations Management Unit at Harvard Business School, and co-Principal Investigator of the Platform Lab at the Harvard AI Institute. Chiara is also an affiliate at the National Bureau of Economic Research (NBER) and the Center for Economic and Policy Research (CEPR).
"},{"name":"Ashkan Ertefaie","affiliation":"Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, U.S.A.","bio":"Ashkan Ertefaie is an Associate Professor in the Department of Biostatistics and Computational Biology at the University of Rochester Medical Center. His methodological research interest lies in personalized medicine, sequential multiple assignment randomized trials (SMARTs), causal inference, comparative effectiveness studies using randomized trials and electronic health records, instrumental variable analyses, high-dimensional data analysis, post selection inference, and survival analysis.
"},{"name":"Walter Dempsey","affiliation":"Institute of Social Research, University of Michigan, Ann Arbor, Michigan, U.S.A.","bio":"Walter Dempsey is an Associate Professor of Biostatistics and an Associate Research Professor in the Institute of Social Research at the University of Michigan. He completed his PhD in Statistics at the University of Chicago under the supervision of Peter McCullagh and was a postdoctoral fellow in the Department of Statistics at Harvard University working with Susan Murphy. His research focuses on Statistical Methods for Digital and Mobile Health. His current work involves three complementary research themes: (1) experimental design and data analytic methods to inform multi-stage decision making in health; (2) statistical modeling of complex longitudinal and survival data; and (3) statistical modeling of complex relational structures such as interaction networks.
"},{"name":"Gregory Crane","affiliation":"Department of Classical Studies and Department of Computer Science, Tufts University, Medford, Massachusetts, U.S.A.","bio":"Gregory Crane received his PhD in Classical Philology at Harvard but has spent much of his career exploring the implications of the digital turn for the Humanities in general and for ancient languages in particular. From 2013–2018, he was Alexander von Humboldt Professor of Digital Humanities at the University of Leipzig. He is currently Professor of Classics at Tufts University, with a secondary appointment in Computer Science.
"},{"name":"Radu V. Craiu","affiliation":"Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada","bio":"Radu V. Craiu is a Professor of Statistical Sciences at the University of Toronto where he served as Chair of the Department for 5 years. He studied Mathematics at the University of Bucharest (BS 1995, MS 1996) and received a PhD from the Department of Statistics at The University of Chicago in 2001. His research interests span computational methods in statistics, especially, Markov chain Monte Carlo algorithms (MCMC), Bayesian inference, copula models, model selection procedures, and statistical genetics. He is currently Contributing Editor for the IMS Bulletin and Associate Editor for the Journal of the American Statistical Association – Theory and Methods, Harvard Data Science Review, Journal of Computational and Graphical Statistics, Statistics Surveys, The Canadian Journal of Statistics, and Statistical Methods and Applications. He received the 2016 CRM-SSC prize, is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, a Faculty Affiliate of the Vector Institute, and an Elected Member of the International Statistical Institute.
"},{"name":"Mine Çetinkaya-Rundel","affiliation":"Department of Statistical Science, Duke University, Durham, North Carolina, U.S.A.; Posit, Boston, Massachusetts, U.S.A.","bio":"Mine Çetinkaya-Rundel is Professor of the Practice and the Director of Undergraduate Studies at the Department of Statistical Science and an affiliated faculty in the Computational Media, Arts, and Cultures program at Duke University and Developer Educator at Posit. Mine is Professor of the Practice at the Department of Statistical Science at Duke University and Developer Educator at Posit, PBC. Mine's work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education. Mine has authored introductory statistics textbooks as part of the OpenIntro project, she is the creator and maintainer of Data Science in a Box, and she teaches the popular Statistics with R specialization on Coursera. Mine is the winner of the 2021 Hogg Award for Excellence in Teaching Introductory Statistics, the 2018 Harvard Pickard Award, and the 2016 ASA Waller Education Award. Learn more on her website at https://mine-cr.com.
"},{"name":"Christine Borgman","affiliation":"Center for Knowledge Infrastructures, University of California Los Angeles, Los Angeles, California, U.S.A.","bio":"Christine L. Borgman, Distinguished Research Professor and Director of the Center for Knowledge Infrastructures at University of California, Los Angeles, is the author of more than 250 publications in information studies, computer science, communication, and law. These include three books from MIT Press: Big Data, Little Data, No Data: Scholarship in the Networked World (2015), Scholarship in the Digital Age: Information, Infrastructure, and the Internet (2007); and From Gutenberg to the Global Information Infrastructure: Access to Information in a Networked World (2000). She is a Fellow of the American Association for the Advancement of Science and of the Association for Computing Machinery. Her research addresses scientific data practices and policy; open science and open access; data sharing and reuse; knowledge infrastructures; privacy; and data governance. She has held visiting appointments at Harvard University, the University of Oxford, Lund University, Royal Netherlands Academy of Arts and Sciences, Loughborough University, Budapest University of Economic Sciences, and Eötvös Loránd University.
"},{"name":"Joseph K. Blitzstein","affiliation":"Department of Statistics, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Joseph “Joe” Blitzstein is Professor of the Practice in Statistics and Director of Undergraduate Studies for the statistics department at Harvard University, where he has taught since 2006, after completing his PhD in Mathematics (with a masters in statistics) at Stanford. At Harvard, he has taught a wide range of undergraduate and graduate probability, statistics, and data science courses. His probability course Stat 110 has grown to over 500 students (which in 2017 was the third-largest course at Harvard), and free online versions of the materials can be found at stat110.net. With Hanspeter Pfister from Computer Science, he launched Harvard's first data science course and first joint Computer Science-Statistics course, CS 109/Stat 121, in 2013. Joe’s main research interests are in statistical inference for networks and foundational issues in statistics and data science.
"},{"name":"Yoav Benjamini","affiliation":"Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel","bio":"Yoav Benjamini is the Nathan and Lily Silver Professor of Applied Statistics at the Department of statistics and Operations Research at Tel Aviv University. He holds BSc in Physics and BSc and MSc in mathematics from the Hebrew University (1976), and PhD in Statistics from Princeton University (1981). He is a member of the Sagol School of Neuroscience and the Edmond Safra Bioinformatics Center, both at Tel Aviv University. He was a visiting professor at Wharton, University of California, Berkeley, Stanford, and Columbia Universities. Benjamini is a co-developer of the widely used and cited False Discovery Rate concept and methodology. His research topics are selective and simultaneous inference, replicability and reproducibility in science, and data mining, with applications in Biostatistics, Bioinformatics, Animal Behavior, Brain Imaging and Health Informatics. He received the Israel Prize for research in statistics and economics, and is a member of the Israel Academy of Sciences and Humanities.
"},{"name":"David Banks","affiliation":"Department of Statistical Science, Duke University, Durham, North Carolina, U.S.A.","bio":"David Banks is a Professor in the Department of Statistical Science at Duke University. Previously, he held positions at the University of Cambridge, Carnegie Mellon University, the National Institute of Standards and Technology, the United States Department of Transportation, and the Food and Drug Administration. He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He is a past editor of the Journal of the American Statistical Association and a founding editor of Statistics and Public Policy. His research areas include adversarial risk analysis, dynamic text networks, human right data analysis, and some topics in machine learning.
"},{"name":"Yves Atchadé","affiliation":"Department of Mathematics and Statistics and Faculty of Computing and Data Science, Boston University, Boston, Massachusetts, U.S.A.","bio":"Yves Atchadé is Professor of Statistics in the Department of Mathematics and Statistics and a Founding Member of the Faculty of Computing and Data Science at Boston University. He is an Elected Fellow of the Institute of Mathematical Statistics (IMS). Before joining Boston University, he was Assistant and Associate Professor of Statistics at the University of Michigan, and at the University of Ottawa. After undergraduate studies in Benin and Cote d’Ivoire, Atchadé received a PhD in statistics from the Universite de Montreal in 2003, and was a postdoctoral fellow at Harvard University in 2003–2004. His research interests revolve broadly around computational methods in statistics and machine learning, with a particular interest in Bayesian methods.
"},{"name":"Liberty Vittert","affiliation":"Olin Business School, Washington University in St. Louis, St. Louis, Missouri, U.S.A.","bio":"Dr. Liberty Vittert, PhD, is a Professor of the Practice of Data Science at the Olin Business School at the Washington University in St. Louis and a Senior Fellow at Harvard University as well as being the Feature Editor of the Harvard Data Science Review and co-host of the Harvard Data Science Review podcast. She is also the host of the MIT (Massachusetts Institute of Technology) data science podcast “Data Nation”. She is a graduate of MIT as well as Le Cordon Bleu Paris and the University of Glasgow.
She is the on-air statistician for News Nation and her OpEd pieces have been featured in Fox News Channel, Popular Science, US News, Newsweek, Business Insider, International Business Times, CBS News, MarketWatch, Boston Globe, The Hill and The Conversation. She is the on-air statistician for the Discovery Channel show \"Strange Evidence\" and “Mission Unexplained. In 2022, she was named one of the top 50 undergraduate business school professors in the United States by Poets and Quants.
As a Royal Statistical Society Ambassador, BBC Expert Woman, and an Elected Member of the International Statistical Institute, Liberty writes popular science books on data. She is also an Associate Editor for the Harvard Data Science Review and is on the board of USA for the UN Refugee Agency (UNHCR) as well as the HIVE, a UN Refugee Agency data initiative for refugees. She is on the International Advisory Council of the UN Migration Agency (IOM)
"},{"name":"Antony Unwin","affiliation":"Department of Computer Oriented Statistics and Data Analysis, University of Augsburg, Augsburg, Germany; American Statistical Association","bio":"Antony Unwin was the first Professor of Computer-Oriented Statistics and Data Analysis at the University of Augsburg in Germany. Earlier he was at Trinity College Dublin. He is a fellow of the American Statistical Association, co-author of the book Graphics of Large Datasets, and co-editor of the Handbook of Data Visualisation. His research focuses on data visualisation, especially in interactive graphics. His research group developed several pieces of interactive graphics software, \"the Augsburg Impressionists,\" and wrote packages for R. Antony is author of the book Graphical Data Analysis with R, published in 2015 by CRC Press.
"},{"name":"Hamit Hamutcu","affiliation":"Elements, Boston, Massachusetts, U.S.A.","bio":"Hamit Hamutcu brings 30 years of industry and consulting experience in data and AI. He was a Senior Advisor at the Institute for Experiential AI at Northeastern University where he led strategy and educational programs in AI upskilling. He is the founder of AIxEd, an AI in Education event and ecosystem initiative, and a co-founder of Elements, a data skills assessment platform. Previously, Hamit was a managing partner for Peppers & Rogers Group in Stamford, Connecticut, where he headed the Global Analytics Group and launched several offices for the company globally.
"},{"name":"Nancy Potok","affiliation":"Center for Excellence in Public Leadership, George Washington University, Washington DC, U.S.A.; New York University, New York, New York, U.S.A.; Research Triangle Institute (RTI) International, Research Triangle Park, North Carolina, U.S.A. ","bio":"Nancy Potok is the former Chief Statistician of the United States, directing statistical and science policy at the U.S. Office of Management and Budget in the Executive Office of the President and leading the U.S. statistical community internationally. Dr. Potok has over 30 years of leadership experience in the public, non-profit, and private sectors. She served as Deputy Director and Chief Operating Officer, as well as CFO, of the U.S. Census Bureau and Deputy Under Secretary for Economic Affairs at the U.S. Department of Commerce; Senior Vice President for Economic, Labor, and Population Studies at NORC at the University of Chicago; and Chief Operating Officer at McManis & Monsalve Associates, a business analytics consulting firm. She was appointed as a Commissioner on the congressionally chartered Commission on Evidence-based Policy Making, which submitted its recommendations and report to Congress and the President in September 2017. Dr. Potok is an adjunct professor at the Trachtenberg School of Public Policy and Public Administration at The George Washington University. She is the recipient of the Presidential Rank Award, the Secretary of Commerce Gold Medal and Silver Medals for outstanding achievements, the Arthur S. Flemming Award, the Enterprise Risk Manager of the Year Award given by the Association for Federal Enterprise Risk Management, and the Distinguished Alumni Award from The George Washington University. Dr. Potok is an elected Fellow of the National Academy of Public Administration (NAPA) and serves on the Board of Trustees for the Institute of Pure and Applied Mathematics at University of California, Los Angeles. She received her PhD in public policy and public administration at the George Washington University.
"},{"name":"Stephanie Dick","affiliation":"Department of History and Sociology of Science, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.","bio":"Stephanie Dick is an Assistant Professor in the School of Communication at Simon Fraser University. Her work sits at the intersection of the history of computing and the history of mathematics, primarily in the nineteenth through twentieth century United States. Her first project explores early attempts to automate mathematical theorem-proving and debates about the character of proof and of human cognitive faculties that informed them. Her second project explores the introduction of computing to domestic law enforcement in the 1960s. She is co-editor, with Janet Abbate, of Abstractions and Embodiments: New Histories of Computing and Society . She serves on the Board of the IEEE Annals of the History of Computing and on the Teaching Advisory Board for the Freedom Summer Collegiate program in Mississippi. Stephanie completed her PhD in History of Science at Harvard University in 2015; she was a Junior Fellow with the Harvard Society of Fellows between 2015 and 2018; and prior to joining the faculty at SFU, she was an Assistant Professor of History and Sociology of Science at the University of Pennsylvania.
"},{"name":"Christopher J. Phillips","affiliation":"Department of History, Dietrich College of Humanities and Social Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, U.S.A.","bio":"Christopher J. Phillips is Associate Professor of History at Carnegie Mellon University, where he teaches the history of science. He is the author of The New Math: A Political History (Chicago University Press) and Scouting and Scoring: How We Know What We Know about Baseball (Princeton University Press). Currently, he is working on a history of statistics in medicine, focused particularly on researchers associated with the National Institutes of Health. More broadly, he researches the history of data and statistics, and in particular the spread of mathematical methods into new fields. He previously taught at New York University (NYU) and holds a PhD in history of science from Harvard University.
"},{"name":"Mark Glickman","affiliation":"Department of Statistics, Harvard University, Cambridge, Massachusetts, U.S.A.; Center for Healthcare Organization and Implementation Research, U.S. Department of Veterans Affairs, Boston, Massachusetts, U.S.A.","bio":"Dr. Mark Glickman, a Fellow of the American Statistical Association, is Senior Lecturer on Statistics at the Harvard University Department of Statistics, and Senior Statistician at the Center for Healthcare Organization and Implementation Research (CHOIR), a Veterans Administration Center of Innovation. Dr. Glickman is known for having invented the Glicko and Glicko-2 rating systems for head-to-head competition, both of which have been adopted by many gaming organizations internationally. Dr. Glickman has served as a member of the U.S. Chess Ratings Committee since 1985, and has been the Chair of the Committee over most of that period. He co-founded and co-organizes the New England Symposium on Statistics in Sports, a bi-annual conference on the research and practice of applying statistical methods in sports. He is Associate Editor for the Journal of Quantitative Analysis in Sports and was Editor-in-Chief 2015–2017. At Harvard, he is the founding head of the Lab for Sports Analytics.
"},{"name":"Iavor Bojinov","affiliation":"Harvard Business School, AI and Data Science Operations Lab, Department of Statistics, and the Harvard Data Science Initiative, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Iavor Bojinov is the James Dinan and Elizabeth Miller Associate Professor of Business Administration at the Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and the Harvard Data Science Initiative. His research focuses on developing novel statistical methodologies to make business experimentation more rigorous, safer, and efficient, specifically homing in on the application of experimentation to the operationalization of artificial intelligence (AI), the process by which AI products are developed and integrated into real-world applications. His work has been published in top academic journals such as Annals of Applied Statistics, Biometrika, The Journal of the American Statistical Association, The Journal of Econometrics, Quantitative Economics, Management Science, and Science, and has been cited in Forbes, The New York Times, The Washington Post, and Reuters, among other outlets.
"},{"name":"Natesh Pillai","affiliation":"Department of Statistics, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Natesh Pillai is a Professor of Statistics at Harvard University. He obtained his PhD from Duke University and joined Harvard in 2010.
He was elected fellow of the Institute of Mathematical Statistics in 2021 and awarded the young researcher award by the International Indian Statistical Association in 2018. His main research interests are computational statistics, reinforcement learning, applied probability, and problems in climate science. He has worked extensively in industry; most recently he was an Amazon scholar and will be joining LinkedIn as a Distinguished Engineer.
"},{"name":"Yazhen Wang","affiliation":"Department of Statistics, School of Computer, Data & Information Sciences, University of Wisconsin-Madison, Wisconsin, U.S.A.","bio":"Yazhen Wang is Professor of Statistics at the University of Wisconsin-Madison and the Statistics Department chair during 2015–2018 and 2021–present. He obtained his PhD in statistics from the University of California at Berkeley in 1992. He is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics. He was an NSF program director during 2007–2009. His research areas include financial econometrics, machine learning, quantum computation, quantum statistics, high dimensional statistical inference, nonparametric function estimation, wavelets and multi-scale methods, change points, long-memory processes, and order restricted inference.
"},{"name":"Hanti Lin","affiliation":"Department of Philosophy, University of California Davis, Davis, California, U.S.A.","bio":"Hanti Lin is Associate Professor of Philosophy at the University of California, Davis. His research focuses on foundational issues about scientific inference: ranging from Hume's problem of induction and other traditional topics in epistemology, to some issues directly relevant to data science, such as the frequentism-Bayesianism debate and the justification of causal inference from observational data. He is the author of Bayesian Epistemology in the Stanford Encyclopedia of Philosophy, and has publications spanning the fields of philosophy and machine learning. He was previously a postdoctoral fellow at the Australian National University, and holds a PhD in philosophy from Carnegie Mellon University.
"},{"name":"Lexing Xie","affiliation":"School of Computing, Australian National University, Canberra, Australia","bio":"Lexing Xie is Professor at School of Computing, the Australian National University. She directs the ANU Computational Media Lab and the Humanising Machine Intelligence initiative. Her research interests are in machine learning, social media, and ethical AI. Of particular interest are economic perspectives of online attention, stochastic point process models, neural networks for sequences and networks, computational mapping of moral dilemmas and controversy. Her research is supported by the US Air Force Office of Scientific Research, Data61, Data to Decisions CRC and the Australian Research Council, and others. She was a research staff member at IBM T.J. Watson Research Center in New York, and adjunct assistant professor at Columbia University. She received BS from Tsinghua University, Beijing, China, and MS and PhD degrees from Columbia University, all in electrical engineering.
"},{"name":"Bin Yu","affiliation":"Department of Statistics, Department of Electrical Engineering and Computer Sciences, and Center for Computational Biology, University of California Berkeley, Berkeley, California, U.S.A.","bio":"Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in Statistics, EECS, and Computational Biology at UC Berkeley. Her recent research focuses on statistical machine learning practice, algorithm, and theory, veridical data science for trustworthy AI, and interdisciplinary data problems in neuroscience, genomics, and precision medicine. She is a member of the U. S. National Academy of Sciences and American Academy of Arts and Sciences. She was a Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, and Rietz Lecturer of the Institute of Mathematical Statistics (IMS) , and won the E. L. Scott Award given by the Committee of Presidents of Statistical Societies (COPSS). She delivered the IMS Wald Lectures and the COPSS Distinguished Achievement Award and Lecture (DAAL) (formerly Fisher) at the Joint Statistical Meetings (JSM) in August, 2023. She holds an Honorary Doctorate from The University of Lausanne. She served on the inaugural scientific advisory board of the UK Turing Institute of Data Science and AI, and is serving on the editorial board of PNAS and as a senior advisor at the Simons Institute for the Theory of Computing at UC Berkeley.
"},{"name":"Dacheng Xiu","affiliation":"Econometrics & Statistics Group, Booth School of Business, University of Chicago, Chicago, Illinois, U.S.A.","bio":"Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He has served as Co-Editor for the Journal of Financial Econometrics and has been on the editorial board as an Associate Editor for many prestigious journals, including the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, and Management Science. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. He has been recognized as one of Poets & Quants’ Best 40-under-40 Business School Professors of 2023. At Booth, he teaches a variety of courses related to FinTech, Big Data, and Statistical Inference to MBA, college, and PhD students. Xiu earned his PhD and MA in applied mathematics from Princeton University.
"},{"name":"Ryan Rogers","affiliation":"Apple, San Francisco, California, U.S.A.","bio":"Ryan Rogers is an Engineering Manager at Apple working on privacy and evaluation for agentic systems. Prior to this position, he served as Senior Staff Software Engineer in the Ads AI and Applied Research teams at LinkedIn, where he designed and implemented private algorithms and systems for data analytics and machine learning tasks. He has also worked in the Machine Learning Privacy Team at Apple as the technical lead on developing the algorithms for the private federated learning project. He received his PhD in Applied Mathematics and Computational Sciences from the University of Pennsylvania.
"},{"name":"Jörg Drechsler","affiliation":"Department for Statistical Methods, Institute for Employment Research, Nürnberg, Germany; Institute for Statistics, Ludwig Maximilians University of Munich, Munich, Germany","bio":"Jörg Drechsler is head of the Department for Statistical Methods at the Institute for Employment Research in Nürnberg, Germany and professor at the Institute for Statistics at the Ludwig-Maximilians-University in Munich. He received his PhD in Social Science from the University in Bamberg in 2009 and his Habilitation in Statistics from the Ludwig-Maximilians-Universität in Munich in 2015. He is also an Associate Research Professor in the Joint Program in Survey Methodology at the University of Maryland and Honorary Professor at the University of Mannheim, Germany. His main research interests are data confidentiality and nonresponse in surveys.
"},{"name":"Mengdi Wang","affiliation":"Center for Statistics and Machine Learning, Department of Electrical and Computer Engineering, Department of Computer Science, and the Omenn-Darling Bioengineering Institute, Princeton University, Princeton, New Jersey, U.S.A.","bio":"Mengdi Wang is associate professor at the Center for Statistics and Machine Learning, Department of Electrical and Computer Engineering, Department of Computer Science (by courtesy) and the Omenn-Darling Bioengineering Institute (by courtesy) at Princeton University. Mengdi got her PhD in EECS from MIT in 2013, where she worked with Dimitri P. Bertsekas at the Laboratory for Information and Decision Systems. She was a visiting research scientist at DeepMind, Institute of Advanced Studies, and Simons Institute on Theoretical Computer Science. Her research focuses on reinforcement learning, generative AI and AI for science applications. Mengdi received the Young Researcher Prize in Continuous Optimization of the Mathematical Optimization Society in 2016 (awarded once every three years), the Princeton SEAS Innovation Award in 2016, the NSF Career Award in 2017, the Google Faculty Award in 2017, and the MIT Tech Review 35-Under-35 Innovation Award (China region) in 2018, WAIC YunFan Award 2022, AACC’s Donald Eckman Award 2024. She served as a Program Chair for ICLR 2023 and Senior AC for Neurips, ICML and COLT, associate editor for Harvard Data Science Review, Operations Research and Mathematics of Operations Research, and is on the editorial board of Journal of Machine Learning Research.
"},{"name":"V. Joseph Hotz","affiliation":"Harris School of Public Policy, University of Chicago, Chicago, Illinois, U.S.A.","bio":"V. Joseph Hotz is a Research Professor in the Harris School of Public Policy at the University of Chicago and the Arts & Sciences Distinguished Professor Emeritus of Economics and Public Policy at Duke University. He was the founding Director of the UCLA Census Research Data Center, a member of the NASEM’s Committee on National Statistics, the chair of the Population Association of America’s Committee on Population Statistics and is currently a member of the National Center for Health Statistics’ Board of Scientific Advisors. He is an elected Fellow of the Econometric Society, the Society of Labor Economists, the International Association of Applied Econometrics, and the Southern Economics Association. His current research focuses on the intergenerational linkages in health, cognition and economic well-being between parents and their adult children, innovation in data collection and measurement in studies of aging, and the trade-offs between data usability and privacy protection in the release of publicly available data sets.
"},{"name":"Tracy Ke","affiliation":"Department of Statistics, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Tracy Ke is currently Associate Professor of Statistics at Harvard University. She received her PhD from Princeton University in 2014, advised by Professor Jianqing Fan. Prior to joining Harvard, she was Assistant Professor of Statistics at The University of Chicago from 2014 to 2018. Her research interests include network data analysis, high-dimensional statistics, text mining, and machine learning. Recently, she is especially interested in developing optimal spectral methods for network and text data. She was the recipient of NSF CAREER award, ASA Gottfried E. Noether Young Scholar Award, IMS Peter Gavin Hall Early Career Prize, and is currently a Sloan Research Fellow.
"},{"name":"Galin L. Jones","affiliation":"University of Minnesota, Minneapolis, Minnesota, U.S.A.","bio":"Galin L. Jones is Dr. Lynn Y. S. Lin Professor of Statistics and inaugural Vice Provost for AI at the University of Minnesota. He studied Mathematics (BS, 1996) and Statistics (PhD, 2001) at the University of Florida. Jones is a Fellow of both the American Statistical Association and the Institute for Mathematical Statistics and Co-Editor of the Journal of Computational and Graphical Statistics (2021–2024). His research interests include Markov chain Monte Carlo theory and application, statistical theory and methods in Bayesian and frequentist domains, philosophy of science, neuroimaging applications, and astrostatistics.
"},{"name":"Sara Algeri","affiliation":"School of Statistics, University of Minnesota, Minneapolis, Minnesota, U.S.A.","bio":"Sara Algeri is an Associate Professor in the School of Statistics at the University of Minnesota. She received her PhD in 2018 from Imperial College London. Her research interests mainly lie in astrostatistics, statistical inference, and goodness-of-fit. The main purpose of her work is to provide highly generalizable statistical solutions that directly address fundamental questions in the physical sciences and can, at the same time, be easily applied to any other scientific problem following a similar statistical paradigm. Motivated by problems arising in physics and astronomy, her current research focuses on developing computationally tractable inferential tools for signal detection in the presence of high background and model assessment.
"},{"name":"Miguel Paredes","affiliation":"Silicon Foundry, San Francisco, California, U.S.A.; AI Fund, Palo Alto, California, U.S.A.; Milemark Capital, Silicon Valley, California, U.S.A.; Pacífico Business School, Lima, Peru","bio":"Miguel Paredes is a senior AI executive, adviser, and consultant, a Venture Partner at Silicon Foundry, and a fellow / adviser for the AI Fund and Milemark Capital, two AI focused VCs. Until recently he was the Vice President of AI & Data Science at Albertsons, where he led strategy and value creation with Data & AI, and co-led the Generative AI Center of Excellence. Before that, he led AI & Data Analytics at a large economic conglomerate in Peru. Miguel holds a PhD in applied artificial intelligence and data science as well as masters’ degrees in engineering and economic development, all from MIT. He is the Executive Director of Pacifico Business School's Executive MBA, where he is also a Professor. Miguel is an alumni of Harvard Business School where he completed the General Manager Program (GMP). He is also an angel investor and helps startups with their AI strategy and product development.
"},{"name":"José Zubizarreta","affiliation":"Department of Health Care Policy, Harvard Medical School, Harvard University; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University; Department of Statistics, Faculty of Arts and Sciences, Harvard University, Boston, Massachusetts, U.S.A.","bio":"José Zubizarreta, PhD, is Professor in the Department of Health Care Policy at Harvard Medical School, Professor in the Department of Biostatistics at Harvard School of Public Health, and Faculty Affiliate in the Department of Statistics at the Faculty of Arts and Sciences at Harvard University. His work centers on the development of statistical methods for causal inference to advance research in health care and public policy. He was a Fulbright Scholar and has received funding from the Alfred P. Sloan Foundation. He serves as Associate Editor of the Annals of Applied Statistics, the Journal of American Statistical Association, and the Journal of Royal Statistical Society. He is a Fellow of the American Statistical Association, and is a recipient of the Kenneth Rothman Award, the William Cochran Award, and the Tom Ten Have Memorial Award.
"},{"name":"Jing Cao","affiliation":"Department of Statistics and Data Science, Southern Methodist University, University Park, Texas, US.A.","bio":"Jing Cao is a Professor of Statistics and the Director of Graduate Studies in the Department of Statistics and Data Science at Southern Methodist University. She earned her PhD in statistics from the University of Missouri at Columbia in 2005. Her research interests encompass clinical trial design, Bayesian methods and applications, and machine learning in text analysis. Recently, her work has focused on integrating statistical learning with machine learning algorithms for sentiment analysis and developing predictive models using electronic health record data. Her research has received funding from NIH, NSF, Patient-Centered Outcomes Research Institute, and Institute of Education Sciences. Prof. Cao is a fellow of American Association of Wine Economists. She also chaired the ASA Committee on Professional Ethics from 2021 to 2024.
"},{"name":"Philippe Masset","affiliation":"EHL Hospitality Business School, Lausanne, Switzerland","bio":"Dr. Philippe Masset is an Associate Professor of Finance at EHL Hospitality Business School. His research interests include wine economics, hospitality economics, empirical finance, and alternative investments. He currently serves as President of the Alliance for Research on Wine and Hospitality Management, sits on the Board of the European Association of Wine Economists, and is a member of the editorial board of the Journal of Wine Economics.
His work has been published in journals such as the Journal of Wine Economics, Economic Modelling, Journal of Alternative Investments, Emerging Markets Review, and in the Handbook of Financial Econometrics and Statistics. His research has received broad media coverage, including in Bloomberg, Reuters, The Times, The Daily Telegraph, China Daily, and La Repubblica.
He has taught finance and wine-related courses at various academic institutions, including the University of Bordeaux, KEDGE Business School, and the University of Fribourg. In addition, he serves on the Investment Committee of Hotela and is President of the Foundation Board of the Prévotel pension fund.
"},{"name":"Kyle Cranmer","affiliation":"University of Wisconsin - Madison, Madison, Wisconsin, U.S.A.","bio":"Kyle Cranmer is the David R. Anderson Director of the Data Science Institute and a Professor of Physics with courtesy appointments in Statistics and Computer Science at the University of Wisconsin – Madison. He obtained his BA in Mathematics and Physics from Rice University and PhD in Physics from the University of Wisconsin-Madison in 2005. He was awarded the Presidential Early Career Award for Science and Engineering in 2007, the National Science Foundation's Career Award in 2009, and became a Fellow of the American Physical Society in 2021 for his work at the Large Hadron Collider. Cranmer was a Professor of Physics and Data Science at NYU from 2007–2022, during which time developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in 2012. His current interests are at the intersection of physics, statistics, and machine learning.
"},{"name":"Victor S.Y. Lo","affiliation":"Data Science and Artificial Intelligence Center of Excellence, Workplace Investing, Fidelity Investments, Boston, Massachusetts, U.S.A.","bio":"Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader with over three decades of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance Analytics, Product Development, Healthcare Analytics, Operations Management, Transportation, Human Resources, and AI Ethics. He is actively engaged with causal inference, AI ethics, and is a pioneer of Uplift/True-lift modeling, a key subfield of data science.
Victor has managed teams of data scientists/quantitative analysts in multiple organizations. He is currently SVP of Data Science & AI, Workplace Investing at Fidelity Investments. Previously he managed advanced analytics/data science teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).
For academic & industry services, Victor has served as an elected board member of the National Institute of Statistical Sciences (NISS) from 2019-2025. Additionally, he has been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for multiple academic journals. He was also a visiting research fellow and corporate executive-in-residence at Bentley University. Victor earned a master of science in Operational Research from Lancaster University and a PhD in Statistics from the University of Hong Kong and was a Postdoctoral Fellow in Management Science at the University of British Columbia. He has co-authored a graduate-level econometrics book and published numerous articles in Data Mining, Marketing, Statistics, Management Science, and AI Ethics literature, and co-authors a newly published graduate-level data science textbook titled “Cause-and-Effect Business Analytics & Data Science.\"
Weijie Su is an Associate Professor in the Wharton Statistics and Data Science Department and, by courtesy, in the Departments of Biostatistics, Epidemiology and Informatics, Computer and Information, and Mathematics, at the University of Pennsylvania. He is a co-director of Penn Research in Machine Learning (PRiML) Center. Prior to joining Penn, he received his PhD in statistics from Stanford University in 2016 and bachelor's degree in mathematics from Peking University in 2011. His research interests span statistical foundations of generative AI, privacy-preserving machine learning, high-dimensional statistics, and optimization. He serves as an associate editor of Harvard Data Science Review, Journal of the American Statistical Association, Journal of Machine Learning Research, Annals of Applied Statistics, Foundations and Trends in Statistics, Operations Research, and Journal of the Operations Research Society of China. His work has been recognized with several awards, such as the Stanford Anderson Dissertation Award, NSF CAREER Award, Sloan Research Fellowship, IMS Peter Hall Prize, SIAM Early Career Prize in Data Science, ASA Noether Early Career Award, ICBS Frontiers of Science Award in Mathematics, IMS Medallion Lectureship, and Outstanding Young Talent Award in the 2025 China Annual Review of Mathematics. He is a Fellow of the IMS.
"},{"name":"Veronica Barassi","affiliation":"School of Humanities and Social Sciences, University of St. Gallen, St. Gallen, Switzerland","bio":"Prof. Veronica Barassi is an Italian anthropologist and media scholar whose research explores the human and social dimensions of artificial intelligence and data technologies. She is Professor of Media and Communication Studies and Vice-Dean of the School of Humanities and Social Sciences at the University of St. Gallen. Educated at Goldsmiths, University of London, where she completed her PhD in Social Anthropology, Barassi brings ethnographic depth to the study of AI systems, investigating how data infrastructures, automation, and algorithmic decision-making and human machine communication intersect with everyday life.
Her research has been supported by the Arts and Humanities Research Council, the Economic and Social Research Council (ESRC), British Academy (BA) in the UK and the Swiss National Science Foundation (SNSF) and Inno Suisse in Switzerland. Through projects such as The Human Error Project, she examines how human biases, errors, and assumptions are reproduced within algorithmic systems — and how these failures reveal the social and ethical limits of machine intelligence.
Barassi is the author of Child | Data | Citizen: How Tech Companies are Profiling Us from Before Birth (MIT Press, 2020) and I Figli dell’Algoritmo (LUISS Press, 2021), widely cited works that trace how datafication begins before birth and continues throughout human development. Her findings have informed international discussions on data ethics and AI governance, including contributions to the UK’s Age Appropriate Design Code and her TED TALK has reached over 2 millions views worldwide, sparking debate on the social costs of data-driven innovation. Through her interdisciplinary work, she invites data scientists and technologists to rethink the role of error, bias, and accountability in the design and deployment of intelligent systems.
"},{"name":"Pavlos Protopapas","affiliation":"Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Pavlos Protopapas is an educator and researcher at Harvard University whose work bridges data science, machine learning, and astrophysics. He teaches Introduction to Data Science (CS109A), Advanced Topics in Data Science (CS109B), and MLOps (AC215), and has previously offered courses in Deep Reinforcement Learning, Capstone Projects in Data and Computational Science, and Physics-Informed Neural Networks. His courses are also available through Harvard Extension School and HarvardX, and he has contributed to international data science schools in Chile, Colombia, India, and Rwanda.
His research focuses on data-driven and physics-informed modeling of complex astrophysical phenomena. He has developed deep learning methods for astronomical data analysis, including transformer-based architectures for temporal and spectral modeling (Astromer, Spectromer) and machine learning pipelines for the Event Horizon Telescope (EHT) aimed at inferring black hole parameters such as spin and accretion dynamics. Beyond astronomy, his interests include Physics-Informed Neural Networks (PINNs), hybrid physical–statistical modeling, model interpretability, uncertainty quantification, and scalable AI systems for scientific discovery.
"},{"name":"Jim Greiner","affiliation":"Access to Justice Lab, Harvard Law School, Harvard University, Cambridge, Massachusetts, U.S.A.; Harvard Law School, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Jim Greiner is the Honorable S. William Green Professor of Public Law at Harvard Law School, where he teaches courses on civil procedure, expert witnesses, and the use of randomized studies in the law. He is the Faculty Director of the Access to Justice Lab, a research center focusing on conducting credible research on access to justice and court administration issues, see a2jlab.org. Jim graduated Phi Beta Kappa from the University of Virginia in 1991; was on the Law Review at the University of Michigan Law School, graduating in 1995; clerked for the Honorable Patrick E. Higginbotham on the Fifth Circuit; spent three years with the United States Department of Justice’s Federal Programs Branch litigating constitutional and agency challenges to federal government action; practiced for three more years with the law firm of Jenner & Block, LLC; obtained his Ph.D. in Statistics from Harvard University in 2007; and joined the Harvard Law faculty that same year. Jim's research focuses on randomized field experiments in the legal setting, particularly in access to justice and adjudicatory administration. His work has appeared in a variety of venues including Science, the Yale Law Journal, the Harvard Law Review, the Journal of the Royal Statistical Society, the Quarterly Journal of Political Science, the Review of Economics and Statistics, Jurimetrics, Daedalus, the Annals of Applied Statistics, and the Proceedings of the National Academy of Sciences.
"},{"name":"Susan Paddock","affiliation":"National Opinion Research Center (NORC), University of Chicago, Chicago, Illinois, U.S.A.","bio":"Susan Paddock is Chief Statistician and Executive Vice President at National Opinion Research Center (NORC) at the University of Chicago. She is generally responsible for the methods of design and analysis used in NORC proposals and projects and for the NORC corporate research and development enterprise. Her team at NORC includes statisticians, survey methodologists, data scientists, and quantitative and computational social scientists who are engaged in projects for federal, other governmental, and private sector clients on topics that span numerous substantive areas including education, health, and public affairs. Susan has published methodological developments in the areas of Bayesian methods, causal inference, hierarchical modeling, longitudinal data analysis, and missing data techniques, most often motivated by collaborations in the fields of health policy and substance abuse treatment research. Prior to joining NORC, Susan was a senior statistician with RAND Corporation and led their statistics group for five years. She has served on editorial boards for the Annals of Applied Statistics, Journal of the American Statistical Association, and Medical Care, and has served on committees for the American Statistical Association and the National Academies of Sciences, Engineering, and Medicine. Susan is a fellow of the American Statistical Association (ASA) and was the recipient of the 2013 Mid-career Award of the Health Policy Statistics Section of the ASA. She received her PhD in statistics from Duke University and her BA in mathematics and biostatistics from the University of Minnesota.
"},{"name":"Suraj Srinivasan","affiliation":"Harvard Business School, Harvard University, Boston, Massachusetts, U.S.A.","bio":"Suraj Srinivasan is the Philip J. Stomberg Professor of Business Administration at Harvard Business School, where he co-leads the HBS MBA program as the chair of the MBA Elective Curriculum. Professor Srinivasan’s expertise spans research domains of data science and artificial intelligence, and corporate governance and boards of directors.
Professor Srinivasan’s research on how artificial intelligence is transforming business covers strategy, execution, and governance of AI and generative AI. He created a new MBA elective course Generative AI for Business Leaders, and Generative AI Strategy and Execution for senior executives. He has published research on AI and data driven decision making at scholarly journals as well as in the Harvard Business Review and has written several case studies on cutting edge phenomenon related to the topics of AI, gen AI, and big data.
Professor Srinivasan is also a widely published expert in corporate governance and boards of directors. He chairs or co-chairs the board governance programs at HBS, frequently advises boards of directors and audit and compensation committees on board effectiveness and corporate governance polices and practices.
Professor Srinivasan earned a bachelor's degree with honors in electrical and electronics engineering and a master's degree in physics with honors from Birla Institute of Technology and Sciences in India. He subsequently earned an MBA from the Indian Institute of Management Calcutta. He received a doctorate degree in business administration from Harvard Business School in 2004 where he received the George S. Dively Award for outstanding thesis research. Prior to joining HBS, Professor Srinivasan was an Assistant Professor of Accounting at the University of Chicago Graduate School of Business from 2004–2008.
"},{"name":"Stephanie Kovalchik","affiliation":"Teamworks, Munich, Germany","bio":"Stephanie Kovalchik brings extensive experience in statistics and sports analytics. She is a Senior Manager of Data Science at Teamworks, where she develops data-driven solutions to enhance athlete performance and decision-making. Previously, she worked with Tennis Australia’s Game Insight Group and was a research fellow at the Institute for Health and Sport at Victoria University, advancing statistical approaches to analyzing performance in professional tennis. Earlier in her career, she held research roles at organizations including the RAND Corporation and the U.S. National Cancer Institute. Stephanie holds a BS from the California Institute of Technology and an MS and PhD in Biostatistics from the University of California, Los Angeles.
"},{"name":"Anna Fergusson","affiliation":"University of Auckland, Auckland, New Zealand","bio":"Anna Fergusson is a Senior Lecturer in Statistics and Data Science at the University of Auckland | Waipapa Taumata Rau in Aotearoa New Zealand. Her work centres on Data Science Education, including the design and evaluation of innovative tasks, technologies, and large-scale assessments that foster learners’ curiosity, creativity, autonomy, and engagement. She teaches undergraduate and postgraduate courses, leads professional development for high school teachers, and drives curriculum and assessment innovation in statistics and data science. Drawing on two decades of secondary and tertiary-level teaching experience, her current research examines how interactive tools and tasks, learning analytics, and AI approaches can support the development of computational and statistical thinking, with a particular interest in the design of interactive coding tasks, automated and semi-automated feedback, and equitable access to data science learning opportunities. Her contributions have been recognised with awards including the 2020 University of Auckland Excellence in Teaching Award, the 2023 New Zealand Association of Mathematics Teachers Bevan Werry Speaker Award, and the 2024 American Statistical Association Jackie Dietz Journal of Statistics and Data Science Education Best Paper Award.
"},{"name":"Paul Kattuman","affiliation":"Cambridge Judge Business School, University of Cambridge, Cambridge, England; Corpus Christi College, Cambridge, England","bio":"Paul Kattuman is Professor of Economics at Cambridge Judge Business School, University of Cambridge, and Fellow and Director of Studies in Management Studies at Corpus Christi College, Cambridge. His recent research focuses on econometric and time-series methods for analysing epidemic-like processes to support real-time decision-making under uncertainty. A central strand of this work develops state-space growth-curve models for tracking and forecasting dynamic social processes. During the COVID-19 pandemic, he played a leading role in operational forecasting and analysis for Public Health England and the UK Health Security Agency in the East of England, alongside developing an India-wide COVID-19 tracker used in state-level policy deliberations. The associated paper, published in Harvard Data Science Review, was recognised in the Financial Times Responsible Business Education Awards (2023) for research with real-world impact. His current research extends these methods to environmental and public health applications, including air-quality early-warning systems and a forecasting toolkit for modelling surges and turning points. He has held visiting appointments at Harvard University and the Harvard Data Science Initiative, among others.
"},{"name":"Michelle H. Wilkerson","affiliation":"School of Education and the Graduate Group in Mathematics and Science Education at the University of California Berkeley, Berkeley, California, U.S.A.","bio":"Michelle H. Wilkerson is an Associate Professor in the School of Education and the Graduate Group in Mathematics and Science Education at the University of California, Berkeley. She directs the Computing, Reasoning, and Expression (“CoRE”) Lab, which conducts basic and applied research that explores how computing practices (e.g., programming, data analysis and visualization, computer simulation, GIS mapping) are changing the ways that young people learn and communicate about our world. Michelle's work exploring K–12 students' data competencies was awarded a 2014 NSF Early CAREER grant, she was the 2020 recipient of the American Educational Research Association's Jan Hawkins Award for Humanistic Research and Scholarship in Learning Technologies, and in 2022 she served as a Co-Chair (with Nicholas Horton) of the National Academies' Workshop on Foundations of Data Science for Students in Grades K–12.
"},{"name":"Mercè Crosas","affiliation":"Computational Social Science and Humanities (CSSH) Lab, Barcelona Supercomputing Center, Barcelona, Spain","bio":"Mercè Crosas is the Director of Computational Social Science and Humanities (CSSH) Lab at Barcelona Supercomputing Center (BSC). The CSSH Lab advances research in the social sciences and humanities by leveraging new data types and developing specialized AI-based and computational methods. Crosas research aims to provide innovative data infrastructures, tools, and best practices to advance science with transparency and rigor, and with open science in mind. Her recent interests focus on defining and building AI Readiness for science, implementing FAIR (Findable, Accessible, Interoperable, Reusable) principles for datasets, workflows, and AI models to enable transparent and reproducible research, and applying privacy-preserving approaches to responsible use of sensitive data. Crosas is also the President of CODATA, the Committee on Data for the International Science Council, and an affiliate researcher of the Institute for Quantitative Social Science at Harvard University, where she spent most of her professional career as Chief Data Science and Technology Officer. She holds a doctorate in Astrophysics from Rice University and a degree in Physics from the University of Barcelona, and was a pre-doctoral and postdoctoral fellow at Harvard University.
"},{"name":"Peter Galison","affiliation":"Department of Physics and the Black Hole Initiative, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Peter Galison is the Joseph Pellegrino University Professor at Harvard University. He directs the Black Hole Initiative at Harvard, a leading center for interdisciplinary research on black holes; he is the Science Team Lead for the Black Hole Explorer, working to detect and measure the black hole’s photon ring. Peter’s writing and film explore the interaction between the principal subcultures of physics—experimentation, instrumentation, and theory—and the embedding of physics in the wider world. In 1997, Peter was awarded a John D. and Catherine T. MacArthur Foundation Fellowship; he won a 1998 Pfizer Award for his book Image and Logic (1997); in 1999, he received the Max Planck and Humboldt Stiftung Prize; in 2017, the Pais Prize from the American Physical Society. More recently, as a member of the Event Horizon Telescope Collaboration, he shared in the 2020 Breakthrough Prize in Fundamental Physics for the capture of the first image of a black hole. His other books include How Experiments End (1987); Einstein’s Clocks, Poincaré’s Maps(2003); and, with Lorraine Daston, Objectivity (2007). His most recent feature-length film is on the journey to image a black hole and to advance progress on the information loss paradox: Black Holes | The Edge of All We Know.
"},{"name":"Ed Humpherson","affiliation":"Office for Statistics Regulation, UK Statistics Authority, London, England, UK","bio":"Ed Humpherson is head of the Office for Statistics Regulation (OSR). The overall aim of OSR is to ensure that statistics and data serve the public good. OSR supports the public’s right to have access to high quality statistics that reflect the society we live in, driving quality improvements in data and statistics right across government in the UK. OSR steps in where there are concerns about the use of statistics and data in public debate.
Ed is Vice Chair of The Motability Foundation, and trustee of Pro Bono Economics and Security Women. He is on the advisory board of the Science Media Centre, the delivery board of Admin Data Research UK, the National Statistician's Expert User Advisory Council and the advisory board of the Ada Lovelace Institute.
Prior to joining the Authority, Ed was a Board Member and Executive Leader for Economic Affairs at the National Audit Office, a post he held since July 2009. He graduated from the University of Edinburgh and is a Chartered Accountant and member of the Institute of Chartered Accountants in England and Wales.
"},{"name":"Adel Daoud","affiliation":"Institute for Analytical Sociology, Linköping University, Linköping, Sweden; Chalmers University of Technology, Gothenburg, Sweden","bio":"Adel Daoud is Professor in Computational Social Science at the Institute for Analytical Sociology, Linkoping University, and Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences at Chalmers University of Technology, Gothenburg, Sweden. Previously he held positions at Harvard University, Stanford University, the University of Cambridge, the Max Planck Institute for the Study of Societies, and the Alan Turing Institute. His research has both a social-scientific and a methodological orientation: substantively he studies global development, focusing on global poverty and health, and methodologically he develops novel approaches in machine learning and causal inference. He has published in journals such as PNAS, Science Advances, World Development, the International Journal of Epidemiology, and Ecological Economics, as well as machine-learning venues including AAAI and NAACL. He leads the AI and Global Development Lab (www.aidevlab.org) that combines AI, Earth observation, and socio-economic theory to analyze sustainable and human development globally.
"},{"name":"Xin Liu","affiliation":"University of California Davis, Davis, California, U.S.A.","bio":"Xin Liu is a Professor of Computer Science at the University of California, Davis. Her research spans machine learning methods and applications in human and animal healthcare, food systems, and communication networks. She is an IEEE Fellow, recipient of the NSF CAREER Award, and co-PI and AI cluster lead of the $20M AI Institute for Next Generation Food Systems.
"},{"name":"Xiaochang Li","affiliation":"Department of Communication and Program in Science, Technology & Society, Stanford University, Stanford, California, U.S.A.","bio":"Xiaochang Li is an Assistant Professor in the Department of Communication and a faculty affiliate in the program in Science, Technology & Society at Stanford University. Her work broadly addresses questions concerning the relationship between information technology and knowledge production and its role in the organization of social life. Her first book, Divination Engines: Natural Language Processing, Artificial Intelligence, and the Making of Algorithmic Culture (University of Chicago Press) examines the history of speech and natural language processing and how the problem of mapping communication to computation fueled the rise of \"data-driven\" machine learning as a privileged and pervasive form of knowledge. Her current book project investigates the history of information retrieval and the rise (and possible fall) of the search engine.
"},{"name":"Linda Young","affiliation":"National Institute of Statistical Sciences, Washington DC, U.S.A.","bio":"Linda J. Young is a Senior Fellow of the National Institute of Statistical Sciences (NISS). She served as Chief Mathematical Statistician and Director of Research and Development of USDA’s National Agricultural Statistics Service (NASS) prior to her retirement from federal service in October, 2026. Linda served on the faculties of three U.S. land grant universities: Oklahoma State University, University of Nebraska, and the University of Florida before joining NASS. She has authored or co-authored four books and more than 100 publications in over 50 different journals, constituting a mixture of statistics and subject-matter journals.
Linda is past editor of the Journal of Agricultural, Biological, and Environmental Statistics and has been associate and guest editor of several other journals. She has served in a broad range of offices within the professional statistical societies, including President of the Eastern North American Region of the International Biometric Society, Vice-President of the American Statistical Association (ASA), Treasurer of the International Biometric Society, Chair of Section U (Statistics) of the American Association for the Advancement of Science (AAAS), Chair of the Committee of Presidents of Statistical Societies and Council Member of the International Statistical Institute (ISI). Linda is a fellow of the ASA, a fellow of AAAS, and an elected member of the ISI.
Linda’s recent research has focused on the use of open-source data, capture-recapture methodology, and integrating survey and non-survey data to produce estimates. She has begun exploring the use of AI in the production of official statistics.
"},{"name":"Aleksandra Faust","affiliation":"Google DeepMind, U.S.A.","bio":"Aleksandra Faust is a Director of Research at Google DeepMind, where she leads Frontier AI Health efforts. Her research focuses on foundation models and world models for complex adaptive systems, treating the AI design pipeline as a learnable, sequential, and self-improving decision-making process. This methodology has driven state-of-the-art improvements across drug discovery, robotics, autonomous driving, and web agents, and led to her founding the field of Automated Reinforcement Learning (AutoRL). Notably, she co-authored the seminal \"Levels of AGI\" framework and led the Gemini Self-improvement research team, developing the reinforcement learning methods behind the Gemini model family. Previously, Aleksandra served as Chief AI Officer at Genesis Molecular AI and held foundational leadership roles at Google Brain, Google Robotics, and Waymo/X. Earlier in her career, she was a Senior R&D Engineer at Sandia National Laboratories. Faust holds a Ph.D. in Computer Science with distinction from the University of New Mexico and an M.S. from the University of Illinois at Urbana-Champaign. She is an IEEE Fellow and a recipient of the IEEE RAS Early Career Award for Industry and the Tom L. Popejoy Dissertation Award, and was named a Distinguished Alumna of the UNM School of Engineering. Her work has been featured in The New York Times, The Economist, and Forbes, and has received multiple Best Paper Awards at premier robotics, machine learning, and systems architecture venues.
"},{"name":"Zhigang Yao","affiliation":"Departments of Statistics and Data Science and Mathematics, National University of Singapore, Singapore","bio":"Zhigang Yao is an Associate Professor in the Department of Statistics and Data Science at the National University of Singapore, with a courtesy appointment in the Department of Mathematics. He develops statistical and geometric methods for revealing structure in complex, high-dimensional data, with particular emphasis on manifold fitting, non-Euclidean data analysis, and geometric approaches to machine learning. His research bridges foundational theory and applications in single-cell genomics, biomedical data analysis, and artificial intelligence, and has been published in JASA, JMLR, PNAS, and Nature Communications. Since 2022, he has been a Visiting Scholar at Harvard University’s Center of Mathematical Sciences and Applications. He serves as an Associate Editor of the Journal of the Royal Statistical Society: Series B and Harvard Data Science Review and is an Elected Member of the International Statistical Institute.
"},{"name":"Zhiwei Zhu","affiliation":"Daniels School of Business, Purdue University, West Lafayette, Indiana, U.S.A.","bio":"Zhiwei Zhu is a Clinical Associate Professor at Purdue University and a former executive at leading global reinsurance companies. His work lies at the intersection of artificial intelligence, data science, and decision governance.
As both an academic and executive leader, Zhu has led educational program and enterprise analytics functions. His recent scholarship, including Data Science at a Fork in the Age of AI: Recognizing Divergent Missions in Research and Education · Harvard Data Science Review and Forecast by Design: Decision-Oriented Time Series Analytics in the Age of AI · Kendall Hunt, argues that AI represents a fundamental shift in how data, analytics, and inference are defined and understood. Building on this perspective, he advocates rethinking analytics education to emphasize purpose-driven decision design and to prepare students to work effectively with AI as both a learning partner and a thinking partner.
A Fulbright U.S. Scholar, Zhu has delivered seminars at business schools across Europe and frequently speaks on the future of analytics, AI, and education.
"},{"name":"Edgar Dobriban","affiliation":"Department of Statistics and Data Science and Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.","bio":"Edgar Dobriban is an associate professor in the Department of Statistics and Data Science at the University of Pennsylvania, with a secondary appointment in Computer and Information Science. His research is at the interface of statistics, machine learning, and artificial intelligence.
"},{"name":"Montserrat Fuentes","affiliation":"Department of Mathematics, St. Edward's University, Austin, Texas, U.S.A.","bio":"Montserrat Fuentes, PhD, is Professor of Statistics in the Mathematics Department at St. Edward’s University, where she served as the 24th president. An American Statistical Association Fellow and former editor of JASA and JABES, she has authored more than 100 peer-reviewed publications, advancing spatial statistics, Bayesian methods, and statistical learning for biomedical and environmental applications.
"},{"name":"Kathleen Gregory","affiliation":"Centre for Science and Technology Studies, Leiden University, Netherlands","bio":"Kathleen Gregory is a Researcher at the Centre for Science and Technology Studies (CWTS) at Leiden University in the Netherlands. She holds a PhD (cum laude) in Science & Technology Studies from Maastricht University, a Master of Science in Library and Information Science, and a Master of Arts in Education. Kathleen is a research fellow at the Research on Research Institute and a member of the Young Academy Leiden. She has extensive expertise in studying research data practices and management; open science and open infrastructures; and scholarly communication. Her research has been funded by the Vienna Science and Technology Fund (WWTF) and the Dutch Research Council (NWO), most recently through a Veni award for individual talent.
"}]},{"title":"Advisory Board","members":[{"name":"Ola Awad","affiliation":"Palestinian Central Bureau of Statistics, Ramallah City, Palestine, Occupied Palestinian Territories","bio":"Dr. Ola Awad is a member of the Palestinian community, regionally and internationally, with over 20 years’ experience working on various topics, including modernization of statistics, sustainable development, gender equality, youth, labor market, Palestinian economy, good governance, statistical literacy, management and communication. She has been the President of the Palestinian Central Bureau of Statistics since 2009, leading the successful development and implementation of various statistical and non-statistical programs on the local, regional and international levels. Dr. Awad is the first woman elected from developing countries and the Arab region to lead the International Association for Official Statistics (IAOS). She is currently a council member in the International Statistics Institute (ISI) for the years 2017–2021. Dr. Awad also represents the Arab region in the HLG-SDGs (High-level Group for Partnership, Coordination and Capacity-Building) for a second round. In addition, she is a Fellow of the American Statistical Association, and has been a fellow of the Aspen Institute Middle East Leadership Initiative (MELI) since 2013. Furthermore, Dr. Awad is a board member of a large number of regional and local bodies. Dr. Awad holds a Doctorate of Business Administration from the University of Liverpool–UK, an MSc in Applied Statistics from Birzeit University, a master’s degree in Business Administration and Management from Boston University and a BSc in Economics from Birzeit University.
"},{"name":"Scott Berinato","affiliation":"Harvard Business Review, Brighton, Massachusetts, U.S.A.","bio":"Scott Berinato, self-professed dataviz geek, is the author of Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations, and Good Charts Workbook. Fast Company said of Good Charts, “It may just be the design manual of the year.” Presentation guru Nancy Duarte called Good Charts “the book I wish I’d written.” Berinato speaks frequently on the power and necessity of good data visualization—including three consecutive year presenting at South by Southwest (SXSW) in Austin, Texas—and has worked with many companies and individuals to up their dataviz game. He is also an award-winning writer and a senior editor at Harvard Business Review, where he writes and edits articles about visualization, data science, and technology and business, and is a content architect responsible for several innovations in publishing. He is a graduate of the Medill School of Journalism at Northwestern University and the University of Wisconsin-Madison.
"},{"name":"David Blei","affiliation":"Department of Computer Science and Department of Statistics, Columbia University, New York City, New York, U.S.A.","bio":"David Blei is a Professor of Statistics and Computer Science at Columbia University and a member of the Columbia Data Science Institute. He studies probabilistic machine learning, including its theory, algorithms, and application. David has received several awards for his research. He received a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), Association for Computing Machinery (ACM)-Infosys Foundation Award (2013), and a Guggenheim fellowship (2017). He is the co-editor-in-chief of the Journal of Machine Learning Research. He is a fellow of the ACM and the Institute of Mathematical Statistics (IMS).
"},{"name":"Rodney Brooks","affiliation":"Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, U.S.A.","bio":"Rodney Brooks is the Panasonic Professor of Robotics (emeritus) at Massachusetts Institute of Technology (MIT), where he was for ten years the director of the Artificial Intelligence Lab and then the Computer Science and Artificial Intelligence Lab (CSAIL) until 2007. After undergraduate and masters degrees in mathematics in Australia he received a PhD in computer science from Stanford in 1981, was a post doc at Carnegie Mellon University (CMU) and MIT, and a faculty member at Stanford, before joining the MIT faculty in 1984. His research areas have been in machine learning, computer vision, robotics, artificial intelligence, and Artificial Life. He was co-founder, CTO and Chairman of iRobot in 1990 and of Rethink Robotics in 2008. He is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and a Fellow of Institute of Electrical and Electronics Engineers (IEEE), Association for Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence (AAAI), and American Association for the Advancement of Science (AAAS).
"},{"name":"Jennifer Chayes","affiliation":"Division of Computing, Data Science, and Society and School of Information, University of California Berkeley, Berkeley, California, U.S.A.","bio":"Jennifer Chayes is Associate Provost of Computing, Data Science, and Society, and Dean of the School of Information at University of California, Berkeley, as well as Professor in four UC Berkeley departments and schools. For 23 years, she was at Microsoft Research where she co-founded and led three interdisciplinary labs: Microsoft Research New England, New York City, and Montreal. Chayes has received numerous awards for both leadership and scientific contributions, including the Anita Borg Institute Women of Vision Leadership Award, the John von Neumann Lecture Award of the Society for Industrial and Applied Mathematics (the highest honor of SIAM), election to the American Academy of Arts and Sciences and the National Academy of Sciences, and an honorary doctorate from Leiden University. Chayes is a Fellow of many societies include the American Association for the Advancement of Science, the American Mathematical Society, the Association of Computing Machinery, the Association of Women in Mathematics, and the Fields Institutes. Chayes’ research areas include phase transitions in computer science, structural and dynamical properties of networks including graph algorithms, and applications of machine learning. Chayes is one of the inventors of the field of graphons, which are widely used for the machine learning of large-scale networks. Her recent work focuses on machine learning, including applications in cancer immunotherapy, ethical decision making, and climate change.
"},{"name":"Cathy Chute","affiliation":"Institute for Applied Computational Science (IACS), Harvard Paulson School of Engineering and Applied Science (SEAS), Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Cathy Chute is the Executive Director of the Institute for Applied Computational Science (IACS) and Assistant Dean for Professional Programs at the Harvard Paulson School of Engineering and Applied Science (SEAS). Cathy's work at IACS, home to two master’s programs in data science and computational science, focuses on growing IACS connections to industry partners and collaborators across the university. She is also responsible for developing new professional executive programs at SEAS. Cathy was previously the Publisher of Harvard Magazine as well as the Executive Director of the Ivy League Magazine Network. Prior to coming to Harvard, Cathy was a director of new business development at the New York Times.
"},{"name":"Sir David Cox","affiliation":"1924–2022","bio":"David Cox studied Mathematics at University of Cambridge. He then worked for six years in government or semi-government research laboratories connected with the aeronautical industry and later the wool industries. Then he held academic posts at Cambridge (Statistical Laboratory) and Chapel Hill (biostatistics) with shorter term appointments at Princeton and Berkeley. On returning to the United Kingdom, he was at Birkbeck College, University of London, and then for 20 years at Imperial College of Science and Technology’s Department of Mathematics. In 1988 he became Warden, Nuffield College, Oxford, and since 1994 has been retired. His past and current research interests span a number of areas of applied and theoretical statistics.
David Cox passed away January 18, 2022.
"},{"name":"Francesca Dominici","affiliation":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts, U.S.A.; Harvard Data Science Initiative, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Francesca Dominici is Clarence James Gamble Professor of Biostatistics, Population and Data Science at the Harvard T.H. Chan School of Public Health and co-Director of the Harvard Data Science Initiative. She was the Interim Co-Editor-In-Chief of Harvard Data Science Review from 2021 to 2023. Her research focuses on the development of statistical methods for the analysis of large and complex data; she leads several interdisciplinary groups of scientists with the ultimate goal of addressing important questions in environmental health science, climate change, comparative effectiveness research in cancer, and health policy. Currently, Dominici’s team uses satellite data and multiple data sources to estimate exposure to air pollution in rural areas in the United States, in India, and in other developing countries. Her studies have directly and routinely impacted air quality policy and led to more stringent ambient air quality standards in the United States. Dominici was recognized on the Thomson Reuters 2015 Highly Cited Researchers list, ranking in the top 1 percent of scientists cited in her field. In 2017, she was named one of the top 10 Italian women scientists with the largest impact in biomedical sciences across the world. In addition to her research interests and administrative leadership roles, Dominici has demonstrated a career-long commitment to promoting diversity in academia. For her contributions, she has earned the Jane L. Norwood Award for Outstanding Achievement by a Woman in the Statistical Sciences and the Florence Nightingale David Award. Dominici currently chairs the University Committee for the Advancement of Women Faculty at the Harvard T.H. Chan School of Public Health. Prior to Harvard, she was on the faculty of the Johns Hopkins Bloomberg School of Public Health, where she also co-chaired the University Committee on the Status of Women. Dominici has degrees from University La Sapienza and University of Padua.
"},{"name":"Christine Franklin","affiliation":"Department of Statistics, Franklin College of Arts and Sciences, University of Georgia, Athens, Georgia, U.S.A.; American Statistical Association (ASA), Alexandria, Virginia, U.S.A.","bio":"Chris Franklin is the K–12 Statistics Ambassador for the American Statistical Association (ASA), an ASA Fellow, and University of Georgia Emerita Statistics faculty. She is the co-author of two statistics textbooks and has published more than 60 journal articles and book chapters. Chris was the writing chair for ASA policy documents (Pre-K–12 GAISE and the Statistical Education of Teachers), and writer for the recent National Council of Teachers of Mathematics (NCTM) policy document Catalyzing Change. She is a past Chief Reader for Advance Placement Statistics, a Fulbright scholar to New Zealand (2015), recipient of the United States Conference on Teaching Statistics (USCOTS) Lifetime Achievement Award, the prestigious ASA Founder’s award and an elected member of the International Statistical Institute (ISI). Chris loves being with her family, running, hiking, scoring baseball games, and reading mysteries.
Interview with Christine Franklin in the Journal of Statistics Education
"},{"name":"Erica Groshen","affiliation":"New York State School of Industrial and Labor Relations, Cornell University, Ithaca, New York, U.S.A.; William Erastus Upjohn Institute for Employment Research, Kalamazoo, Michigan, U.S.A.","bio":"Dr. Groshen is the Visiting Senior Scholar at the Cornell University School of Industrial and Labor Relations and Research Fellow at the Upjohn Institute for Employment Research. From 2013 to 2017, she served as the 14th Commissioner of the U.S. Bureau of Labor Statistics, the principal federal agency responsible for measuring labor market activity, working conditions, and inflation. Before that she was Vice President in the Research and Statistics Group of the Federal Reserve Bank of New York. Her research has centered on jobless recoveries, wage rigidity and dispersion, and the role of employers in the labor market. She co-authored How New is the “New Employment Contract”? from W.E. Upjohn Institute Press and co-edited Structural Changes in U.S. Labor Markets: Causes and Consequences, from M.E. Sharpe, Inc. Dr. Groshen received the 2017 Susan C. Eaton Outstanding Scholar-Practitioner Award from the Labor and Employment Relations Association. She holds a PhD in economics from Harvard University and a BS in mathematics and economics from the University of Wisconsin-Madison.
"},{"name":"Philip Guarino","affiliation":"Elementi Consulting, Paris, France","bio":"Philip Guarino is an international strategy and business development and transformation executive. With over 25 years experience as an executive, entrepreneur and consultant, Philip has worked in over 50 countries advising firms in technology (artificial intelligence, e-commerce, Software as a service), luxury and consumer goods helping them build and execute successful global growth and business transformation strategies. Philip is the Chair of the Paris chapter of Harvard Alumni Entrepreneurs (HAE) and an Editor-in-Chief of HAE's podcast series. Philip is fluent in five languages and is an avid traveler and oenophile. He is a graduate of Harvard College cum laude and holds an MBA from the Wharton School of the University of Pennsylvania in Multinational Management and Entrepreneurship.
"},{"name":"Michael Jordan","affiliation":"Department of Electrical Engineering and Computer Science, Department of Statistics, Miller Institute, Berkeley AI Research Lab, and RISE Lab, University of California Berkeley, Berkeley, California, U.S.A.","bio":"Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences. Professor Jordan is a member of the National Academy of Sciences and a member of the National Academy of Engineering. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the International Joint Conference on Artificial Intelligence (IJCAI) Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the Association for Computing Machinery/Association for the Advancement of Artificial Intelligence (ACM/AAAI) Allen Newell Award in 2009.
“SysML 18: Michael Jordan, Perspectives and Challenges”
"},{"name":"Rita Ko","affiliation":"Racial Equity Analytics Lab, Office of Race and Equity Research, Urban Institute, Washington DC, U.S.A.","bio":"Rita Ko is the chief data officer and director of the Racial Equity Analytics Lab at the Urban Institute. A global data strategy and social impact executive with 15 years of experience as a trusted leader, Ko joins Urban from The Hive and USA for UNHCR’s innovation lab, where she was director. Her leadership is based in taking missions and visions to execution by leading multidisciplinary teams, designing technical roadmaps that build capacity, and operationalizing initiatives across organizations.
In 2019, Rita was selected for the distinguished 92Y WomeninPower fellowship, and received industry recognition in 2017, named as one of the top 10 professionals under 35 in social impact on the LinkedIn Next Wave List. Previously, Rita worked at the Vancouver Mayor’s Office as Former Mayor Gregor Robertson’s Director of Operations for three consecutive terms. She holds a BA in Economics from the University of Toronto and MBA from Cornell University.
"},{"name":"Karim R. Lakhani","affiliation":"Harvard Business School, Laboratory for Innovation Science at Harvard, and NASA Tournament Laboratory, Harvard University, Boston, Massachusetts, U.S.A.","bio":"Karim R. Lakhani is the Dorothy & Michael Hintze Professor of Business Administration at the Harvard Business School. He specializes in technology management, innovation, digital transformation and artificial intelligence (AI). His innovation-related research is centered around his role as the founder and co-director of the Laboratory for Innovation Science at Harvard (LISH) and as the principal investigator of the NASA Tournament Laboratory. Karim is known for his original scholarship on open source communities and innovation contests and has pioneered the use of field experiments to help solve innovation-related challenges while simultaneously generating rigorous research in partnership with organizations like NASA, Harvard Medical School, The Broad Institute, TopCoder, The Linux Foundation, and various private organizations. His digital transformation research investigates the role of analytics and AI in reshaping business and operating models. This research is complemented through his leadership as co-founder of the Harvard Business School Digital Initiative and as co-founder and co-chair of the Harvard Business Analytics Program, a university-wide online program transforming mid-career executives into data-savvy leaders. Karim has taught extensively in Harvard Business School’s MBA, executive, doctoral and online programs. He has co-developed new courses on Digital Innovation & Transformation, Digital Strategy and Innovation, and Laboratory to Market. He co-chairs the Harvard Business School executive program on Competing with Big Data and Business Analytics, various custom executive education offerings and developed the HarvardX online course on Technology Entrepreneurship.
"},{"name":"Elizabeth Langdon-Gray","affiliation":"Harvard Data Science Initiative (HDSI), Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Liz Langdon-Gray is the inaugural Executive Director of the Harvard Data Science Initiative (HDSI). In this role, she is responsible for implementing the academic vision of the Initiative, engaging closely with Harvard schools and the Office of the President and Provost. Liz oversees the management of the HDSI’s operations and programmatic priorities, including the postdoctoral fellows program, the HDSI Competitive Research Fund, seminars and workshops, and Harvard Data Science Review. She fosters research collaborations through outreach efforts both within the University and with external partners, and leads the Initiative’s corporate engagement activities, establishing the HDSI's Corporate Membership Program in 2018.
"},{"name":"Alex Liu","affiliation":"RMDS Lab, Pasadena, California, U.S.A.","bio":"Dr. Alex Liu is the founder and CEO of the RMDS Lab, a data and AI ecosystem service provider. From 2013 to 2019, Alex was one of the data science thought leaders and a distinguished data scientist at IBM where he served as a Chief Data Scientist for analytics services. Before joined IBM, Dr. Liu worked as a chief data scientist for a few companies including iSKY and Retention Science. Previously, Dr. Liu taught advanced quantitative methods to PhD candidates in the University of Southern California and the University of California at Irvine, while consulted for many well-known organizations such as the United Nations and Ingram Micro. Alex has a MS of Statistical Computing and a PhD of Sociology from Stanford University.
"},{"name":"Matthew Marolda","affiliation":"Applied Analytics Division, WarnerMedia, Boston, Massachusetts, U.S.A.","bio":"Matt Marolda runs WarnerMedia’s Applied Analytics division, which uses data and analytics to drive strategic marketing and creative decisions across its operating divisions, including Warner Bros., Turner, and HBO. He joined WarnerMedia via its acquisition of Legendary Entertainment’s Applied Analytics division. Matt founded Applied Analytics and served as Legendary’s Chief Analytics Officer. Prior to Legendary, Matt founded StratBridge LLC, which had two software solutions: “moneyball” player analysis software used by many organizations in the NFL, NBA, among others, and dynamic pricing and revenue analysis software used by many professional sports teams (acquired by Legendary in 2014, after he had joined Legendary).
"},{"name":"Maja Mataric","affiliation":"Robotics and Autonomous Systems Center (RASC) and Interaction Lab, Department of Computer Science, Neuroscience Graduate Program, and Department of Pediatrics, University of Southern California, Los Angeles, California, U.S.A.","bio":"Maja Mataric is the Chan Soon-Shiong Distinguished Professor of Computer Science, Neuroscience, and Pediatrics at the University of Southern California (USC), founding director of the USC Robotics and Autonomous Systems Center and her Interaction Lab. She received a PhD and MS from Massachusetts Institute of Technology and BS from the University of Kansas. She is Fellow of American Association for the Advancement of Science (AAAS), Institute of Electrical and Electronics Engineers (IEEE), and Association for the Advancement of Artificial Intelligence (AAAI), and recipient of the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring, Anita Borg Institute Women of Vision Award in Innovation, National Science Foundation (NSF) Career, Massachusetts Institute of Technology’s Technology Review (MIT TR35) Innovation, and Institute of Electrical and Electronics Engineers (IEEE) Robotics and Automation Society (RAS) Early Career Awards. A pioneer of the field of socially assistive robotics, her research enables robots to use human-robot interaction to aid in therapy, rehabilitation, training, and education and has been validated with children with autism, stroke survivors, Alzheimer's patients and other populations. She is also very involved in K–12 STEM outreach and mentoring of women and other under-represented groups.
"},{"name":"Brendan McCord","affiliation":"Cosmos Institute, Austin, Texas, U.S.A.","bio":"Brendan McCord is the founder and Chair of the Cosmos Institute and a key thinker at the intersection of AI and philosophy. In the private sector, Brendan was the founding CEO of two AI startups that were acquired for $400 million. In the public sector, Brendan was the principal founder of the first applied AI organization for the US Department of Defense and author of its first AI strategy. Brendan is a graduate of MIT and Harvard Business School and was a Visiting Fellow at St Catherine’s College at the University of Oxford. After MIT, he spent 610 days underwater on a submarine. He lives in Austin, TX, with his wife and two children.
"},{"name":"Antonietta Mira","affiliation":"Data Science Lab, Università della Svizzera Italiana, Lugano, Switzerland","bio":"Antonietta Mira is professor of statistics, founder and director of the Data Science Lab at Università della Svizzera Italiana (Lugano, Switzerland), where she served as the Vice-Dean in the Faculty of Economics (2013–2015). She is also part-time professor of statistics at Insubria University (Como, Italy), fellow of the International Society for Bayesian Analysis, of the Institute of Mathematical Statistics, and of the Istituto Lombardo Accademia di Scienze e Lettere. She has been visiting fellow at the Isaac Newton Institute for Mathematical Sciences at Cambridge University (2014 and 2016) and visiting professor at Université Paris-Dauphine, University of Western Australia, Queensland University of Technology, and University of Bristol. Antonietta is member of the board of the Swiss Office of Federal Statistics and of the Swiss Statistical Society Her current research focuses on data science and Bayesian computational statistics, with a clear interdisciplinary scope with applications to life sciences, social sciences and finance. She has won awards for excellence in both research and teaching. Antonietta is involved in public outreach both as an organizer of events and as a speaker and through the media is engaged in the strengthening of the culture of data science.
"},{"name":"O'Neil A.S. Outar","affiliation":"The Greater Boston Food Bank, Boston, Massachusetts, U.S.A.","bio":"O’Neil A.S. Outar is the Executive Vice President of Advancement for The Greater Boston Food Bank (GBFB), overseeing communications, fundraising, marketing, and public affairs.
Prior to joining GBFB, O’Neil spent 31 years in senior fundraising and engagement management roles at Rhode Island School of Design, the University of Pittsburgh, Harvard University, the University of Alberta, MIT, and Tufts University. Among his accomplishments, he has led or played leadership roles in campaigns ranging from $9 billion to $500 million and established the fundraising framework for signature programs in cancer research, energy, global partnerships, graduate education, and student leadership development.
He is a Special Adviser to The Guyana Foundation and an Advisor to Project Citizenship, and holds a Master of Arts in Urban and Environmental Public Policy and a Bachelor of Arts from Tufts University.
David C. Parkes is the Dean of the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and George F. Colony Professor of Computer Science at SEAS at Harvard University. He was also faculty co-lead for planning the expansion of SEAS into the Allston campus, and co-director of the Harvard/2u Business Analytics certificate program. Parkes was the Interim Co-Editor-In-Chief of Harvard Data Science Review from 2021 to 2023. He founded the EconCS research group, which pursues work in artificial intelligence, machine learning, and economics. His research focuses on the design of incentive-aligned, multi-agent systems, and envisions a future where economic transactions are mediated by automated software agents. Together with students and colleagues, Parkes has designed novel mechanisms for combinatorial auctions, planning, and information elicitation. These methods consider complex and competing interests in promoting optimal solutions, and find application throughout the digital economy, including to problems of pricing, matching, personalization and reputation. In recent work he has pioneered the use of machine learning for the automated design of revenue optimal auctions that have defied theoretical analysis. He teaches in both applied mathematics and computer science, including courses on machine learning, artificial intelligence, and optimization. Parkes served on the inaugural panel of the \"Stanford 100 Year Study on Artificial Intelligence,\" co-organized the 2016 Office of Science and Technology Policy (OSTP) Workshop on \"AI for Social Good,\" and served as chair of the Association for Computing Machinery (ACM) Special Interest Group on Electronic Commerce (2011–2016). Parkes is Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and recipient of the 2017 Association for Computing Machinery's Special Interest Group on Artificial Intelligence (ACM/SIGAI) Autonomous Agents Research Award, the National Science Foundation Career Award, the Alfred P. Sloan Fellowship, the Thouron Scholarship, and the Roslyn Abramson Award for Teaching. Parkes has degrees from the University of Oxford and the University of Pennsylvania, serves on several international scientific advisory boards, and has been a technical advisor to a number of start-ups.
"},{"name":"Nancy Potok","affiliation":"Center for Excellence in Public Leadership, George Washington University, Washington DC, U.S.A.; New York University, New York, New York, U.S.A.; Research Triangle Institute (RTI) International, Research Triangle Park, North Carolina, U.S.A. ","bio":"Nancy Potok is the former Chief Statistician of the United States, directing statistical and science policy at the U.S. Office of Management and Budget in the Executive Office of the President and leading the U.S. statistical community internationally. Dr. Potok has over 30 years of leadership experience in the public, non-profit, and private sectors. She served as Deputy Director and Chief Operating Officer, as well as CFO, of the U.S. Census Bureau and Deputy Under Secretary for Economic Affairs at the U.S. Department of Commerce; Senior Vice President for Economic, Labor, and Population Studies at NORC at the University of Chicago; and Chief Operating Officer at McManis & Monsalve Associates, a business analytics consulting firm. She was appointed as a Commissioner on the congressionally chartered Commission on Evidence-based Policy Making, which submitted its recommendations and report to Congress and the President in September 2017. Dr. Potok is an adjunct professor at the Trachtenberg School of Public Policy and Public Administration at The George Washington University. She is the recipient of the Presidential Rank Award, the Secretary of Commerce Gold Medal and Silver Medals for outstanding achievements, the Arthur S. Flemming Award, the Enterprise Risk Manager of the Year Award given by the Association for Federal Enterprise Risk Management, and the Distinguished Alumni Award from The George Washington University. Dr. Potok is an elected Fellow of the National Academy of Public Administration (NAPA) and serves on the Board of Trustees for the Institute of Pure and Applied Mathematics at University of California, Los Angeles. She received her PhD in public policy and public administration at the George Washington University.
"},{"name":"Arvind Satyanarayan","affiliation":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, U.S.A.","bio":"Website
MIT Visualization Group Website
Arvind Satyanarayan is an NBX Career Development Assistant Professor of Computer Science in the Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science (EECS) and a member of MIT Computer Science & Artificial Intelligence Laboratory (CSAIL). He leads the MIT Visualization Group which uses data visualization as a petri dish to study intelligence augmentation (IA), or how software systems help amplify our cognition and creativity while respecting our agency. Visualization toolkits and systems he has helped develop have won awards at premier academic venues, and have been broadly adopted within the data science community (including Jupyter and Observable) as well as industry (including at Apple, Google, and Microsoft). Arvind earned his BS in Computer Science at University of California, San Diego, his MS and PhD in Computer Science at Stanford University, and spent a year at Google Brain as a Postdoctoral Research Scientist. He is also a co-editor of Distill.pub, a new interactive academic journal for machine learning research devoted to clear, broadly accessible communication and non-traditional contribution types.
"},{"name":"Katie Shah","affiliation":"Harvard Graduate School of Education Dean’s Council and Dean’s Council for Harvard’s Graduate School of Arts and Sciences, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Katherine (Katie) Shah is a magna cum laude graduate in math and philosophy from Duke University, she has an MBA from Stanford, attended the Graduate School of Arts and Sciences at Harvard, and studied for an MFA at Christie’s Education in London. For a dozen years Katie worked on special projects for the president of the Metropolitan Museum of Art. These projects included evaluating business opportunities, analyzing customer data, identifying both cost and revenue areas for margin improvement, and creating and editing presentations for the Board of Trustees. The last several years Katie has been raising her four kids full time and volunteering with their schools and other organizations. She has been a member of the Harvard Graduate School of Education Dean’s Council as well as the Dean’s Council for Harvard’s Graduate School of Arts and Sciences. She is now beginning to look at some consulting projects as two of her kids are in college. She and her husband, Dhiren Shah, and their children live in New York City.
"},{"name":"Lara S. Schmidt","affiliation":"Sargent Aerospace & Defense, Tucson, Arizona, U.S.A.","bio":"Lara Schmidt is Principal Director of Strategic and Global Awareness at the Aerospace Corporation. In this role, she leads a portfolio of technical intelligence analysis activities providing international context to U.S. national security space program planning, acquisition, operations, and policy communities. Prior to joining Aerospace, Lara served in various analytic leadership positions at the RAND Corporation, focusing on national security issues including: cybersecurity; cyber effects estimation; integration of cyber operations into campaign plans; threats to military missions and the associated risk to the warfighter; target development practices; approaches to resilient space operations; special operations and irregular warfare; and formalizing data collection to support planning for cyber operations. She recently completed a four-year Secretary of Defense appointed term on the Air Force Scientific Advisory Board where she chaired both the Innovative Space Applications panel and the Science and Technology Reviews. Lara has held leadership positions with the American Statistical Association, including serving as the Chair of the Section on Statistics in Defense and National Security. Lara holds BS and MS degrees in mathematics and a PhD in mathematical statistics.
"},{"name":"Adrian Smith","affiliation":"The Alan Turing Institute, London, England, U.K.","bio":"Adrian Smith joined The Alan Turing Institute in September 2018. He was previously University of London's Vice-Chancellor since 2012 and was Director General, Knowledge and Innovation in BIS (now BEIS) between 2008-2012. He has worked with the UK Higher Education Funding and Research Councils and was appointed Deputy Chair of the UK Statistics Authority from September 2012. In 2014, he was appointed Chair of the Board of Diamond and in 2018, a board member of the UK Atomic Energy Authority. He was previously President of the Royal Statistical Society and became a Fellow of the Royal Society in 2001 for his contributions to statistics. In 2017, on behalf of Her Majesty's Treasury and the UK Department for Education, he published a 16–18 MathsEducation Review. In 2006 he completed a report for the UK Home Secretary on the issue of public trust in Crime Statistics. He received a knighthood in 2011.
"},{"name":"Alfred Spector","affiliation":"Department of Electrical Engineering and Computer Science , MIT, Cambridge, Massachusetts, U.S.A.; Blackstone","bio":"Dr. Alfred Spector is a Visiting Scholar at MIT and a Senior Advisor at Blackstone. His career has led him from innovation in large scale, networked computing systems to broad engineering and research leadership. Recently, he co-authored a Cambridge University Press textbook, “Data Science in Context: Foundations, Challenges, Opportunities.” (See www.datascienceincontext.com)
Previously, Dr. Spector was CTO and Head of Engineering at Two Sigma Investments. Before that, he spent eight years as VP of Research and Special Initiatives at Google, and he held various senior-level positions at IBM, including as global VP of Services and Software Research and global CTO of IBM’s Software Business. Earlier in his career, he founded Transarc Corporation, a pioneer in distributed transaction processing and wide-area file systems, and he was a tenured professor at Carnegie Mellon University.
Spector was a Hertz Fellow at Stanford and is also a Fellow of both the ACM and the IEEE. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences. Dr. Spector won the 2001 IEEE Kanai Award for Distributed Computing and the 2016 ACM Software Systems Award. In 2018–2019, Dr. Spector lectured widely as a Phi Beta Kappa Scholar (for example, on the growing importance of computer science across all disciplines based on the evocative phrase, “CS+X”). He has been a member of the ACM Turing Award Committee and has done national service through chairing the NSF’s CISE Advisory Board and his membership on the Army and Defense Science Boards. Dr. Spector obtained a PhD in computer science from Stanford and a BA in applied math from Harvard.
"},{"name":"Dustin Tingley","affiliation":"Department of Government, Vice Provost for Advances in Learning Data Science and Technology Group, and Harvard Initiative on Learning and Teaching, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Dustin Tingley is Deputy Vice Provost for Advances in Learning, Faculty director for the Vice Provost for Advances in Learning Research Group (Harvard higher education data science group), and Faculty director for the Harvard Initiative on Learning and Teaching. He received a PhD in Politics from Princeton in 2010 and BA from the University of Rochester in 2001. His research interests include international relations, international political economy, statistical methodology, and experimental approaches to political science. His book on American foreign policy, Sailing the Water's Edge, was published in fall 2015, and was awarded the Gladys M. Kammerer Award for the best book published in the field of U.S. national policy. Recent projects include attitudes towards global climate technologies and policies, and the intersection of causal inference and machine learning methods for the social sciences.
"},{"name":"Gaurav Vazirani","affiliation":"LabXchange, Cambridge, Massachusetts, U.S.A.; Wireless Philosophy, Boston, Massachusetts, U.S.A.","bio":"Gaurav Vazirani serves as the managing director for LabXchange, an online science education platform aimed at facilitating the discovery and authentic experience of the scientific process. Gaurav is also the founder and executive director of Wireless Philosophy, an online platform aimed at introducing people to the practice of philosophy and critical thinking. Gaurav earned his Bachelors in Philosophy at Rutgers University and his MPhil from Yale University.
"},{"name":"Xuemao Wang","affiliation":"Northwestern University, Evanston, Illinois, U.S.A.","bio":"Xuemao Wang is the Dean of Libraries and the Charles Deering McCormick University Librarian for Northwestern University. In his role, Xuemao oversees and facilitates the vision and strategies, and strategic plan implementation for the University Libraries. Xuemao has more than 30+years of diverse library and information technologies, services, management, and leadership experiences, with a career that spans the public, academic, large library consortium, and international library worlds. He has held global leadership positions in the International Federation of Library Association (IFLA), American Library Association (ALA), Center for Research Libraries (CRL), Association of Research Libraries (ARL), HathiTrust, SPARC, and OhioLINK. He has been frequently invited to international conferences to speak on topics of digital scholarship, digital humanities, and library strategic planning. Prior to working at Northwestern, he served as the Vice Provost for Digital Scholarship, Dean and University Librarian of the University of Cincinnati. He has held leadership positions at Emory University, Johns Hopkins University, the Metropolitan New York Library Council, Queens Borough Public library and worked as an academic librarian in China in his early career.
"},{"name":"Ronald L. Wasserstein","affiliation":"American Statistical Association (ASA), Alexandria, Virginia, U.S.A.","bio":"Ron Wasserstein has served as the executive director of the American Statistical Association (ASA) since August 2007. In this role, he provides executive leadership and management for the association and is responsible for ensuring that the ASA fulfills its mission to promote the practice and profession of statistics. Wasserstein joined the association in 1983 and has been active as a volunteer and held leadership positions in multiple chapters and sections. Prior to joining the ASA, Wasserstein was a mathematics and statistics department faculty member and administrator at Washburn University in Topeka, Kansas, from 1984–2007. During his last seven years at the school, he served as the university’s vice president for academic affairs. Wasserstein is a Fellow of the ASA and American Association for the Advancement of Science. He was presented the John Ritchie Alumni Award and Muriel Clarke Student Life Award from Washburn University and the Manning Distinguished Service Award from the North American Association of Summer Schools.
"},{"name":"Herbert Winokur Jr.","affiliation":"Capricorn Holdings, Inc., Greenwich, Connecticut, U.S.A.","bio":"Herbert S. \"Pug\" Winokur, Jr. has been Chairman and CEO of Capricorn Holdings, Inc., since 1987, and the managing partner of partnerships which invest in, or acquire, companies with the potential for long-term capital appreciation. He currently is an investor in a number of growth equity companies in the energy, defense and technology sectors. Over his career, he held leadership positions at Penn Central Corporation and served three years as a U.S. Army officer in the Office of the Secretary of Defense. Winokur is a co-Founder and former Chairman of ICF Inc., a management consulting firm specializing in policy planning for senior government and business officials, a member of the Council of Foreign Relations, a board member of Ithaka Harbors, Inc. and of Squash Haven (which he co-founded). He holds a PhD from Harvard in Decision and Control Theory and served on the Harvard Corporation.
"},{"name":"Marjorie Yang","affiliation":"Esquel Group, Wanchai, Hong Kong, China","bio":"Marjorie Yang is the Chairman of Esquel Group, a leading Hong Kong-based textile and apparel manufacturer with operations throughout the world. Marjorie serves as Chairman of the Seoul International Business Advisory Council (SIBAC). She is also a Hong Kong, China representative to Asia-Pacific Economic Cooperation (APEC) Business Advisory Council (ABAC), and a member of Chief Executive’s Council of Advisers on Innovation and Strategic Development. Marjorie is the Co-chairman of the advisory board of Computer Science and Artificial Intelligence Lab (CSAIL) at Massachusetts Institute of Technology (MIT) and the Chairperson of the Steering Committee of CoolThink@JC. She also serves as a member of various advisory boards including Harvard University, Harvard Business School, MIT Work of the Future, MIT Sloan, Tsinghua University’s School of Economics & Management and served as Council Chariman of the Hong Kong Polytechnic University. Marjorie is an Independent Non-executive Director for The Hong Kong and Shanghai Banking Corporation Limited (Asia Pacific).
"},{"name":"Margo Seltzer","affiliation":"Canada 150 Research Chairs Program, Government of Canada, Canada; Computer Systems, University of British Columbia, Vancouver, British Columbia, Canada","bio":"Margo Seltzer is a Canada 150 Research Chair and Cheriton Family Chair in Computer Systems at the University of British Columbia. Her research interests are in systems, construed quite broadly: systems for capturing and accessing provenance, file systems, databases, transaction processing systems, storage and analysis of graph-structured data, new architectures for parallelizing execution, and systems that apply technology to problems in healthcare. She is the author of several widely-used software packages including database and transaction libraries and the 4.4BSD log-structured file system. Dr. Seltzer was a founder and CTO of Sleepycat Software, the makers of Berkeley DB, and is now an Architect at Oracle Corporation. She serves on the Computer Science and Telecommunications Board (CSTB) of the National Academies and the Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) study group. She also served on the Computing Research Association Board of Directors, the Computing Research Association (CRA) Computing Community Consortium, and was President of the USENIX Association. She is a Sloan Foundation Fellow in Computer Science, an Association for Computing Machinery (ACM) Fellow, a Bunting Fellow, and was the recipient of the 1996 Radcliffe Junior Faculty Fellowship. She is recognized as an outstanding teacher and mentor, having received the Phi Beta Kappa teaching award in 1996, the Abrahmson Teaching Award in 1999, and the Capers and Marion McDonald Award for Excellence in Mentoring and Advising in 2010. Dr. Seltzer received an AB degree in Applied Mathematics from Harvard/Radcliffe College and a PhD in Computer Science from the University of California, Berkeley.
"},{"name":"Reetu Kumra","affiliation":"Fidelity Investments, Canada","bio":"Reetu Kumra is a Director of Research and Portfolio Manager at Fidelity (Canada) Asset Management ULC. Reetu manages the Fidelity Advantage Bitcoin ETF™ and the Fidelity Advantage Ether ETF™. Prior to this role, Reetu was an equity research analyst covering a variety of sectors on the Toronto Stock Exchange including consumer staples, power & utilities, energy, insurance and real estate.
Prior to her role at Fidelity Investments, Reetu worked at Argent Financial Group as a Fixed Income Analyst. Prior to this role, she held roles at Capital One and TNS as a Statistician.
Reetu has her Hons BSc from the University of Toronto with a major in Statistics and Biology and a minor in Mathematics. She has an AM in Statistics from Harvard University. She also has an MBA from the University of Chicago Booth School of Business. Reetu is a Chartered Financial Analyst charterholder.
"},{"name":"Dessislava Pachamanova","affiliation":"Mathematics, Analytics, Science, and Technology Division, Babson College, Wellesley, Massachusetts, U.S.A.","bio":"Dessislava Pachamanova is Professor and Zwerling Family Endowed Term Chair at Babson College and Research Affiliate at the Massachusetts Institute of Technology, University of Rochester, and University of Massachusetts Medical School. Her interests are in the design of advanced analytical tools in robust optimization, simulation, predictive analytics, and natural language processing, as well as their applications to numerous fields, including financial risk management, healthcare policy and operations, marketing, business strategy, legal studies, and engineering. Dessislava holds an AB in Mathematics with a certificate in Public and International Affairs from Princeton University and a PhD in Operations Research from the Sloan School of Management at the Massachusetts Institute of Technology (MIT).
"},{"name":"Don St. Pierre","affiliation":"AdaptEdge, U.S.A.","bio":"As the co-founder and chairman of AdaptEdge, Don St. Pierre is pioneering new frontiers in the wine and spirits industry. This new venture is a testament to his profound influence over 34 years as an entrepreneur, shaping the distribution and sales channels within the sector. His crowning achievement, ASC Fine Wines, emerged under his leadership as the most esteemed foreign-owned wine import company in China, heralding a new era in a rapidly evolving market.
Don’s expertise extends beyond establishment to advancement; he has been instrumental in fostering strategic growth, innovation, and sustainability across markets at varying stages of development. His strategic contributions have been pivotal in the evolution of Vinfolio and Fine + Rare, culminating in a best in class global direct-to-consumer fine wine marketplace. In more recent ventures, Don has embraced the roles of advisor and investor with Vivino China, helping to steer its nascent journey, and with Vino-Joy, where his insights are fine-tuning the growth of an already thriving platform.
His dedication to industry progression is further evidenced by his contributions as an advisory board member to Vinexposium, as an entrepreneur-in-residence at Harvard Innovation Labs, and as an advisor to the Harvard Data Science Review. Acclaimed for his visionary leadership, Don has been knighted in the French Ordre du Merite Agricole, recognized by Decanter Magazine as one of the Top 50 Global Wine Industry Power Players, and honored by Wine Enthusiast as the “International Man of the Year” in 2011. Complementing his professional accolades is his personal joy as a father to three daughters and as husband to Monica Xu St. Pierre, his partner and co-founder at AdaptEdge.
"},{"name":"Yi Wang","affiliation":"Harvard Center Shanghai, Harvard University, Shanghai, China","bio":"Yi Wang is the Executive Director of Harvard Center Shanghai. He represents Harvard University in China and oversees University’s programs in China. He took on this role in July 2014.
Before joining Harvard, Yi was a Managing Directors at Goldman Sachs and a partner at McKinsey & Company.
Outside of his full-time work, Yi has devoted himself to education-related charity work. Yi is co-founder and Vice Chair of the Board of United World College (UWC) Changshu China, which opened in 2015. He is also a co-founder and board member of China World Academy in Changshu and Tsinglan School in Dongguan.
Yi graduated from Harvard College with a bachelor’s degree in Physics and Astrophysics in 1995. He studied at University of Oxford from 1997 to 1998 for his Masters degree.
"},{"name":"Cathy Huyghe","affiliation":"Enolytics, Atlanta, Georgia, U.S.A.","bio":"Cathy Huyghe is an entrepreneur, writer and mindfulness proponent. She is the co-founder of Enolytics, a technology-centric disruptor and provider of business analytics solutions for the wine industry. She writes about the business and politics of the wine industry for Forbes, and she also co-creates the content for A Balanced Glass, a community dedicated to wellness in the wine and spirits world. All three of those ventures have been recognized for their innovative contributions. She also founded the Harvard Alumni in Wine and Food (HAWF) Shared Interest Group, and in 2021 Huyghe was named one of the industry’s Most Inspiring People by the Wine Industry Network. In 2022 she was nominated as Innovator of the Year by Wine Enthusiast magazine. Huyghe’s most recent work involves thought leadership speaking engagements around the world, particularly on the subject of innovation and AI for the wine industry.
"},{"name":"Jen Cortellini","affiliation":"Independent Strategic Partner & Experience Architect ","bio":"Jen Cortellini is a strategic leader and brand builder with deep expertise across wine, hospitality, and experiential marketing. After years of building and scaling business verticals for legacy brands, Jen is now creating her own platform focused on clarity, execution, and meaningful impact.
Most recently, she served as Vice President of Wine Operations, Strategy, and Experiences at Brooklyn Sports & Entertainment, where she built and launched Brooklyn Wine Club, a new wine and hospitality vertical. Previously, Jen held senior roles at Wine Enthusiast Media, where she led high-impact events and business development initiatives that significantly expanded brand visibility and revenue. She also served as Managing Director for Sona Hills and Claude Philippe Wines, where she launched and grew new ventures in California’s Paso Robles region.
With a rare combination of strategic vision, operational depth, and creative instinct, Jen brings a high level of polish and follow-through to every project. Her new platform is a space for brands looking to execute with impact and turn bold ideas into meaningful results.
"},{"name":"John Moon","affiliation":"Morgan Stanley Energy Partners, U.S.A.","bio":"John Moon is a Managing Director and Head of Morgan Stanley Energy Partners (MSEP). He is based in New York and Houston. Mr. Moon served as a senior member of the Morgan Stanley Capital Partners (MSCP) team from 1998 to 2004 and then rejoined Morgan Stanley in 2008. Prior to his return to MSCP, Mr. Moon was a Managing Director of Riverstone Holdings where he served on the Investment Committee. Prior to Riverstone, Mr. Moon was a founding partner, Managing Director and member of the Management and Investment Committees of Metalmark Capital.
Mr. Moon is a member of the investment committee of numerous Morgan Stanley alternative investment funds. He is Chairman of the North Haven Energy Capital Fund and serves as a director of portfolio companies operating in a broad range of subsectors of the energy industry.
Prior to joining Morgan Stanley in 1998, Mr. Moon worked in the Investment Banking Division of Goldman Sachs in New York. Mr. Moon holds an AB in Economics and an AM and PhD in Business Economics from Harvard University. Mr. Moon is an adjunct professor at Columbia Business School.
"},{"name":"Jon McAuliffe","affiliation":"Voleon, Berkeley, California, U.S.A.; University of California Berkeley, Berkeley, California, U.S.A.","bio":"Jon McAuliffe is co-founder and Chief Investment Officer of Voleon, heading the firm's investment-strategy research and development. Jon is also Adjunct Professor of Statistics at the University of California, Berkeley. His research focuses on theory and methodology for large-scale data analysis.
Dr. McAuliffe has a substantial track record of successful innovation in applying statistical methods to real-life prediction problems, particularly in the financial markets. He was formerly a Vice President at D. E. Shaw & Co., where he researched, developed, and managed statistical arbitrage trading strategies. Prior to co-founding Voleon, Dr. McAuliffe built recommender systems at Amazon.com, real-time click prediction rules at search-keyword portfolio manager Efficient Frontier, and genotype-calling algorithms at microarray maker Affymetrix.
Dr. McAuliffe held a University of California Regents Fellowship from 2000 to 2004, and won the Berkeley Statistics Department's Erich Lehmann award for the outstanding dissertation in theoretical statistics. He is widely published and served on the editorial board of The Journal of Machine Learning Research. He has served on the senior program committee for NeurIPS and ICML, the flagship annual machine learning conferences. Dr. McAuliffe was chosen to be in \"The Bloomberg 50: The People Who Changed Global Business in 2019.\"
Dr. McAuliffe earned his PhD in Statistics from the University of California, Berkeley. He also holds an AB in Computer Science, with honors, and an SM in Applied Mathematics, both from Harvard University.
Dr. McAuliffe's academic CV can be found here.
"},{"name":"Ricky Sun","affiliation":"Bain Capital Life Sciences, Boston, Massachusetts, U.S.A.; Emalex Biosciences, Chicago, Illinois, U.S.A.; ADARx Pharmaceuticals, San Diego, California, U.S.A.; Tenacia Biotechnology, Shanghai, China; Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Dr. Sun joined Bain Capital Life Sciences in 2016 and is a Partner. He is currently a member of the Board of Directors of Emalex, Adarx, and Tenacia. Previously he was a board member of Savara (NASDAQ: SVRA), Arcutis (NASDAQ: ARQT), Annexon (NASDAQ: ANNX), Avistone, and served as board observers of Pharvaris (NASDAQ: PHVS) and Replimune (NASDAQ: REPL). He also served as a member of the trustees of the Boston Museum of Science. He also serves on the Harvard University Graduate School of Arts and Science Alumni Association Counsel.
Prior to joining Bain Capital, from 2013 to 2016, he was a Director of Corporate Development and Strategy at Biogen. Prior to Biogen, Dr. Sun served as a Vice President at BlackRock, as a member of the Fundamental Equity division of BlackRock's Alpha Strategies Group and senior analyst for BlackRock's Fundamental Large Cap Growth equity team, covering the health care sector. Prior to that, he was a senior healthcare analyst at Citadel and Alyeska Investment Group in Chicago and worked as a pharmaceuticals equity research analyst on Wall Street, spending time at Lehman Brothers and Morgan Stanley. Dr. Sun began his career as a senior scientist at Ironwood Pharmaceutical where he was involved in the discovery and development of the drug Linzess for irritable bowel syndrome.
Dr. Sun received a PhD degree in Chemistry and Chemical Biology from Harvard University and was an NIH post-doctoral fellow in Biological Chemistry & Molecular Pharmacology at Harvard Medical School. He also received an MBA from New York University Stern School of Business, where he was a Mildred Elperin Scholar. He graduated summa cum laude from Berea College with a BA in chemistry.
"},{"name":"Dirk Hofmann","affiliation":"DAIN Studios, Berlin, Germany","bio":"Dirk Hofmann is the co-founder of DAIN Studios and CEO of its German operations. He has nearly 20 years of experience in data and AI, having led strategy, transformation, and product development initiatives at companies including Siemens, Nokia, and Deutsche Telekom. At DAIN Studios, he focuses on helping industrial sector leaders harness the full potential of AI and data, from strategy and execution to building AI-powered services. He holds a Master’s degree in Media Science, Psychology, and Economics from Ludwig Maximilian University in Munich.
"},{"name":"Ulla Kruhse-Lehtonen","affiliation":"DAIN Studios, Helsinki, Finland","bio":"Ulla Kruhse-Lehtonen is the co-founder of DAIN Studios and CEO of its Finnish operations. She has extensive experience leading data, analytics, and AI functions at global companies including Nokia and Sanoma. At DAIN Studios, Ulla has shaped and implemented data and AI strategies across a wide range of industries. She holds a PhD in Economics from the Helsinki School of Economics (Aalto University).
"},{"name":"James Zou","affiliation":"Department of Biomedical Data Science, Stanford University, Stanford, California, U.S.A. ","bio":"James Zou is an associate professor of Biomedical Data Science, CS and EE at Stanford University. He works on developing cutting-edge AI for biomedical applications. His group developed many widely used innovations including EchoNet AI (FDA cleared for assessing cardiac function), Gradio (used by over a million developers), and SyntheMol (NY Times 2024 Good Tech). He has received the Overton Prize, Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, best paper awards at ICML and other AI conferences, and faculty awards from Google, Amazon, Adobe and Apple.
"},{"name":"Francine Berman","affiliation":"College of Information and Computer Sciences at University of Massachusetts Amherst, Amherst, Massachusetts, U.S.A., Berkman Klein Center for Internet and Society, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Francine Berman is the Edward P. Hamilton Distinguished Professor in Computer Science at Rensselaer Polytechnic Institute (RPI) and the Director of Public Interest Technology and the Stuart Rice Research Professor in the College of Information and Computer Sciences at University of Massachusetts Amherst. She is a Faculty Associate at the Berkman Klein Center for Internet and Society at Harvard University and was selected as the 2019–2020 Katherine Hampson Bessell Fellow at the Radcliffe Institute for Advanced Study at Harvard University. She is a Fellow of the American Association for the Advancement of Science (AAAS), Institute of Electrical and Electronics Engineers (IEEE), and Association for Computing Machinery (ACM). In 2009, Berman was the inaugural recipient of the ACM/IEEE-CS Ken Kennedy Award for \"influential leadership in the design, development, and deployment of national-scale cyberinfrastructure.\" In 2015, she was nominated by President Obama and confirmed by the U.S. Senate to become a member of the National Council on the Humanities. Berman is a founder of the Research Data Alliance, a community-driven international organization created to accelerate research data sharing. She has served in many leadership positions including as Director of the San Diego Supercomputer Center, co-Chair of the National Academies Board on Research Data and Information, and co-Chair of the US-UK Blue Ribbon Task Force for Sustainable Digital Preservation. Berman currently serves as a Trustee of the Sloan Foundation and the Anita Borg Institute. Her research interests include data cyberinfrastructure, stewardship, preservation and policy, and social and ethical impact of the Internet of Things.
"},{"name":"Julia Lane","affiliation":"Wagner Graduate School of Public Service, New York University, New York City, New York, U.S.A.","bio":"Julia Lane is Professor Emerita at NYU’s Wagner Graduate School of Public Service and formerly served as a senior advisor in the White House Office of the Federal CIO, supporting implementation of the Federal Data Strategy. She has served on national advisory bodies including the Advisory Committee on Data for Evidence Building, the National AI Research Resources Task Force, the Secretary of Labor’s Workforce Innovation Advisory Committee, and the National Science Foundation’s Advisory Committee on Cyberinfrastructure. She currently serves on the advisory boards of the UK Data Service, Te Pūnaha Matatini (New Zealand), BERD@NFDI (Germany), the Electronic Privacy Information Center, and the National Data Platform (US).
An elected fellow of the American Association for the Advancement of Science, the National Academy of Public Administration, the International Statistical Institute, and the American Statistical Association, Julia is the recipient of the 2004 Vladimir Chavrid award from the National Association of State Workforce Agencies as well the 2014 Julius Shiskin award, the 2025 Society of Labor Economists Prize for Contributions to Data and Measurement, the 2014 Roger Herriot award from the American Statistical Association, and the 2019 LEHD Founders award from the Census Bureau for establishing the LEHD program. She is also the recipient of the 2017 Warren E. Miller Award and the 2019 Distinguished Fellow award from the New Zealand Association of Economists.
She has initiated or co-founded many data-related initiatives, including the Longitudinal Employer-Household Dynamics program at the US Census Bureau, the NORC at the University of Chicago remote access data enclave, Patentsview at the USPTO, the Integrated Data Infrastructure at Statistics New Zealand, the STAR METRICS/UMETRICS program at the Institute for Research on Innovation and Science at the University of Michigan, and the Coleridge Initiative (including the Administrative Data Research Facility and the Applied Data Analytics training program). She is currently working with a number of scientific and statistical agencies on the Democratizing Data project. She has authored more than 80 scientific publications, coauthored or edited 13 books, and holds a PhD in Economics and an MA in Statistics.
"},{"name":"Maria Fasli","affiliation":"School of Engineering and Informatics, University of Sussex; Economic and Social Research Council's Business and Local Government Data Research Centre, University of Sussex, Brighton, England, UK","bio":"Maria Fasli is the Pro-Vice-Chancellor (Research and Innovation) at the University of Sussex and Director of the Economic and Social Research Council Business and Local Government Data Research Centre. Prior to this appointment, she was the Acting Vice-Chancellor of the University of Essex from 2024–2025 and the Executive Dean of the Faculty of Science and Health between 2019-2024. Prior to this was the founding Director of the Institute for Analytics and Data Science from 2014–2019. She was educated in Greece (BSc Informatics 1996) and obtained her PhD in computer science from the University of Essex (2000). She has held positions at the University of Essex since 1999 and was Head of School of Computer Science and Electronic Engineering (CSEE) between 2009–2014. In 2005, she was awarded a National Teaching Fellowship by the Higher Education Academy in the United Kingdom for her novel approaches to education and supporting the student experience. In 2016, she was awarded the United Nations Educational, Scientific and Cultural Organization (UNESCO) Chair in Analytics and Data Science. Her research interests lie in artificial intelligence techniques for complex systems and analyzing and modelling structured/unstructured data. Her research has been funded by Research Councils and other organizations (over £10M to date) and she has worked with a range of companies in data analytics and artificial intelligence related projects. She has published over 130 papers in the field of AI and data science and has delivered keynote talks at international conferences and other events.
"},{"name":"Greg Lewis","affiliation":"Amazon, Cambridge, Massachusetts, U.S.A.","bio":"Greg Lewis is a senior principal economist at Amazon. He received his bachelor’s in economics and statistics from the University of the Witwatersrand and his PhD in economics from the University of Michigan. Prior to joining Amazon, he was associate professor of economics at Harvard and a senior principal researcher at Microsoft, where he was a co-lead on the Automated Learning and Intelligence for Causation and Economics (ALICE) project, an effort to develop economic AI.
His main areas of expertise are industrial organization, market design, applied econometrics and machine learning. His research spans a range of industries—online retailing, online advertising, procurement, electricity, education—and has been published in top economics and management journals and computer science conferences.
"},{"name":"Ryan Adams","affiliation":"Department of Computer Science, Princeton University, Princeton, New Jersey, U.S.A.","bio":"Ryan Adams is a machine learning researcher and Professor of Computer Science at Princeton University. Ryan completed his PhD in physics under David MacKay at the University of Cambridge, where he was a Gates Cambridge Scholar and a member of St. John's College. Following his PhD Ryan spent two years as a Junior Research Fellow at the University of Toronto as a part of the Canadian Institute for Advanced Research. From 2011–2016, he was an Assistant Professor at Harvard University in the School of Engineering and Applied Sciences. In 2015, Ryan sold the company he co-founded, Whetlab, to Twitter and he spent three years in industry at Twitter and Google before joining the faculty at Princeton in 2018. Ryan has won paper awards at the International Conference on Machine Learning (ICML), Conference on Uncertainty in Artificial Intelligence (UAI), and International Conference on Artificial Intelligence and Statistics (AISTATS), received the Defense Advanced Research Projects Agency (DARPA) Young Faculty Award and the Alfred P. Sloan Fellowship. He also co-hosted the popular Talking Machines podcast.
"},{"name":"Dave Wang","affiliation":"Wall Street Prompt, New York, New York, U.S.A.","bio":"Dave Wang is the CEO and co-founder of Wall Street Prompt, which delivers training for investment funds and banking teams on AI-native investing workflows. The platform complements these programs with self-paced courses and tooling designed to help investors move from theory to implementation.
Dave has worked with firms and professionals at top finance shops around the world including JPMorgan, Goldman Sachs, Millennium, and more for AI upskilling. He applies his AI techniques to his own book as an investor, and is an AI Research Fellow at Harvard in his free time. Previously, he founded and sold 99 Capital, led crypto investing at SoftBank’s Latin America Fund ($1b+ book), and worked in M&A at Morgan Stanley; he is also an AI Research Fellow at Harvard.
Dave earned his BA in Statistics, cum laude, from Harvard.
"},{"name":"Sean Ryan","affiliation":"Kearney, Chicago, Illinois, U.S.A.","bio":"Sean is a partner at Kearney where he has held multiple leadership roles: Americas Leadership Team, Global CPG Sector Team lead, Global Partner Selection Committee, Americas lead: Strategy, Marketing and Sales Practice. He has thirty years of consulting experience working and living in the Americas, Europe and Asia.
Sean’s client work focuses on growth and profitability transformation. Active work is focused on digital platform strategy, merger integration, route to market and data management. Sean serves global clients across multiple CPG sectors: Healthcare, Food & Beverage, Beverage Alcohol, Beauty and Personnel Care and Household.
He publishes and speaks at industry conferences. He has been quoted by the Wall Street Journal, The New York Times, CNN Money. He is recognized by the Chicago Council on Global Affairs as one of Chicago’s emerging leaders and serves on the Advisory Board of Johns Hopkins University School of Advanced International Studies. He is also the proud owner of The Bentwood Inn in Jackson, Wyoming where he is bringing AI Technology to the hospitality industry
"},{"name":"John M. Abowd","affiliation":"Department of Statistics and Data Science, Cornell University, Ithaca, New York, U.S.A.","bio":"John M. Abowd is the Edmund Ezra Day Professor Emeritus of Economics, Statistics and Data Science at Cornell University. He has also served as Chief Scientist, Associate Director for Research and Methodology, and Distinguished Senior Research Fellow at the U.S. Census Bureau. He was the founding Director of the Labor Dynamics Institute (LDI) at Cornell. He is a Fellow of the American Association for the Advancement of Science; past President (2014–2015) and Fellow of the Society of Labor Economists; past Chair (2013) of the Business and Economic Statistics Section and Fellow of the American Statistical Association; Elected Member of the International Statistical Institute; and Fellow of the Econometric Society. He is an editor of the Journal of Privacy and Confidentiality. His current research focuses on the creation, dissemination, privacy protection, and use of linked, longitudinal data on employees and employers.
"},{"name":"Shefaly Yogendra","affiliation":"Independent Board Director, England, UK","bio":"Shefaly Yogendra, PhD, is an experienced independent board director with a focus on stewardship in complexity, and governance to enable value creation. She has worked closely with Owner-led as well as Professional Manager-led organisations in their journey towards enhanced resilience with the lenses: technology innovation, risk and foresight, and governance. Shefaly is experienced in regulated sectors including financial services, energy, higher education and legal services. She is also an experienced committee chair having chaired audit & risk, nomination, remuneration, and ESG. She is a trusted boardroom adviser and a popular speaker and podcast guest on themes such as AI, investment, and future skills. Her book, \"Uncharted Spaces. Reset the Agenda. Reimagine the Boardroom,\" is out in April 2026.
"},{"name":"Ani Adhikari","affiliation":"Department of Statistics, University of California Berkeley, Berkeley, California, U.S.A.","bio":"Ani Adhikari is Teaching Professor in the Department of Statistics at the University of California, Berkeley. Her research interests are centered on the applications of statistics in medicine and the natural sciences, but pedagogy has always been her primary focus. Most recently she has been involved in the development of UC Berkeley's new Data Science major for undergraduates and in the development of several new data science classes. She is a developer and instructor in two Massive Open Online Courses (MOOCs) on the EdX platform, including an introduction to data science. Adhikari is the recipient of the Distinguished Teaching Award at Berkeley and the Dean's Award for Distinguished Teaching at Stanford University. She received her undergraduate degree from the Indian Statistical Institute and her PhD in Statistics from UC Berkeley.
"},{"name":"Sach Mukherjee","affiliation":"Medical Research Council (MRC) Biostatistics Unit, University of Cambridge, Cambridge, England, U.K.; Departments of Statistics and Machine Learning, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany","bio":"Sach Mukherjee is Director of Research in Machine Learning for Biomedicine at the Medical Research Council (MRC) Biostatistics Unit, University of Cambridge and Head of Statistics and Machine Learning at the German Center for Neurodegenerative Diseases (DZNE), one of Germany's major biomedical institutions. He earned a DPhil in machine learning at Oxford, was a postdoctoral fellow in statistics at the University of California, Berkeley and has previously held faculty positions in Warwick and Amsterdam. He is a principal investigator in several consortia at the intersection of machine learning and medicine and has served in leadership and advisory roles spanning statistics, machine learning and the biomedical sciences. His research centres on methods for large-scale biomedical data, with specific interests in high-dimensional models, data heterogeneity, networks and causality. He has been a Fulbright Fellow and a recipient of the Wolfson Research Merit Award of the UK Royal Society.
"},{"name":"Susan R. Lin","affiliation":"VinoConcerto; Belmont Wine Exchange; Asian Wine Association of America, San Francisco, California, U.S.A.","bio":"Susan R. Lin is a Master of Wine and holds a Master of Fine Arts in Classical Piano and Musicology. She creates memorable experiences through music and wine. Her research on the interaction of music and wine has been featured in Decanter, Food & Wine, GuildSomm, and other publications.
Susan is also the Head of Wine for Belmont Wine Exchange, a San Francisco-based boutique fine wine retailer, providing personalized concierge services for clients globally. Her passion for championing diversity in wine led her to found the Asian Wine Association of America, where she serves as President. She judges international wine competitions and performs music for audiences worldwide on the piano.
See Susan’s activities @SusanRLin on Instagram, LinkedIn, and YouTube.
"},{"name":"Minsoo Pak","affiliation":"EY, U.S.A.","bio":"Minsoo Pak is the EY Americas Leader for Innovation Realized. Over more than 25 years he has helped organizations reimagine their business through transformation, innovation and emerging tech/AI, combining creativity, experience design and measurable results. As a certified futurist, Minsoo continues to challenge convention by establishing experiences as the new business currency.
Previously, Minsoo held top creative posts at Sparks Grove, USWeb/CKS, Studiocom, Voltage Factory, Modem Media/DIGITAS and TribalDDB. At Ogilvy, he led their interactive practice when the Internet was in its infancy. Minsoo’s work has won numerous advertising/design accolades including the ADDY’s Best of Show, AIGA SEED Awards and CA Advertising Annual. At EY, his transformation client experience includes companies such as Royal Caribbean Cruise Lines, Toyota/Lexus NA, Caterpillar, Xcel Energy and Rheem.
Silver Stevie Award – Executive of the Year (2013)
BA Psychology from Emory University and graduated from The Portfolio Center with an Art Direction/Graphic Design portfolio.
He is also a featured speaker for AIGA, AMA, Georgia State University Robinson School of Business, Georgia Tech and Emory University-Goizueta Business School.
"},{"name":"Melisa Muraca","affiliation":"Sun Life, Montreal, Quebec, Canada","bio":"Melisa Muraca is Vice President and Global Chief Enterprise Architect at Sun Life, where she leads enterprise-wide architecture strategies aligned with business goals and Client needs.
With 25 years of experience, she brings a global perspective on transformation, change management and evolving tech trends. Before joining Sun Life in 2007, Melisa also held various IT roles within several Canadian and global organizations in the financial, food, aerospace and telecommunications industries.
Melisa leads Enterprise architecture strategy & execution bringing business perspectives, cross discipline viewpoints and thought leadership advocating for change and innovation. She led Sun Life’s Global Technology & Transformation Strategy, Digital Enterprise Centre of Excellence, created the Global Value Realisation Office and served as Vice President of Technology Products for Sun Life U.S., driving product strategy and digital transformation execution. She drove the implementation of innovative technologies, including optical recognition and global mobile strategies, execution and best practices while also spearheading organizational execution best practices and development automation. Melisa is the recipient of the “LIMRA Rising Star” award in the Information Technology category that represents a group of Canadian technology leaders under 40 who have achieved significant accomplishments in their career. Melisa’s area of interest is also promoting women and girls in STEM careers through networking and educational events.
"},{"name":"Martha Minow","affiliation":"Harvard Graduate School of Education and Harvard Law School, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Martha Minow is the 300th Anniversary University Professor at Harvard University and for eight years served as the dean of Harvard Law School, where she has taught since 1981. An expert in human rights with a focus on members of racial and religious minorities and women, children, and persons with disabilities, she has also worked on transitional justice, and freedoms of speech and religion. In the fall of 2018, she was one of seven faculty members from four universities offering a course on Law for Algorithms. Minow serves as an advisory council member of the Massachusetts Institute of Technology (MIT) Media Lab and as a trustee of the MacArthur Foundation, and for two years, she was the acting director of Harvard’s Edmond J. Safra Center for Ethics.
"}]},{"title":"Early Career Board","members":[{"name":"Aleksandrina Goeva","affiliation":"Macosko Lab, Stanley Center for Psychiatric Research, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, Massachusetts, U.S.A.","bio":"Aleksandrina Goeva is a Postdoctoral Fellow in the Macosko Lab at the Stanley Center for Psychiatric Research within the Broad Institute of MIT (Massachusetts Institute of Technology) and Harvard. She received her PhD in Mathematics and Statistics at Boston University, where she worked with Henry Lam and Eric Kolaczyk on complexity penalized methods for structured and unstructured data. During her postdoc, Aleksandrina has developed a deep interest in complex biological systems, for which we typically have only an incomplete and noisy set of measurements, and for the impact that expanding our knowledge of basic biological mechanisms can have on improving human disease. Her current research is focused on ill-posed inverse problems in biology, where she combines domain expert knowledge with modeling approaches ranging from linear and non-linear matrix factorizations, to graph attention networks, applied to single-cell RNA-seq and spatial transcriptomics data to answer questions about the mechanisms of interaction between cells and the function of tissues in health and disease, driven by applications to neurodegenerative diseases. In addition to striving to gain useful biological insights, Aleksandrina also works on theoretical problems in mathematics and statistics that arise from her applied work. Aleksandrina has a deep passion for science education and communication directed by her desire to convey complex concepts in simple and accurate terms. She also serves at the Steering Committee of the Models, Inference & Algorithms initiative at the Broad Institute. Aleksandrina believes that any human endeavor can benefit from having different perspectives being represented and strives to promote diversity within her spheres of influence.
"},{"name":"Keyon Vafa","affiliation":"Harvard Data Science Initiative, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Keyon Vafa is a postdoctoral fellow at Harvard as part of the Harvard Data Science Initiative. His research develops machine learning methodology to uncover insights into human behavior in labor economics and political science, among other fields in the social sciences. He completed his PhD in computer science at Columbia University in 2023, where he was advised by David Blei. During his PhD, he was an NSF GRFP Fellow and Cheung-Kong Innovation Doctoral Fellow.
"},{"name":"Kelly Blumenthal","affiliation":"Office for Astronomy Outreach, International Astronomical Union, Tokyo, Japan","bio":"Kelly Blumenthal is currently a project scientist for the International Astronomical Union, in partnership with the Kavli Institute, working to develop evidence-based training for astronomers on strategic science communication. She earned her PhD in theoretical astrophysics from the Institute for Astronomy at the University of Hawai'i at Mānoa in 2019. Throughout her PhD, Kelly performed outreach and public engagement activities by way of a number of different programs, and developed a strong appreciation for communicating science to the public through accessible and equitable means. For this reason, she decided to pursue a masters degree in education policy and management from the Harvard Graduate School of Education, which she received in 2020. Kelly is also involved in a number of other projects, including teaching a course on cultural, ethno- and archaeoastronomy to an excellent group of high school students in Hawai'i. Kelly's entire body of work is dedicated to effective and equitable science communication, and she strives to understand the nuances that exist across cultures in pursuit of this goal.
"},{"name":"Ana Trisovic","affiliation":"Institute for Quantitative Social Science, Harvard University, Cambridge, Massachusetts, U.S.A.","bio":"Ana Trisovic is a Sloan postdoctoral scholar at the Institute for Quantitative Social Science (IQSS) at Harvard University. Her research focus is on computational reproducibility, data provenance, and data science. She investigates how the reuse of research data and code could be facilitated through automation, metadata, and encapsulation. In these projects, she collaborates with the Dataverse team. Previously, Ana Trisovic was a CLIR postdoctoral fellow at the University of Chicago, where she worked with the Energy Policy Institute (EPIC) and the Library. She completed her Ph.D. in computer science at the University of Cambridge in 2018 on the topic of \"Data preservation and reproducibility at the LHCb experiment at CERN\". While at CERN, she worked with the LHCb collaboration, CERN Open Data, and CERN Analysis Preservation groups. During her Ph.D., she was a scholar of the Muir Wood studentship of the Newnham College, CERN doctoral student program, and Google Anita Borg Memorial Scholarship.
"},{"name":"Sara Stoudt","affiliation":"Department of Mathematics, Bucknell University, Lewisburg, Pennsylvania, U.S.A.","bio":"Sara Stoudt is a statistician with research interests in ecology and the communication of statistics. Stoudt received her bachelor’s degree in mathematics and statistics from Smith College and a doctorate in statistics from the University of California, Berkeley. Her graduate work involved evaluating species distribution and abundance models under model mis-specification. While at Berkeley she was also a Berkeley Institute for Data Science Fellow and worked with Professor Deborah Nolan to teach statistical writing. Keep an eye out for their forthcoming book, Communicating with Data: The Art of Writing for Data Science. Follow her on Twitter: @sastoudt
"},{"name":"Javin Pombra","affiliation":"Harvard College, Cambridge, Massachusetts, U.S.A.","bio":"Javin is currently a student studying Computer Science and Applied Math at Harvard College. Javin’s main research interests include deep learning recommendation architecture and systems. Javin has explored this topic through the work he is doing with the Harvard Computer Architecture Lab and as a machine learning intern with live-streaming start-up Caffeine.tv. Javin is also interested in Bayesian Neural Networks and questions of fairness of modern machine learning algorithms. Javin’s past work includes internships in data science for financial institutions and major presidential campaigns. Javin has also previously worked as a machine learning consultant for a variety of organizations including large utilities and consumer technology companies. Javin is originally from the Bay Area and will be working in the McKinsey San Francisco office as an incoming intern in its Digital Practice.
"},{"name":"Shuang Lu Frost","affiliation":"Aarhus University, Aarhus, Denmark","bio":"Shuang Frost is an ethnographer of technology and innovation and is currently an assistant professor of Digital Innovation and Business Transformation in Aarhus University, Denmark. Before that, she worked as a postdoctoral researcher at Lloyd Greif Center for Entrepreneurial Studies at University of Southern California Marshall Business School. She recently completed her Ph.D. of social anthropology at Harvard University, with a secondary field in Science, Technology and Society (2020). Her dissertation “Moralizing Disruption: China’s Ride-Hailing Revolution” explores the emergence, contestation, and moralization of ride-hailing platforms in contemporary China. During 20 months of ethnographic fieldwork spanning six years, she immersed herself in communities of computer engineers, corporate managers, on-demand drivers, hackers, and labor contractors, exploring how different groups of actors participate in and make sense of the changes brought on by ride-hailing platforms. In addition, she has studied a wide range of communities such as taxi drivers in Hangzhou, beggars in Northwestern China, slum dwellers in Shanghai, and AI policymakers at global conferences. Through all of these projects, she explores how individuals make sense of economic and technological changes in contemporary societies, and how they navigate a shifting landscape of precarity and opportunities. Her recent publications include “Platforms as if People Mattered,” “Uber in China,” and “Devaluing Human Labor.” She is also committed to using her research findings to inform technological designs and public policies that could empower individuals in global technological transformations. She is currently serving as the China representative for global think tank The Future Society, working on artificial intelligence policy research and community building between academia, governments, and industries.
"},{"name":"Vinitra Swamy","affiliation":"Scholé AI, ML4ED Laboratory, EPFL, Switzerland","bio":"Vinitra Swamy is the co-founder and CEO of Scholé AI, an EPFL and UC Berkeley spinoff using the best of AI to help learners learn effectively. She earned her PhD in Computer Science from EPFL, advised by Professor Tanja Käser (ML for Education Lab) and Professor Martin Jaggi (ML and Optimization Lab), focusing on the research directions of explainable AI, multimodal AI, and AI for education. Prior to entering Switzerland, Vinitra graduated from UC Berkeley with both a Bachelor's and Master's in Computer Science at age 20, holding the record for the youngest M.S. graduate in the EECS department. She spent two years at Microsoft AI working on open source AI frameworks as a lead engineer for the Open Neural Network eXchange (ONNX), coordinating over 30 companies including Nvidia, Intel, Meta, Oracle, and Amazon towards a interoperability standard for neural networks. She has served as a lecturer for data science courses at UC Berkeley Division of Data Sciences and the University of Washington CSE Department, as well as presented her research to the Red Cross and WHO as part of the team working on (at the time) the best performing open source medical LLM. Vinitra has been recognized as a “Rising Star in Data Science” by Stanford, UC San Diego, and the University of Chicago, is a (three-time) recipient of the EPFL IC Distinguished Service award as well as the UC Berkeley EECS Award for Excellence in Teaching and Leadership, and is a winner of the largest international edtech competition (the Learning Engineering Tools Competition) for her team's work on Scholé.
"}]}],"about":[{"slug":"mission","title":"Mission and Scope","html":"As an open access platform of the Harvard Data Science Initiative, Harvard Data Science Review (HDSR) features foundational thinking, research milestones, educational innovations, and major applications, with a primary emphasis on reproducibility, replicability, and readability. We aim to publish content that helps define and shape data science as a scientifically rigorous and globally impactful multidisciplinary field based on the principled and purposed production, processing, parsing, and analysis of data. By uniting the strengths of a premier research journal, a cutting-edge educational publication, and a popular magazine, HDSR provides a crossroads at which fundamental data science research and education intersect directly with societally-important applications from industry, governments, NGOs, and others. By disseminating inspiring, informative, and intriguing articles and media materials, HDSR aspires to be a global forum on everything data science and data science for everyone.
HDSR prioritizes quality over quantity, with a primary emphasis on reproducibility, replicability, and readability; attracting readers via inspiring, informative, and intriguing articles; and authors via our multi-domain reader outreach platform, speedy review process, and flexibility in presentation styles. Creative presentations are especially encouraged. We leverage digital technologies and advances to emphasize multimedia dissemination, learning-oriented visualization, enhanced author-reader interactions, and a global presence. In particular, our editorial board, authors, and external referees are drawn globally with the intention to progressively increase coverage and diversity. Submissions are by invitation only after a screening process. HDSR’s publications will be in a variety of forms, including research articles, discussion papers, special columns, interviews, conversations, tutorials, short essays, personal stories, and letters to the editor. HDSR publishes quarterly in digital forms, and also in print for special issues and collections.
"},{"slug":"publish","title":"What We Publish","html":"Harvard Data Science Review is committed to publishing top-quality content and conducting top-quality review of all submitted work (including all column articles). Our editorial board reflects a wide range of areas of expertise to facilitate our mission of publishing everything data science, and data science for everyone.
HDSR publishes four regular issues per year, plus special issues, on the digital platform. We operate as an open access platform and are the inaugural publication of the Harvard Data Science Initiative.
Our platform publishes a wide range of content, including full articles, perspectives, columns, interviews, tutorials, pedagogical materials, and more, as highlighted below.
The complex data science ecosystem requires wide-ranging discussions and in-depth introspection from all the varieties of its inhabitants in order to ensure its harmonious evolution. Panorama content aims to provide a global forum for broad overviews, deep reflections, inspiring visions, insightful blueprints, and informative debates stimulated by fresh perspectives from leaders and builders from all walks of data science. The forum aims to intrigue and engage experts and the public alike about the evolution of data science and its consequences, both positive and negative, in a thought-provoking and hype-resisting manner.
The main source of nourishment to the ever-evolving data science ecosystem comes from the direct and broad impact it has on the world. Cornucopia content showcases impactful applications and innovative implementations of data science theory and methods to solve problems of importance to human society and nature, and to address issues of intellectual and general interest. It prioritizes articles where the knowledge transfer processes themselves can be transferred to tackle other problems, and applications that help to further advance data science research. Articles in this section may feature data science in industry, government, NGOs, such as those pertaining to AI; personalized health and medical advances; nano technologies; biomedical engineering; business analytics; social policy; economic developments; smart cities; food supply; human rights; counter terrorism; international relationships; climate change and environmental protections; consumer products and retail; sports and entertainment industry; and voting and election systems.
Data science and artificial intelligence are advanced by human intelligence and diligence; harvesting and optimizing their benefits requires penetrating experts, a skilled workforce, and educated citizens. Stepping Stones content explores educational policies, infrastructures, contents, and innovations that can enhance and deepen data science education and training at all levels, from playroom to boardroom. It provides pedagogical strategies, methods, curricula, and lessons, as well as success stories that demonstrate the broad benefit of effective teaching and communicating data science.
Data science, as a foundation for knowledge discovery and augmentation in the digital age, is itself built upon fundamental research from many contributing disciplines. Milestones and Millstones content features foundational achievements, theoretical breakthroughs, and methodological marvels pertaining to data science, from ethical conundrums to quantum computing and everything in between. It aims to push the frontiers of research on some of the most perplexing problems, to generate new directions of inquiry, and to motivate inquisitive minds worldwide to challenge themselves to tackle similar provocations put forward by data science. Articles in this section tackle issues such as the tradeoff between computational and statistical efficiency; digital humanities; data privacy versus data utility; deep learning versus deep understanding; learning causality from massive online data; data curation and provenance; information governance; FAIR (findable, accessible, interoperable, reusable) data; algorithm fairness and accountability; data flows and markets; and research reproducibility, replicability and triangulation.
The power of data science in the business realm is tremendous, but the hype and complexity surrounding it can be overwhelming. The "Active Industrial Learning" column focuses on applications of data science to businesses, with an eye toward practical considerations that can make data science initiatives more likely to succeed. The aim is to translate business concerns into the language of data science, and vice versa, to empower data science leaders at companies of all sizes and data maturity levels.
Causal inference is a pivotal element in disciplines focused on unraveling the cause-effect relationships that underpin outcomes. The “Catalytic Causal Conversations” column provides approachable insights and varied perspectives on the evolving landscape of causal inference. Its primary objective is to clarify and explore the complexities of causal inference, thereby making it relatable and compelling for a broad audience, and to showcase cutting-edge methods and practical applications in this ever-evolving field.
Data science is a diverse field, and one which is changing rapidly in response to developments in theory and practical capabilities, as well as to the challenges of the great many domains in which it is applied. The “Diving into Data” column presents short articles describing ideas, concepts, methods, and tools used in data science. The overriding aim is that the articles should be enlightening, useful, and accessible, avoiding obscure technicalities.
Governments of all scales increasingly rely on data to form, implement, and evaluate policies, and to seek more effective ways to collect, utilize and disseminate data for better governance and societal engagements. The "Effective Policy Learning" column features engaging articles that highlight current developments in data science as well as efforts made in government sector around the world to uncover insights for evidence-based policy making. The aim is to equip policy makers with better understanding of the power and perils of data science, and to inspire data scientists to consider the public sector as a vital place to employ their skills and to maximize their impact.
Data science and AI raise countless big questions about the goals, structure, functions, methods, meaning and social roles of research. The “Meta Data Science” column presents short articles that discuss philosophical issues around the practice and theory of data science—ranging from ethical to epistemological and metaphysical questions. It aims to promote philosophy-minded data science and data-science-minded philosophy, and to bring philosophical debate around AI, statistics, and all things about data to a wider audience.
Data science is one of the fastest growing areas of employment in virtually all sectors, demanding many more qualified young talents than traditional training can supply. The “Minding the Future” column features pedagogical content for and about the younger audience, whether by age or experience, with a specific focus on students in their preteen and teen years via engaging their teachers and parents. It aims to inspire young minds to try out their first substantial data science-oriented studies, by computer simulations, classroom activities, or individual explorations.
To fully understand the practice and theory of modern data science, we must understand why the field developed as it did, with historical roots in mathematics, philosophy, statistics, and technology. The "Mining the Past" column presents short articles that explore these diverse histories and asks how they might inform contemporary debates, perspectives, and goals. The aim is to provide enlightening and useful historical perspectives that contextualize the field, enrich technical discourse, and enhance the public understanding of data science.
Data science has applicability that goes well beyond technological and academic domains. The "Recreations in Randomness" column presents short articles that demonstrate the scope, power and relevance of data science to the leisurely activities in our lives. The aim is to present explorations of various recreational activities, pastimes, hobbies, and games and sports, to highlight the surprising diversity of data science, and to engage the general public to better appreciate the value (and enjoyment) of proper reasoning under uncertainty.
Transparent and credible research relies not only on computational reproducibility or availability of materials, but on a multitude of individual and disciplinary conditions, infrastructure, and regulations, in order to enhance scientific replicability and ultimately the trust in (data) science. Many scientists don't have the time to explore all of these topics. The “Reinforcing Reproducibility and Replicability” column will explore topics in this space via short articles, enriching the discussion within and across disciplines, and highlighting sometimes neglected topics.
Data science as an ecosystem relies on effective and timely communications among all its inhabitants to advance and sustain its healthy evolution. Bits and Bites collects timely reflections, enlightening stories, Letters to the Editor, and other succinct writings that can enhance such communications. The aim is to facilitate communication between authors of varied backgrounds or interests with the broader data science community via enticing topics and newsworthy reports, augmenting readers’ appreciation of the kaleidoscopic nature of data science activities and schools of thoughts.
"},{"slug":"peerreview","title":"Peer Review","html":"All proposals and manuscripts submitted to Harvard Data Science Review for regular and special issues are entered into our online submission system, Editorial Manager (EM). Upon receipt, all submissions are initially reviewed by the Editor-in-Chief (EIC) and other members of the Editorial Board to assess whether the article is within scope of the journal, adheres to the journal’s publishing and ethics guidelines, and is of sufficient quality to merit external peer review. If an article is found not to meet these criteria, it will be editorially rejected. A decision letter is sent to the corresponding author stating the reasons for the rejection.
If the criteria are met, the article will be assigned to a Handling Editor who then sends the article to at least two, and possibly more, independent reviewers. Manuscripts are reviewed in single-blind fashion, with authors’ names and affiliations known to the reviewers and the reviewers’ identities kept confidential. Reviewers are selected based on their expertise in the subject matter of the article. Upon invitation, reviewers are asked to comment on the technical merits of the work, as well as its significance and impact. Reviewers are asked to decline the assignment if they have any conflicts of interest that may interfere with an objective evaluation of the manuscript. Upon submitting their reviews, reviewers are asked to declare any possible conflict and can leave confidential comments to the editor, as well as comments that will be transmitted to the authors.
The Handling Editor reviews the reports and makes a decision which is transmitted to the EIC for approval. If the decision is “Reject” or “Reject and Resubmit,” a decision letter is sent to the corresponding author that states the reason for rejection and appends the reviews. If the decision is to revise (“Major Revision” or “Minor Revision”), the reviews are appended to the decision letter and the editors may provide additional comments. Once a revised manuscript is received, it will again be examined by the EIC and/or Handling Editor and then sent to all or a subset of the original reviewers if determined necessary by the Editor(s). Once reviews are received, the EIC/Handling Editor again makes a decision which may be to Reject, Revise, or a “Provisional Accept.” In the case the decision is “Revise,” the manuscript is returned to authors for another round of revisions. In the case the decision is “Provisional Accept,” the manuscript is returned to the authors for final file checks and forms to be filled. The journal office reviews the final files submitted for compliance with all publication and formatting instructions. The EIC/Handling Editor then makes a final decision to “Accept” the manuscript for publication.
"},{"slug":"ethics","title":"Ethics and Peer Review Statement","html":"Harvard Data Science Review (HDSR) adheres to the ethical guidelines for journal publications put forth by The Committee on Publication Ethics (COPE). This ethics statement is based on COPE’s Best Practice Guidelines for journal editors, peer reviewers and authors.
HDSR is committed to the highest standard of publication ethics and critical scholarly review and to supporting ethical research practices. Below, please find HDSR’s practices and policies for conducing ethical review and publication of submitted content.
Content submitted to HDSR remains confidential throughout the peer review process. Publication staff and reviewers alike do not share submitted content with anyone outside of those involved in the review process and relevant content generating process (e.g., discussions, media summaries) without the permission of the authors and of the Editor-in-Chief (EIC) or a Co-Editor as authorized by the EIC.
When an author or the institution of the author has a relationship, financial or otherwise, with individuals or organizations that could influence the author’s work inappropriately, a conflict of interest may exist. Perceptions of conflict of interest can be as important as actual conflicts of interest.
Financial relationships (such as employment, consultancies, stock ownership or options, honoraria, patents, and paid expert testimony) are the most easily identifiable conflicts of interest and the most likely to undermine the credibility of the journal, the authors, and of science itself. However, conflicts can occur for other reasons, such as personal relationships or adversarial rivalries. Authors should avoid entering into agreements with study sponsors, both for-profit and non-profit, that interfere with authors’ access to all of the study’s data or that interfere with their ability to analyze and interpret the data and to prepare and publish manuscripts independently when and where they choose.
Members of HDSR’s advisory board, editorial board, and staff do not receive preferential treatment on submitted content. If an affiliate of the journal is an author or a co-author on a submission, that submission must receive the same review process and fair and rigorous treatment as all other submissions to the journal.
Editors who share a direct work affiliation (e.g., work in the same department) with the author(s) of a given manuscript do not oversee review of those manuscripts to prevent conflicts of interest. Reviewers may not share a direct work affiliation with the author(s).
When reviewers receive their invitation to review, they must confirm that they have no conflict of interest. If reviewers feel that they cannot provide impartial feedback or that there is a potential conflict of interest, they should decline the invitation to review. If reviewers are not sure if they have a conflict of interest, they should contact the EIC directly.
Participants have a right to privacy that should not be infringed upon without their informed consent. Identifying information, including participants’ names, initials, or hospital numbers, should not be published in written descriptions, photographs, and pedigrees unless the information is essential for scientific purposes and the participant (or parent or guardian) gives written informed consent for publication. Informed consent for this purpose requires that a participant who is identifiable be shown the manuscript before it is to be published. Complete anonymity is difficult to achieve, however, and informed consent should be obtained if there is any doubt. Informed consent is required for many different reasons:
To include subjects/participants in a study
To publish identifying information in text and images
To publish a paper which includes details of the subject/participant
In the Methods section of the manuscript, authors must state the following verbatim, or after suitable modifications unique to the study: “Informed consent was obtained from all participants for being included in the study.”
Authors should also state whether the informed consent was written or oral. If informed consent was received orally, the author must include the following:
Why written consent could not be obtained
That the institutional review board or appropriate research committee(s) approved the use of the oral consent
How oral consent was documented
If informed consent was not obtained, authors are required to give an explanation why this was so.
The Consent Form for Publication in Harvard Data Science Review may be downloaded below.
All research involving human participants must have been approved by the authors’ Institutional Review Board (IRB) or by equivalent ethics committee(s), and must have been conducted according to the principles expressed in the Declaration of Helsinki. Authors should be able to submit, upon request, a statement from the IRB or ethics committee indicating approval of the research. We reserve the right to reject work that we believe has not been conducted to a high ethical standard, even when acceptance by the editorial process has been obtained. Submissions must be in accordance with the DoH, with the exception of clause 35 (study preregistration) which is optional
Subjects must be properly instructed and have indicated that they consent to participate by signing the appropriate informed consent paperwork.
Peer review must be conducted and logged through our submission management platform, Editorial Manager. Entering comments and (possibly confidential) recommendations in the system ensures that HDSR has a written record of the review process and proof of reviewer and editor participation.
Authors, reviewers, editors, and administrators are each responsible for managing their own tasks through Editorial Manager. Some tasks are handled exclusively through an account with administrative access, in which case one should contact the editorial office (datasciencereview@harvard.edu). However, administrators do not invite reviewers, enter review comments, nor make recommendations or decisions on the editor’s behalf. This policy is in place to avoid both potential errors or miscommunications, as well as any ethical concerns of having a go-between enter content that influences final decisions.
Comments from editors and reviewers appear separately in decision letters to authors. Comments from reviewers will be clearly labeled as reviewer feedback. Comments from editors will be clearly labeled as editorial feedback. Co-Editors will have a decision template email to work from, but their comments should be integrated in the body of the email and thus are also separated from Associate Editor and Reviewer comments. In this way, the journal is fully transparent with authors as to how many different individuals reviewed the submission (for example, Co-Editor, Associate Editor, Reviewer 1, Reviewer 2, Reviewer 3—letting authors know that five individuals have read and provided comments on their work prior to a decision being made).
Authorship. The corresponding author is responsible for the appropriateness and completeness of the authorship list, appropriate credit attribution, and agreement of all authors to the journal’s open access, ethical, and if applicable, data sharing policies (please see our Author Guidelines for more details). HDSR follows the guidelines set out by the Contributor Roles Taxonomy Project (CRediT) to assign author contributions. The corresponding author is responsible for declaring these contributions, which are required for all submissions where such declaration is meaningful.
Originality. All content published in HDSR represents original work in the sense that it does not overlap with existing peer-reviewed publications in substantive ways, as determined by HDSR’s review process and the information available to HDSR. While under consideration for publication, content cannot be submitted for publication elsewhere.
Plagiarism and Research Fraud. A determination of plagiarism or fabrication by HDSR will require contacting the corresponding author’s institution and possibly funding agencies, whenever permissible by relevant governing law. If plagiarism or fabrication is determined post-publication, HDSR will investigate potential courses of action, up to and including formal retraction of the article.
Retraction Statement. In the event that an article is determined to have clear evidence that the findings are unreliable, either as a result of misconduct or honest error, a Notice of Retraction will be attached to all versions of the article on the HDSR site. For full Retraction Guidelines, please visit the following COPE webpage: COPE Retraction Guidelines
Post-Publication Corrections. HDSR adheres to the COPE guidelines for post-publication corrections as outlined in the post-publication discussions and corrections section of the COPE website: https://publicationethics.org/postpublication
Editorial Expressions of Concern. In the event that a published article is found to contain materials that may raise questions about its integrity, but they do not necessarily warrant a full recreation (e.g., a shared dataset is not longer accessible), an Editorial Expressions of Concern will be attached to all versions of the article on the HDSR site to warn the readers about the nature of the concerns.
"},{"slug":"author-info","title":"Author Guidelines","html":"Harvard Data Science Review conducts manuscript review at two stages: proposals and invited manuscripts. HDSR will only review manuscripts by invitation, but all interested authors are invited to submit proposals for full papers.
For more information about submissions and publication policies, please see below.
"}],"guidelines":[{"slug":"guidelines-author","title":"Full Manuscript Guidelines","dek":"Information for authors whose proposals are invited for full manuscript review","html":"Harvard Data Science Review reviews full manuscripts by invitation only, following proposal screening. HDSR will only accept content submitted through our submission management system, Editorial Manager.
HDSR does not accept unsolicited submissions of full manuscripts. If authors submit full manuscripts without going through proposal review, the manuscript submission will be returned to their authors without review.
Article submissions must follow HDSR’s Formatting and Style Guidelines and include the following components, in this order:
Full title
Names, titles, and affiliations of all co-authors
Indicate corresponding author
List contributions of all participants utilizing the CRediT taxonomy
Abstract (approximately half a page, or 250 words)
Keywords (up to 6, capitalized only if proper nouns)
Media Summary (approximately one page, or 400 words; see below on requirements)
Full manuscript content
Figures, tables, photos, etc., including legends (optional)
Disclosure Statement
Acknowledgements (optional)
Appendices (optional)
Data Repository/Code (if applicable)
Supplementary Files (optional)
References
Authors may submit audio or video abstracts in addition to or in place of standard text abstracts. Authors should consult the Formatting and Style Guidelines for permissible file types.
The media summary is a plain-language highlight of the major contributions and their societal and scholarly impact. The purpose of Media Summary is to provide an overview of the article’s general importance for media and general readers. HDSR requires media summaries for all Cornucopia and Milestones & Millstones articles and may request them for articles in other categories.
For examples of media summaries, authors are encouraged to review the following HDSR articles:
Banks, D., Albert, L., Caulkins, J., Frühwirth-Schnatter, S., Greig, F., Raftery, A., & Thomas, D. (2020). A Conversation about COVID-19 with Economists, Sociologists, Statisticians, and Operations Researchers. Harvard Data Science Review. https://doi.org/10.1162/99608f92.7fa08812
Keller, S. A., Shipp, S. S., Schroeder, A. D., & Korkmaz, G. (2020). Doing Data Science: A Framework and Case Study. Harvard Data Science Review, 2(1). https://doi.org/10.1162/99608f92.2d83f7f5
Romano, Y., Barber, R. F., Sabatti, C., & Candès, E. (2020). With Malice Toward None: Assessing Uncertainty via Equalized Coverage. Harvard Data Science Review. https://doi.org/10.1162/99608f92.03f00592
The acknowledgements include the authors’ personal thanks and comments. Acknowledgments are strongly encouraged but optional.
The disclosure statement reports any financial support (such as funding, grants, or sponsorship) or potential conflicts of interest (such as place of employment and type of research or work conducted). This is required for all submissions. If the authors have nothing to disclose, text should read:
Author(s) has/have no financial or non-financial disclosures to share for this article.
Each author’s full name should be spelled out as it appears in the byline of the article.
Appendices appear as a section of the text and serve as additional information or reference. Supplementary files exist separately from the main article and are generally more extensive than appendices. When published, they will be posted with their own URL and/or DOI.
Our digital edition allows for multimodal submission content, such as video abstracts, online appendices, and supplementary files. Authors are encouraged to consider innovative and interactive supplements to their manuscript submissions.
Screened/invited manuscripts should be submitted exclusively to HDSR.
Manuscripts in review that do not contain mathematical symbols should be prepared in Word, in a single-column format. Manuscripts should be formatted using HDSR’s Word template.
Manuscripts in review with mathematical symbols should be prepared in LaTeX using the HDSR template available on Overleaf. Authors should also submit a PDF file together with the Overleaf project link. Please ensure that editorinchief_datasciencereview@harvard.edu is added to the Overleaf project with editor permissions. We strongly recommend including an access link to your project with your submission.
In addition to the required computer artifacts (e.g., data, code, notebook), authors may also choose to include tables, figures, or charts. For the review process, the only requirement for figures and illustrations is legibility.
For submissions with multiple illustrations and/or media components—such as interactive data visualizations, audio, or video—a specialized editor may be involved for review. These editors’ comments and requests for revision are not necessarily limited to illustrations or the media component of the submission.
While there is no maximum length for manuscripts, the most successful articles communicate their ideas concisely and engagingly, with selective use of tables and figures. Concise and engaging articles may also significantly reduce the review period.
Authors must include a Point-by-Point Response to Reviewer Comments when submitting revisions.
HDSR aims to respond to full manuscripts within 4 months of submission. Authors may inquire about the status of their proposals after this point.
Invited revisions should be made within 6 months of the invitation. Revisions sent after 6 months may be considered as new submission, and risk a delayed process.
Accepted manuscripts require production-ready files prior to publication, such as an editable file containing the full text of the manuscript (.docx, .tex) and separate image files. PDFs are not acceptable file types for text but may be included for reference for image placement or mathematical content.
If there are images or figures, authors should supply them as separate files. They must be high-resolution files.
Authors of accepted content must include a preview image for the issue page and social media sharing. Ideally, this image will come directly from the article itself, or otherwise be author-created. Some authors use royalty-free image sites as well. Authors are responsible for confirming that they have permission to use their preview images.
A full PDF file will be required for those authors who want to post their pre-copyedited final manuscripts as Just Accepted pieces.
Authors who are interested in publishing in HDSR may submit a proposal to be screened for invitation of a full manuscript. HDSR reserves the right to make adjustment to this and other guidelines at any time and without notice.
The editorial board reserves the right to impose specific requirements for individual articles, including modifying titles and abstracts, before acceptance.
HDSR is a single-blind journal. Co-Editors, Associate Editors, and Reviewers will know the authors’ identities, but authors will not know any of their editors’ or reviewers’ identities.
Manuscripts are assigned to a Co-Editor and/or Associate Editor who oversee peer review. HDSR conducts single-blind review, so comments will be shared with the authors labeled by role, not name (i.e. “Editor Comments”/ “Reviewer 1” / “Reviewer 2”)
The editorial board reserves the right to impose specific requirements for individual articles, including modifying titles and abstracts, before acceptance.
Authors must complete a publication agreement form prior to publication. The agreement guarantees the publication in the open-access digital form, and permits HDSR to publish the accepted article in other forms (e.g., in print for special issues or collections) solely at HDSR’s discretion. Publication beyond the digital version is not guaranteed.
HDSR is an open access journal that publishes content under a CC-BY 4.0 license.
Authors retain copyright and full publishing rights without restriction. Please see our author agreement here. Authors may post their articles to an institutional or other repository of their choice.
Authors of accepted content must include a preview image for the issue page and social media sharing. Ideally, this image will come directly from the article itself, or otherwise be author-created. Some authors use royalty-free image sites as well. Authors are responsible for confirming that they have permission to use their preview images.
The editorial board reserves the right to impose specific requirements for individual articles, including modifying titles and abstracts, before acceptance.
HDSR considers exclusive submissions only. However, HDSR will consider content that has been deposited in institutional archives or repositories such as arXiv.
Work that has been presented at conferences but has not been formally published is eligible.
HDSR will not consider content that another peer-reviewed publication has already published, unless HDSR requests for re-printing for special reasons (e.g., per agreement with an affiliated publication).
Written permission is required if authors include any content published in copyrighted material, such as figures and tables, or quoted text exceeding Fair Use parameters. It is the author’s responsibility to acquire permissions for copyrighted materials. Authors must provide proof of permissions before publication.
It is important to note that authors are responsible for acquiring permission to use data or media they did not create. Authors also must have written permission from patients, students, case studies, etc., if cases or data are referenced in their manuscripts. HDSR does not collect patient consent forms or comparable sensitive materials. When submitting to the publication, authors must confirm that they have collected the necessary permission and, should a conflict arise, and are expected to produce written proof of permission.
Accepted articles must undergo copyediting and proofreading in advance of publication. At the time of acceptance, authors must also sign HDSR’s author agreement form. Once authors submit their signed forms, HDSR may publish the Just Accepted version of the article, as well as its title and abstract.
The Just Accepted version of the manuscript is a watermarked PDF of the accepted, pre-copyedited article. The HDSR editorial office will add the watermark and, if applicable, convert the Word doc to PDF formatting. The DOI (digital object identifier) remains the same for the Just Accepted and final versions. If referencing the Just Accepted version, authors should indicate the version in their references, works cited, or bibliography.
"},{"slug":"wjotxfm3","title":"How to Submit","dek":"Information for authors on how to use our submission management platform","html":"HDSR does not accept unsolicited submissions of full manuscripts. If authors submit full manuscripts without going through proposal review, submissions will be returned to their authors.
HDSR will only accept manuscript proposals through Editorial Manager. We do not accept work submitted outside of Editorial Manager.
Full manuscript submissions must include a formal cover letter.
Prospective authors must create an account through our EM page. New users can set up an account by clicking Register Now.
Once your account is set up, go to the Author Main Menu and click Submit New Manuscript. This link leads to our submission page, where authors can fill in the requested submission information.
Authors may upload files either by clicking the Choose Files button or by dragging and dropping files into the upload space. Authors should designate the type of each file (cover letter, document, etc.).
When authors are finished entering information and uploading files, they should click Build my PDF, which will create the PDF review version. Authors must click View Submission before the submission can be processed; they may download the review PDF or choose to open it in the program of their choice (including web browsers). If the PDF preview is acceptable, authors should click Approve Submission to submit manuscripts to the HDSR editorial office.
Authors will receive an automatic e-mail confirming receipt of their submission.
The journal will notify authors via e-mail when a decision is made.
Aries’ official Editorial Manager Vimeo (includes walkthrough videos and tutorials)
Harvard Data Science Review welcomes authors to submit article proposals for screening. Interested authors should submit proposals through HDSR’s submission portal, Editorial Manager. Successful authors will be invited to submit a full submission, but the invitation provides no guarantee of any kind to the ultimate publication, which is determined solely by the review process.
HDSR does not accept unsolicited submissions of full manuscripts. If authors submit full manuscripts without invitation, these manuscripts will be returned to their authors without review.
No article processing charges are required to submit to HDSR.
Title
All authors, including affiliations, with corresponding author indicated
Up to 6 keywords
1-2 page short description or executive summary of the proposed article.
HDSR assigns a proposal to a screening editor, typically a board member of HDSR, but guest editors are possible when appropriate.
Editors may request a revised proposal before making final decisions on whether to invite. Rejections are final.
Proposals must be relevant to the scope of HDSR and must be of interest to the data science community. See our Mission and Scope and Content Guidelines for more information.
HDSR aims to respond to proposals within 4 to 6 weeks of submission. Authors may inquire about the status of their proposals after this point by emailing the Editorial Office at datasciencereview@harvard.edu.
Data are important products of the scientific enterprise and should be preserved for future uses. HDSR requires, as a condition for publication unless otherwise waived (e.g., for protecting data privacy), that all data necessary to replicate published results should be archived in a well-supported archival repository, such as the Harvard Dataverse repository (https://dataverse.harvard.edu/). HDSR maintains a Dataverse instance, where authors are required to deposit the data file, code file, and any supplementary materials in their deposit and provide the citation to the dataset, including a persistent identifier and repository name, at the time of formal article submission. The dataset in Dataverse will not be released until the article is published, and only the authors and reviewers (or anyone designated by HDSR as necessary for processing the review or publication of the article) will have access to the dataset before the article’s publication.
Data, code, and related files used to replicate the results in the manuscript can be uploaded in any format to the HDSR Dataverse instance. However, we recommend that tabular datasets be uploaded either as an R data frame, an SPSS file, or a CSV file. The Harvard Dataverse recognizes these formats as tabular data and automatically generates summary statistics and provides variable information, which allows users to better understand the data. Interactive demonstrations can also be uploaded to the HDSR Dataverse instance.
If code has been used in the study, then that code should be deposited with the data. It is highly recommended that, whenever possible, the code be open source. In those cases where sensitive data cannot be made available in the repository, exceptions may be granted at the discretion of the Editor-in-Chief or their designator.
By default, data deposited in the Harvard Dataverse will be given a Creative Commons 0 waiver, thereby allowing it to be reused for any purpose, with the expectation that the data will be cited when reused. If data has already been deposited in a repository other than the Harvard Dataverse, then the author is required to deposit the data into the HDSR Dataverse instance and add a link to the original dataset. Once a dataset is deposited in the Harvard Dataverse, the data citation must be listed in the article’s references.
If a dataset is larger than 1TB, please contact HDSR for guidance.
Guidelines updated July 2020
Columns and Discussion content is by invitation only. This content follows the general guidelines of the full manuscript, with the following distinctions.
You may obtain a manuscript template for column and discussions from our Templates for Authors page.
HDSR features several regular columns for targeted audiences (e.g., leaders in governments and industries) as well as for a general audience. The following are guidelines for writing a successful column article in HDSR.
Column articles are short, generally between 1500–2500 words (excluding references), corresponding roughly to 6–10 double-spaced pages. Figures and tables should be used sparingly, but if necessary additional illustrations and materials can be provided through hyperlinks to other online sources. Manuscripts should be submitted in Editorial Manager as Word files.
Technical detail should be avoided, though in rare cases where such detail is unavoidable it is important to provide enough detail to appreciate the details without belaboring the issue or creating a distraction.
A column article is intended to be mainly an engaging and succinct review of a topic or a demonstration of instructional materials (for educationally-oriented columns), not a presentation of original research or a case study. An ideal column article would explain the relevance (current or historical) of data science to a particular substantive area, or the application of data science to a substantive topic, as well as how to address specific questions or conduct education training in the realm of data science. Articles that discuss review different points of view or different ways of thinking about a data science topic may also be a good fit to an HDSR column.
Despite being reviews and demonstrations, column articles should attempt to have a specific focus rather than cover too broad an area. For example, a column article that describes all the major applications of data science to baseball would be too broad (such an overview article would be more suited for the Panorama section of HDSR), but a column that describes the development of data science methods to improve fielding in baseball using ball tracking data would be a far better fit to HDSR.
Although the intended audience for column articles is an educated but non-technical readership, HDSR column articles are scholarly pieces and should be written with a level of depth, insight, and thoroughness expected of a typical peer-reviewed article. It is important to provide sufficient context for the material with appropriate citations to supporting work. That is, column articles need to be engaging but not overly simplifying, and they are not meant to be opinion pieces or perspective articles (which are more suited for the Panorama section).
All column articles should be ready 3 months in advance of their scheduled release. HDSR currently publishes quarterly. Therefore, a column article scheduled for the July issue, for example, should be in its final form (other than copyediting) by the beginning of April.
Column articles will be circulated for peer review prior to publication with an expectation of likely revisions. Submitting a column article to HDSR, whether solicited or not, does not guarantee its publication.
Authors should be aware of several logistical issues that are specific to column articles:
Column articles should have a title and 5-6 keywords, but column editors will supply a short preface (Column Editor’s Note) to the article in place of an abstract. Bits and Bites articles may retain their abstracts at HDSR discretion, though HDSR reserves the right to implement an Editor-in-Chief’s Note in place of an abstract when deemed helpful to the reader, typically in cases of extraneous/non-traditional material such as Letters to the Editor or book reviews.
Sectioning column articles is encouraged. Section headers should be unnumbered, except for the column “Bits and Bites.”
Discussion articles and discussants are selected by the editors. Authors do not submit articles to be part of discussions. However, editors may choose author-submitted articles for discussions, in which case the editors will contact the authors directly, and will invite discussants. The authors will be provided with an opportunity to prepare a rejoinder.
Discussion articles follow the guidelines for full papers.
Discussion commentaries and original author rejoinders should follow the following points:
Use a unique title (instead of “Commentary on ‘Article Title’”)
Do not include an abstract or keywords
Do not number headings
Q: How technical should my article be?
A: HDSR publishes a wide range of content, so how technical an article is depends on which section it appears; see What we Publish.
Q: How long should my manuscript be?
A: HDSR does not have a firm manuscript length requirement. However, content must justify length. We encourage authors to write concisely and engagingly. Editors and/or peer reviewers may recommend that authors edit their page count in order to develop the most successful HDSR paper.
Generally speaking, content in the Panorama section and Columns (including Bits & Bites) averages 2000-5000 words, while content in the Cornucopia, Stepping Stones, and Milestones & Millstones sections averages 4000-12,000 words. These numbers are only for reference; submissions do not need to adhere to them to be accepted.
Q: What is a media summary?
A: A media summary is a plain-language summary of an article for general audiences and press releases. We include media summaries with our most technical content. Media summaries should be no more than 400 words, or approximately 1 single-spaced typed page.
Q: I already have a full paper to submit. Do I have to write a proposal?
A: Yes. All unsolicited content undergoes proposal screening with our editorial board prior to being invited for full manuscript submission and formal peer review.
Q: What is an ORCID ID? Do I need one to submit?
A: An ORCID ID is an identifying number that researchers may elect to use to organize their publications. More information is available at the ORCID website. When submitting to HDSR via Editorial Manager, the system prompts authors to include an ORCID ID if they have one. It is entirely optional for our publication.
Q: Do I have to have an account in Editorial Manager to submit?
A: The corresponding author must have an account to submit.
Q: Can I just e-mail you my paper?
A: No, we only accept submissions via Editorial Manager.
Q: Why are you only accepting submissions via Editorial Manager?
A: In using a formal submission management system, we ensure that no manuscripts are lost due to technical difficulties with e-mails. Editorial Manager provides a clear, organized dashboard for our staff to view all manuscripts and streamline tasks. The system also maintains a record of editorial and peer review tasks, upholding our mission to conduct thorough and proper peer review.
Q: Do I have to have a PubPub account to submit?
A: No, you do not need a PubPub account to submit. However, we do require that accepted authors create accounts. For more information, see our Publication Policies.
Q: How do I leave comments and annotate using my PubPub account?
A: PubPub has information on how to engage with interactive content here. HDSR encourages thoughtful commentary and discussion of our published content.
Q: I’m having an issue with my PubPub account/using or navigating the site.
A: For questions or concerns regarding our digital platform, please contact help@pubpub.org.
Q: What is a preview image? Why do I need one?
A: When an article is accepted for HDSR, we request that authors supply a preview image to use in our table of contents (for example, our COVID-19 special issue) or as an accompanying image on Twitter. Preview images create a more visually dynamic site but also add appeal when sharing article across social media. For articles that have images and figures, authors may elect to select one of their own pictures as a preview image. Preview images should be submitted as .jpeg or .png files and should be at least 1200 x 600 pixels.
Q: My article doesn’t have images. What do I use for my preview image?
A: Our authors have used trustworthy license-free photo sites such as https://pixabay.com/ or https://unsplash.com/.
"},{"slug":"qfvp0her","title":"LaTeX Guidelines","dek":"Information for authors submitting work with mathematical formulas (DO NOT use LaTeX otherwise)","html":"For authors working in LaTeX, HDSR requires the use of our journal template, available on Overleaf. When submitting to Editorial Manager, authors may upload files directly to the system as well as provide a link to their manuscript in Overleaf.
We request that authors only use LaTeX when working with mathematics. For non-mathematical articles, please refer to our Word doc guidelines (and template, here).
While downloading this template does not require an Overleaf account, we do use Overleaf to facilitate copyediting and production for accepted works to expedite the production process and simplify the process of making errata and addendum. Authors are encouraged to work in Overleaf, especially if invited to submit a revision after initial decision.
Authors working in LaTeX should create a single .tex file for the full manuscript and supply separate image files, if applicable. JPEG and PNG are preferred for online; please do not use PDFs for images.
Some code is not compatible with our site. For more details on LaTeX commands that do and don’t work, please refer to this document. HDSR recommends that authors check this list before submitting to avoid incompatible latex commands. If the authors run into trouble with the latex compiling that are not addressed in this document, they should report the problems to our editorial office.
Guidelines updated September 2020
For all submissions that do not include mathematics or formulae, please use our downloadable Word templates below.
For sections (including Panorama, Cornucopia, Stepping Stones, and Milestones and Millstones) and non-mathematical content for Special Issues, please use the following template.
For columns and discussions, please use the following template.
HDSR Column and Discussion Template.docx 93 KB
For mathematical content only, HDSR hosts our LaTeX template on Overleaf, which authors may access here. Please do not use LaTeX if your article does not contain mathematical content.
Please find below our publication agreement form. Please sign either by hand or by official digital signature; a typed name will not suffice.
HDSR Publication Agreement (fillable PDF).pdf 195 KB
HDSR Publication Agreement PDF.pdf 190 KB
HDSR Publication Agreement DOCX.DOCX 27 KB
"},{"slug":"garel84k","title":"Formula Guidelines","dek":"Information for formatting mathematics","html":"Please number your formulas by section using Arabic numbers.
Article numbers should be whole numbers (1, 2, 3), not decimals.
Please center equations and flush article numbers to the right:
Formulas should not exceed 75% of the page width. For longer formulas, please determine the best line breaks for equations and align across multiple lines.
Any formulas or equations referenced within the text must be numbered. Equations that authors do not reference should not be unnumbered.
To ensure that formulas will render properly on the digital platform, authors may check their work in KaTeX. Code that works in this site will render on PubPub.
Below are the formatting and style guidelines for authors submitting work to HDSR. All content accepted for publication also undergoes copyediting and proofreading prior to publishing.1
HDSR follows the guidelines in the most current edition of the APA Style, available from the American Psychological Association. As of right now, the most recent version is the seventh edition. Up-to-date APA guidelines are available on their official website, here: https://www.apastyle.org.
In addition to these formatting guidelines, please note the following exceptions and addenda.
Please use American English spellings and grammatical conventions.
We encourage active voice whenever possible.
ACTIVE Example: Data scientists achieve many things.
PASSIVE Example: Many things are achieved by data scientists.
When referring to “data science” or “data scientist,” no capitalization is necessary. If referring to a department or official title (i.e., “Harvard Data Science Initiative,” “Chief Data Scientist”), please capitalize accordingly.
Please put commas inside of quotation marks when quoting.
Double quotation marks should indicate direct quotations only. If quotation marks indicate hypotheticals, ironies, or colloquialisms, please use single quotation marks around the first instance; later uses of this term do not require quotation marks.
Please use one space between sentences.
If emphasizing language within quotations, please use italics and include “[emphasis added]” in brackets.
Please use the Oxford comma before “and” in groups of three or more.
Example: “We bought apples, oranges, and grapes.”
When using abbreviations, please spell out the full name or title at first use with the abbreviation in parentheses. For all subsequent uses, use the abbreviation.
Please note that HDSR is reviewed, edited, and published in American English. Therefore, American English language spelling, grammar, and readability are important components of our review and decision process. Authors who feel that their English-language writing skills could use polishing are encouraged to consider seeking the assistance of an outside language editing service. HDSR reserves the right to use professional writers to edit any piece before publication, should the editors deem it necessary.
There is no minimum or maximum length requirement for submissions to HDSR. However, content must justify length, and the writing must be concise and engaging.
Authors may use up to two levels of headings. These headings should be numbered with Arabic numbers.
Example:
Section Title
1.1. Subsection Title
1.2. Subsection Title
Section Title
Please use in-line citations for references, following APA guidelines. Citations should be formatted as (Author Last Name, Year). When authors are named outside of the quotation, please include only the year in the citation.
Example:
“This is how to cite a quotation in HDSR” (Blue, 2018).
According to Red (2019), “You might also quote this way.”
If citing data visualization, please follow APA guidelines for citing infographics. In-line citations should appear as standard.
Endnotes of up to one paragraph length are acceptable for additional or supplementary commentary, notes, or further reading.
A Digital Object Identifier (DOI) provides a persistent link to digital content online. DOIs do not change over time. The DOI can typically be located at the top of an article’s first page. DOIs are formatted as https://doi.org/xxxx
A Uniform Resource Locator (URL) provides a specific location for digital information online. URLs may change over time. The URL can be found in the address bar of your internet browser.
Per APA style, provide a DOI for all works that have a DOI, whether you used the digital or print version. If a work has both a DOI and a URL, only provide the DOI.
Present both DOIs and URLs as hyperlinks in the reference list.
Do not add a period after DOIs or URLs.
Tables should not be formatted as image files or embedded images. We prefer tables in editable forms such as Word docs.
Tables should include call-outs in the manuscript for placement, as well as legends. Legends should include short titles/headings and a brief description.
Please label all tables clearly and use Arabic numbers when labeling figures (Table 1, Table 2).
If tables have multiple parts, please label each segment in consecutive order using Arabic numbers and lower-case letters (Table 3a, Table 3b).
Images should include call-outs in the manuscript for placement, as well as legends.
Please label all images clearly and use Arabic numbers when labeling figures (Figure 1, Figure 2).
If figures have multiple parts, please label each segment in consecutive order using Arabic numbers and lower-case letters (Figure 3a, Figure 3b).
For the review process, the only requirement for figures and illustrations is legibility. For accepted manuscripts, figures and illustrations must be submitted in production-ready quality.
Please note the following guidelines for production-ready images:
Figures and images must be submitted as separate files from the main document.
For best quality figures in the digital version, image files should be submitted at a minimum of 300 dpi resolution. They may be any common image file type (JPEG, PNG, etc.). They may not be embedded in documents or PDF files.
For best quality figures in print, image files should be submitted at a minimum of 300 dpi resolution, with a minimum width of 12.5 cm/5 in. They should be in TIF/TIFF or EPS format.
Excepting privacy alterations (such as a black bar over eyes) or adjustments of brightness, contrast, and color balance, photography should not undergo digital manipulation without explicit mention. If photographic manipulation is intentional for the purpose of the paper, authors should include a disclaimer in the photo caption explaining what was changed, how, and why. In the interest of maintaining scientific integrity and allowing for reproducibility, instances of intentional manipulation of photography or misrepresentation of photographic data may result in paper rejection.
HDSR adheres to current HIPAA guidelines protecting patient/subject privacy. Any images or photographs of patients in which an individual is identifiable, including images with minor alterations (i.e., black bars over eyes), requires the individual’s written permission to publish. HDSR does not collect consent forms regarding adherence to patient confidentiality and privacy policies, but should a query arise, the author must be able to present a signed consent form authorizing their use of the image(s).
Data visualizations are interactive media beyond static figures, images, or tables. For examples of visualizations that HDSR has published, see the series of music players in “(A) Data in the Life: Authorship Attribution in Lennon-McCartney Songs” or Figure 12 in “Machine Learning with Statistical Imputation for Predicting Drug Approvals.”
Data visualizations may be created in a number of programs. HDSR incorporates them into our site using iFrame
Creators of data visualization must be credited per the CRediT Taxonomy.
Videos may be embedded into the online platform in two ways:
File submitted as .mp4 or .webm file
Video uploaded to YouTube or Vimeo and embedded via link
Authors must have permission to publish videos in their work. There is no length restriction, but the content must be relevant to authors’ work and deemed appropriate by HDSR.
Please label tables, figures, etc. with either an “A” or “S” as appropriate. For example, a figure appearing in the Appendix should be labeled “Figure A1”; if this figure has multiple parts, it should be labeled “Figure A1a,” Figure A1b,” etc.
If authors must submit corrections or updates to published work, they should contact the editorial office. Errata and addenda appear at the end of the work, immediately preceding the references, and are labeled and dated.
Corrections will be made and noted in the text, and the authors should write a short statement to be included at the end of the article.
Authors should write a short follow-up statement to be included at the end of the article. For updates that do not require author corrections, no changes to the text are necessary.
Guidelines updated September 2020