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 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 MOOCs on the EdX platform, including an introduction to data science. Prof. 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 Ph.D. in Statistics from Berkeley.
Shivani Agarwal is Rachleff Family Associate Professor of Computer and Information Science at the University of Pennsylvania, where she also co-directs the Penn Research in Machine Learning (PRiML) center. Prior to this, she has been a Radcliffe Fellow at Harvard University, an Assistant Professor and Ramanujan Fellow at the Indian Institute of Science, and a postdoctoral lecturer at MIT. She received her PhD in computer science at the University of Illinois, Urbana-Champaign, and a bachelors degree in computer science as a Nehru Scholar at Trinity College, University of Cambridge. Her research interests include foundational questions in machine learning, applications of machine learning in the life sciences, and connections between machine learning and other disciplines such as economics, operations research, and psychology. More broadly, she is excited by research at the intersection of computer science, mathematics, and statistics, and its ability to turn data into actionable insights in both the natural and social sciences.
Anima Anandkumar is a Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology and Director of Research in Machine Learning at NVIDIA. Her research is in the areas of large-scale machine learning and high-dimensional statistics, and in particular, development of tensor methods that scale up machine learning to higher dimensions. She is also the recipient of the Alfred Sloan Fellowship, Microsoft Faculty Fellowship, ARO and AFOSR Young Investigator Awards, NSF Career Award and several paper awards. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a Postdoctoral Researcher in the Stochastic Systems Group at MIT from 2009-2010, an Assistant Professor at UC Irvine from 2010-2016, and Principal Scientist at Amazon Web Services from 2016-2018.
Stephen Ansolabehere is Frank G. Thompson Professor of Government at Harvard University. He is one of the nation’s leading academic experts on U. S. elections and voting behavior, and has written extensively on electoral competition, public opinion, media, political parties and election law and administration. He is author of four books, The Media Game (1993), Going Negative (1996), The End of Inequality: One Person, One Vote and the Transformation of American Politics (2008), American Government (2009, 2013), Cheap and Clean: How Americans Think About Energy in the Age of Global Warming (2014) and has published academic research in a wide range of fields, including political science, communications, economics, law, environment, and statistics. He is the Principal Investigator of the Cooperative Congressional Election Study, which he started in 2005. This NSF-funded research program conducts large-scale, national-sample election surveys as well as dozens of smaller surveys designed by 50 different research teams throughout the United States. He is a member of the CBS News Election Night Decision Desk and leads the Harvard Election Data Archive, an archive of election data for the United States and related census data. He directed of the Caltech-MIT Voting Technology Project from its founding in 2000 through 2004. He is a member of the Advisory Board of the Reuters Institute for the Study of Journalism at Oxford University. In 2007, he was elected to the American Academy of Arts and Sciences.
David Banks is the director of the Statistical and Applied Mathematical Sciences Institute and a professor in the Department of Statistical Science at Duke University. Previously, he held positions at the University of Cambridge, Carnegie Mellon, the National Institute of Standards and Technology, the U.S. Department of Transportation, and the Food and Drug Administration. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. 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.
Yoav Benjamini is the Nathan and Lily Silver Professor of Applied Statistics at the Department of statistics and O.R. 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, UC Berkeley, Stanford and Columbia Universities. Prof. 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.
Francine Berman is the Hamilton Distinguished Professor in Computer Science at Rensselaer Polytechnic Institute (RPI). She is a Fellow of the AAAS, IEEE, and 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.
Joe Blitzstein is Professor of the Practice in Statistics at Harvard University, where has taught since 2006, after completing his Ph.D. 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.
Alex Blocker is Head of Data Science at Foresite Capital, where his team works in statistical genetics, bioinformatics, machine learning, and platform engineering for the analysis of biomedical data. He was previously a data scientist for GRAIL, a company focused on early cancer detection using blood-based assays. At GRAIL, he drove development of classification methodology and machine learning-based somatic variant calling. Previously, he was technical lead for computational biology at Verily, where he led analysis of multi-omic studies in multiple sclerosis, guided the design of the Baseline study, and built core elements of data infrastructure. He came to Google and Verily after working as part of the quantitative team behind Google AdWords. He received a Ph.D. in statistics from Harvard University in 2013 and an M.A. in economics from Boston University in 2008. In addition to computational biology, his research has included statistical theory, asset valuation, astrophysics, information networks, and distributed computational methods for big data.
Christine L. Borgman, Distinguished Research Professor and Director of the Center for Knowledge Infrastructures at UCLA, 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.
Tamara Broderick is an Assistant Professor in the MIT Electrical Engineering and Computer Science Department. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She holds a PhD from UC Berkeley Statistics, an MS in Computer Science from UC Berkeley, an MPhil in Physics and a Master’s in Mathematics from U Cambridge, and an AB in Mathematics from Princeton. She has been awarded an NSF CAREER Award, a Sloan Research Fellowship, an Army Research Office Young Investigator Program award, Google Faculty Research Awards, the ISBA Lifetime Members Junior Researcher Award, the Savage and Evelyn Fix dissertation awards, the Berkeley Fellowship, an NSF Graduate Research Fellowship, and a Marshall Scholarship. Her research is in scalable, theoretically sound methods for uncertainty and robustness quantification – with a focus on Bayesian approaches.
Emery N. Brown
Emery N. Brown, M.D., Ph.D. is the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT; the Warren M. Zapol Professor of Anesthesia at Harvard Medical School; and a practicing anesthesiologist at Massachusetts General Hospital. Dr. Brown is an anesthesiologist-statistician whose experimental research has established maintenance of oscillations in the brain’s extracellular currents as a primary mechanism through anesthetics create general anesthesia. His statistics research has developed signal processing algorithms to study dynamic processes in neuroscience. Dr. Brown served on President Obama’s NIH Brain Initiative Working Group. He is the recipient of an NIH Director’s Pioneer Award, a Guggenheim Fellowship in Applied Mathematics, and the American Society of Anesthesiologists Excellence in Research Award. He is a Fellow of the American Academy of Arts and Sciences and the National Academy of Inventors. Dr. Brown is members of the National Academy of Medicine, the National Academy of Sciences, and the National Academy of Engineering.
Carlos Castillo-Chavez is a Regents Professor, Joaquin Bustoz Jr. Professor of Mathematical Biology, Distinguished Sustainability Scientist and Founding Director of the Simon A. Levin Mathematical, Computational and Modeling Sciences Center at ASU. Castillo-Chavez received his bachelor’s, master’s and doctoral degrees from three campuses of the University of Wisconsin and has co-authored over 250 publications. Per the Mathematics Genealogy Project, he is among the top 200 mentors of PhD students (49) in the history of mathematics. He was recognized with three White House Awards (1992, 1997, 2011) and served on President Obama’s Committee on the National Medal of Science (2010-2015). He is a George Polya Lecturer (2017-2019). He is a Provost Visiting Professor at Brown University, within its Division of Applied Mathematics and its Data Science Initiative. He is a fellow of SIAM, AMS, AAAS and the American College of Epidemiology.
Mine Çetinkaya-Rundel is Senior Lecturer in the School of Mathematics at University of Edinburgh and Data Scientist and Professional Educator at RStudio. She is on leave from her Associate Professor of the Practice position at the Department of Statistical Science at Duke University. 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 as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. She organizes ASA DataFest, an annual international two-day data science competition. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. She also teaches the popular Statistics with R MOOC on Coursera as well as numerous other online courses. Mine is the Chair of the ASA's Section on Statistical Education. In 2018 Mine received the David Pickard Teaching Award and in 2016 the ASA Waller Education Award. She is also the recipient of the 2015 JSM Best Paper Award in the Section on Teaching Statistics in the Health Sciences and the 2014 Duke University David and Janet Vaughan Brooks Award for Teaching Excellence.
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 through 2018 he was Alexander von Humboldt Professor of Digital Humanities at the University of Leipzig. He is Professor of Classics at Tufts University, with a secondary appointment in Computer Science.
Radu V. Craiu is Professor and Chair, Department of Statistical Sciences at the University of Toronto. He studied Mathematics at the University of Bucharest (BS 1995, MS 1996) and Statistics at The University of Chicago (PhD 2001). His main research interests are in computational methods in statistics, especially Markov chain Monte Carlo algorithms (MCMC), Bayesian inference, copula models and model selection. His theoretical and methodological contributions have found useful applications in Genetics and Ecology. He is an Elected Member of the International Statistical Institute and was awarded the SSC-CRM prize given by the Centre de recherche mathématiques and the Statistical Society of Canada to a statistical scientist for professional accomplishments in research during the first fifteen years after earning a doctorate.
Stephanie Dick is an Assistant Professor of History and Sociology of Science at the University of Pennsylvania. 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. Stephanie completed her PhD in History of Science at Harvard University in 2015 and was a Junior Fellow with the Harvard Society of Fellows between 2015 and 2018.
Moon Duchin is an Associate Professor of Mathematics and Senior Fellow in the Tisch College of Civic Life at Tufts University. She serves as director of the interdisciplinary program in Science, Technology, and Society and as collaborating faculty in the Department of Race, Colonialism, and Diaspora Studies. Her mathematical subfields are geometry, topology, group theory, and dynamical systems. Her data science focus is in the study of electoral redistricting in the United States, using Markov chain Monte Carlo and other randomized algorithms to understand relationships between community, partisanship, race, and representation.
Vanja is a Professor of Applied Mathematics, and a courtesy Professor of Economics, at the University of Colorado at Boulder. After receiving her PhD in Applied Mathematics from Brown University in 2001, she was a postdoctoral fellow and visiting professor in the Department of Statistics, and then an Assistant and Associate Professor in Biostatistics at the University of Chicago. She has served on the Board of Directors of ISBA, Scientific Advisory Board for ICERM, and as an associate editor for JASA, Bayesian Analysis, and Statistica Sinica. Vanja's main research interests are in Bayesian modeling, multiscale systems modeling, and computational statistics. Her work has applications to a wide variety of fields, including environmental science, medicine (infectious disease spread, diagnostic testing, biomarker calibration), ecology, risk modeling and insurance. She has also served as an expert consultant for the government and numerous industry clients.
Barbara Engelhardt is Associate Professor of Computer Science at Princeton University. She received her PhD from University of California, Berkeley, advised by Michael Jordan and was a Postdoctoral Scholar at the University of Chicago with Matthew Stephens. She was an assistant professor at Duke University in Biostatistics and Statistical Sciences for three years prior to her move to Princeton. Barbara previously worked at Jet Propulsion Laboratory, Google Research, and 23andMe. For her work, she received the Google Anita Borg Fellowship, an NIH Pathway to Independence Award, a Sloan Research Fellowship, and an NSF CAREER Award. She develops statistical and machine learning approaches for the analysis of biomedical data, including genomic data and hospital patient data.
Charles Elkan is currently a managing director and the global head of machine learning at Goldman Sachs in New York, and also an adjunct professor at the University of California, San Diego (UCSD). Previously, he was the first Amazon Fellow and the leader of Amazon's central machine learning team in the US; before that, he was a professor of computer science at UCSD for many years, with leaves that included being a visiting associate professor at Harvard. Dr. Elkan's research has been mainly in machine learning, data science, and computational biology. In particular, the MEME algorithm that he developed with Ph.D. students has been used in over 4000 published research projects in biology. He is fortunate to have had inspiring undergraduate and graduate students who have become faculty at institutions including Columbia, Stanford, and the University of Washington, or who have become leaders at Google and other major companies.
Prof Maria Fasli is the Director of the ESRC Business and Local Government Data Research Centre and founding Director of the Institute for Analytics and Data Science at the University of Essex. 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 HEA UK for her novel approaches to education and supporting the student experience. In 2016, she was awarded the UNESCO Chair in Analytics and Data Science. Her research interests lie in artificial intelligence techniques for complex systems and analysing and modelling structured/unstructured data. Her research has been funded by Research Councils and other organisations (over £8M 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.
Sylvia Fruhwirth-Schnatter is Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics, Vienna University of Economics and Business (Austria). She holds a PhD in mathematics from the Technical University of Vienna (Austria). She has contributed to research in Bayesian modeling and MCMC inference for a broad range of models, including finite mixtures, Markov switching models and state space models. She is particularly interested in applications of Bayesian inference in economics, finance, and business. She has published in all major statistical journals. Her 2006 monograph Finite Mixture and Markov Switching Models was awarded the Morris-DeGroot Price by of the International Society of Bayesian Analysis (ISBA). She was elected Member of the Austrian Academy of Sciences in 2014 and ISBA Fellow in 2018.
Robert D. Gibbons is the Blum-Riese Professor and a Pritzker Scholar at the University of Chicago. He has appointments in the Departments of Medicine, Public Health Sciences and Comparative Human Development. He also directs the Center for Health Statistics. Professor Gibbons is a Fellow of the American Statistical Association, the International Statistical Institute, and the Royal Statistical Society, and a member of the National Academy of Medicine of the National Academy of Sciences. He has authored more than 300 peer-reviewed scientific papers and five books. His statistical work spans the areas of longitudinal data analysis, item response theory, environmental statistics and drug safety and has led to thousands of applications in the biological and social sciences. Professor Gibbons has received life-time achievement awards from the American Statistical Association, the American Public Health Association and Harvard University. He is a founder of the Mental Health Statistics section of the American Statistical Association.
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, 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 US Chess Ratings Committee since 1985, and has been the Chair of the Committee continuously since 1992. 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.
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 Ph.D. in statistics from Harvard University (2018) and B.Sc. 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).
David Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, where he previously held the Chair of Statistics and Chaired the Research Board of the Data Science Institute. He serves on the Board of the UK Statistics Authority and the European Statistical Advisory Committee. He is a former president of the Royal Statistical Society and former Chair of the Board of the Administrative Data Research Network. He has received many awards for his research, including the Guy Medal of the Royal Statistical Society and the Box Medal from the European Network for Business and Industrial Statistics. His 29 books include Principles of Data Mining, Measurement Theory and Practice, The Improbability Principle, Information Generation, and Intelligent Data Analysis.
Alfred Hero is John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is also founding Co-Director of the University’s Michigan Institute for Data Science (MIDAS). He has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He received his Ph.D in Electrical Engineering and Computer Science from Princeton University. He is Fellow of the Institute of Electrical and Electronics Engineers, was President of the IEEE Signal Processing Society, and has served on the IEEE Board of Directors of the IEEE. He is chair of the Committee on Applied and Theoretical Statistics under the US National Academies and co-chair of the SAMSI National Advisory Committee. Alfred Hero’s recent research interests are in high dimensional spatio-temporal data, multi-modal data integration, statistical learning and signal processing.
Andrew Ho is Professor of Education at the Harvard Graduate School of Education. He is a psychometrician whose research aims to improve the design, use, and interpretation of test scores in educational policy and practice. Professor Ho is known for his research documenting the misuse of proficiency-based statistics in state and federal policy analysis. He has also clarified properties of student growth models for both technical and general audiences. His scholarship advocates for designing evaluative metrics to achieve multiple criteria: metrics must be accurate, but also transparent to target audiences and resistant to inflation under perverse incentives.
Nicholas Horton is Beitzel Professor of Technology and Society and Professor of Statistics 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 and the American Assocation 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 workgroup. Nick serves on the National Academies Committee for Applied and Theoretical Statistics and is a co-author of the recently published "Undergraduate Data Science: Opportunities and Options" consensus study report.
Rafael Irizarry received his Bachelor’s in Mathematics in 1993 from the University of Puerto Rico and his Ph.D. 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.
Marina Jirotka is Professor of Human Centred Computing at the University of Oxford. She leads a team focused on responsible innovation in a variety of advanced technologies - machine learning and AI, quantum technologies, social media technologies and the digital economy. Her work focuses on bringing a richer comprehension of socially organised work practices and interaction into the process of engineering technological systems. In particular her work is concerned with how new developments in machine learning and AI can be shaped to respect human agency, ensuring accountability of systems and the digital rights of individuals and communities. She is a leader in the discipline of Responsible Research and Innovation (RRI), a framework that uses anticipatory governance, reflection and stakeholder involvement to ameliorate the potential harms than can arise from computer-related technologies.
Booil Jo, Ph.D., 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.
Noriko Kando is a professor in the Information-society Research Division of the National Institute of Informatics (NII), Tokyo, Japan, and has been co-appointed as a professor in the Department of Informatics at the Graduate University of Advanced Studies. She initiated NTCIR (http://research.nii.ac.jp/ntcir/index-en.html), an evaluation of information-access technologies—such as information retrieval, summarization, question answering, and text mining—using East Asian languages and English documents, and has been a main designer of various tasks which have attracted international participation, including cross-lingual IR, patent retrieval, opinion analysis, complex question answering, community QA, and geo-time search. She has been involved in various projects on interactive information access, such as Cognitive Research on Exploratory Search (CRES), Multifaceted Exploratory on Web (MEW), Interacting with Cultural Heritage Digital Archives for Discovery Learning (CEAX), Gaze-driven Interactive Image Search (GLAISE) and Search as Learning (SAL). She received her Ph.D. from Keio University in Library and Information Science in 1995 and has been teaching and conducting research at NII since 1994. She has had more than 200 of her refereed scientific articles published in journals and by international conferences, and has been an invited speaker at many international conferences and workshops.
Karim Kassam heads analytics at the Pittsburgh Steelers, working with the coaching staff to analyze opponent tendencies and with the scouting staff to predict which college athletes will go on to become successful professionals. Prior to joining the Steelers, he was Senior Director of Quantitative Analytics at Legendary Entertainment, and Assistant Professor in the Department of Social and Decision Sciences at Carnegie Mellon University. Kassam obtained a BSc in Electrical Engineering from Queen’s University, and MSc in Advanced Computing from Imperial College, and a PhD in Social Psychology from Harvard University.
Sallie Keller, Ph.D., is Director of the Social and Decision Analytics Division within Biocomplexity Institute and Professor of Public Health Sciences at University of Virginia. Prior positions include Professor of Statistics and Director of Social and Decision Analytics Laboratory at Virginia Tech; Academic Vice-President and Provost at University of Waterloo; Director of IDA’s Analyses Science and Technology Policy Institute; William and Stephanie Sick Dean of Engineering at Rice University; Head of Statistical Sciences group at Los Alamos National Laboratory, and Professor of Statistics at Kansas State University. She served as member of National Academy of Sciences Board on Mathematical Sciences and Their Applications; Committee on National Statistics; and chaired the Committee on Applied and Theoretical Statistics. She is fellow of AAAS, elected member of ISI, fellow and past president of the ASA, and member of the JASON advisory group. She holds a Ph.D. in statistics from Iowa State University.
Professor Frauke Kreuter is Director of the Joint Program in Survey Methodology at the University of Maryland, USA; Professor of Statistics and Methodology at the University of Mannheim; and head of the Statistical Methods Research Department at the Institute for Employment Research in Nürnberg, Germany. She founded the International Program in Survey and Data Science, and is co-founder of the Coleridge Initiative. Frauke Kreuter is elected fellow of the American Statistical Association, recipient of the Gertrude Cox Award and the ASA Links Lecture Award, and member of the advisory boards to Statistics Sweden, Statistics Canada and the German Institute for Economic Research.
Todd Kuffner is an Assistant Professor in the Department of Mathematics and Statistics at Washington University in St. Louis. Professor Kuffner earned degrees from the University of Michigan and London School of Economics before completing his Ph.D. in the Department of Mathematics at Imperial College London in 2011. His research interests include philosophy of statistics and data science, higher-order asymptotics, high-dimensional and post-selection inference, the bootstrap, dependent data, differential geometry in statistics, and Bayes-frequentist reconciliation. He also works with neuroscientists at the Washington University School of Medicine to study disease progression in Alzheimer's disease.
Julia Lane is a Professor at the NYU Wagner Graduate School of Public Service at the Center for Urban Science and Progress, and a Provostial Fellow for Innovation Analytics. She cofounded the Coleridge Initiative, whose goal is to use data to transform the way governments access and use data for the social good through training programs, research projects and a secure data facility. Prior to this, Julia was a Senior Managing Economist and Institute Fellow at American Institutes for Research. In this role, Julia co-founded the Institute for Research on Innovation and Science (IRIS) at the University of Michigan. Julia has held positions at the National Science Foundation, The Urban Institute, The World Bank, American University and NORC at the University at Chicago. In these positions, Julia has led many initiatives, including co-founding the Institute for Research and Innovation in Science (IRIS) at the University of Michigan and STAR METRICS programs at the National Science Foundation. She also initiated and led the creation and permanent establishment of the Longitudinal Employer-Household Dynamics Program at the U.S. Census Bureau.
Nicole Lazar is Professor, Department of Statistics, University of Georgia. 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. 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 is serving at President of the Caucus for Women in Statistics. She is an elected member of the International Statistical Institute and a Fellow of the American Statistical Association.
Sabina Leonelli is Professor in Philosophy and History of Science at the University of Exeter, where she co-directs the Centre for the Study of the Life Sciences (Egenis) and coordinates the Data Studies group. She is also a Fellow of the Alan Turing Institute in London. Her research, currently supported by the European Research Council, UKRI, Turing and Australian Research Council, concerns the methods, infrastructures and assumptions involved in the use of big data for discovery; the epistemology of data-intensive science and its social and scientific implications; the role of the open science movement within global shifts in the production and publication of knowledge; and the status and history of organisms as scientific models and data sources. Leonelli’s publications span the fields of philosophy, social science, biology, history, data science and science policy, and include the award-winning book Data-Centric Biology: A Philosophical Study (2016). She works closely with the European Commission on the development and implementation of Open Science policies.
Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. His research focuses on machine learning and data mining applied to social, information and biological networks, their evolution, and the diffusion of information and influence over them. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, economics, marketing, and healthcare. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University.
Edlyn V. Levine, Ph.D. is a Lead Scientist at the MITRE Corporation and a Research Associate in the Department of Physics at Harvard University. Dr. Levine currently leads several research projects in communications and cybersecurity at the nexus of classical and quantum information, with foci of plasmas dynamics and quantum metrology. Her research efforts are aimed at the development of advanced sensor platforms and communications systems. She is a four-time awardee of the MITRE Innovation Program grant for her research. Dr. Levine earned her Ph.D. in Applied Physics from Harvard University where she was a National Defense Science and Engineering Fellow and a National Science Foundation Graduate Fellow. She is a member of the Harvard Graduate School Alumni Council, and a member of the Executive Committee for the American Physical Society (APS) Forum for Industrial and Applied Physics.
Andrew W. Lo is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management, director of the MIT Laboratory for Financial Engineering, a principal investigator at the MIT Computer Science and Artificial Intelligence Laboratory, and an affiliated faculty member of the MIT Department of Electrical Engineering and Computer Science. His current research interests in data science include: applications of machine learning to credit risk management, drug discovery, cancer therapeutics, and real-time psychophysiological data from financial traders; interpretability of machine-learning models; and data visualization. He has published numerous articles in finance and economics journals, and his most recent book is Adaptive Markets: Financial Evolution at the Speed of Thought. He is currently co-editor of the Annual Review of Financial Economics. He received a B.A. from Yale and an M.A. and Ph.D. from Harvard, all in economics.
David Madigan is Professor of Statistics at Columbia University in New York City and Dean Emeritus of Arts and Sciences. He received a bachelor’s degree in Mathematical Sciences and a Ph.D. 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 180 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 ASA Data Science Journal.
Andrew McCallum is a Professor and Director of the Information Extraction and Synthesis Laboratory, as well as Director of Center for Data Science in the College of Information and Computer Science at University of Massachusetts Amherst. He has published over 250 papers in many areas of AI, including natural language processing, machine learning and reinforcement learning; his work has received over 50,000 citations. In the early 2000s he was Vice President of Research and Development at at WhizBang Labs, a startup company that used machine learning for information extraction from the Web. He is a AAAI Fellow and the recipient of the UMass Chancellor's Award for Research and Creative Activity, the UMass NSM Distinguished Research Award, the UMass Lilly Teaching Fellowship, and research awards from Google, IBM, Microsoft, and Yahoo. He was the General Chair for the International Conference on Machine Learning (ICML) 2012, and is the current President of the International Machine Learning Society.
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 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.
Daniel T. O’Brien
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, 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.
Christopher J. Phillips
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 NYU and holds a PhD in history of science from Harvard University.
H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University, where he has been on the faculty since 1990. Prior to joining the Princeton faculty, he was on the faculty of the University of Illinois at Urbana-Champaign. During 2006 to 2016, he served as Dean of Princeton’s School of Engineering and Applied Science. He has also hold visiting appointment at a number of other universities, most recently at Berkeley and Cambridge. His research interests are in the areas of information theory and signal processing, and their applications in wireless networks, energy systems, and related fields. Dr. Poor is a member of the National of Academy of Engineering and the National Academy of Sciences, and is a foreign member of the Chinese of Academy of Sciences, the Royal Society, and other national and international academies. Recent recognition of his work includes the 2017 IEEE Alexander Graham Bell Medal.
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.
Cynthia Rudin is an associate professor of computer science, electrical and computer engineering, and statistics at Duke University, and directs the Prediction Analysis Lab. Her interests are in machine learning and data science. Prof. Rudin has held positions at MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo, and a PhD from Princeton University. She is the recipient of the 2013 and 2016 INFORMS Innovative Applications in Analytics Awards, an NSF CAREER award, and was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015. She is past chair of the INFORMS Data Mining Section, and is currently chair of the Statistical Learning and Data Science section of the American Statistical Association. She also serves on (or has served on) committees for DARPA, the National Institute of Justice, the National Academy of Sciences (for both statistics and criminology/law), and AAAI.
Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University. She grew up in Italy where she graduated from Bocconi University with a degree in “Economics and Social Disciplines” (DES) in 1993, working with Eugenio Regazzini. She obtained a PhD in Statistics from Stanford University in 1998 with a thesis on multi scale MCMC methods under the supervision of Jun Liu. Between 1998 and 2000 she was a post-doctoral scholar in Statistical Genetics with Neil Risch. In 2000 she joined the faculty at UCLA in the newly established departments of Human Genetics and Statistics and in 2009 she was appointed at Stanford. Her research efforts are devoted to developing statistical methodologies to extract actionable information from high dimensional genomics datasets. She has a keen interest in how society understands and utilizes what we learn from data and enjoys exploring these questions in open forums.
Nathan Sanders is a statistician, astronomer, and an organizer in science communication. Nathan is the Chief Scientist for WarnerMedia Applied Analytics, where he leads a team of physical, social, and computational scientists engaged in performing experiments, developing novel statistical and machine learning models, and generating remarkable insights about consumers and content for WarnerMedia's global media and entertainment business. Nathan is the Chair of the national Leadership Team for ComSciCon, the Communicating Science Conference series for graduate students (http://comscicon.com/); is a co-founder of Astrobites (http://astrobites.org) and the ScienceBites network of graduate student writing collaboratives; and serves on the Board of the American Institute of Physics. Nathan did his undergraduate work in Physics and Astrophysics at Michigan State University and earned his PhD in Astronomy and Astrophysics from Harvard University.
Ruslan Salakhutdinov is a UPMC Professor of Computer Science in the Department of Machine Learning at 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 NIPS and 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.
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 A.B. in classics (Greek) from Harvard and a Ph.D. 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”.
Michael Stein graduated from MIT in 1980 with a B.S. in mathematics and received the M.S. and Ph.D. in statistics from Stanford in 1982 and 1984. After spending a year at the IBM T.J. Watson Research Center, he joined the faculty at the University of Chicago, where he is now the Ralph and Mary Otis Isham Professor of Statistics and the College. Most of Stein's research has been in the statistical analysis of spatial and spatial-temporal processes with applications to the environmental sciences. His current research focuses on statistical problems in climatology and meteorology.
Hal Stern is Chancellor’s Professor in the Department of Statistics at the University of California, Irvine (UCI). He is known for his research work developing Bayesian statistical methods and model assessment techniques and for collaborative projects in the life sciences and the social sciences. He has published more than 100 refereed journal articles and he is a co-author of the highly-regarded graduate level statistics text Bayesian Data Analysis. Current areas of interest include applications of statistical methods in psychology, personalized medicine, sports statistics and forensic science. He is a Fellow of the American Association for the Advancement of Science (AAAS), American Statistical Association (ASA) and the Institute for Mathematical Statistics (IMS). Stern received his B.S. degree in Mathematics from the Massachusetts Institute of Technology in 1981 and M.S. and Ph.D. degrees in Statistics from Stanford University in 1985 and 1987, respectively.
John J. Stevens
John Stevens is an Associate Director in the Division of Research and Statistics at the Board of Governors of the Federal Reserve System. His responsibilities include communicating the staff’s economic projection to policymakers, participating in range of strategic planning and oversight activities, conducting economic research, and leading or participating in a variety of data-related initiatives. He is a member of the National Bureau of Economic Research Conference on Research on Income and Wealth, the American Economic Association, and the National Association for Business Economics’ Statistics Committee. He is also a Special Sworn Status researcher at the U.S. Census Bureau. Mr. Stevens holds a Ph.D. in Economics from the University of Minnesota. His research has appeared in a variety of journals, including the Journal of Political Economy, the Journal of International Economics, the Review of Economics and Statistics, the Journal of Economic Geography, and the B.E. Journals in Macroeconomics.
Piyushimita (Vonu) Thakuriah is Dean of the Edward J Bloustein School of Planning and Public Policy and Distinguished Professor in Rutgers University. Her research interests are on transportation, smart cities, and urban informatics. She is more widely interested in the role of automation and AI on our daily lives and on jobs, and in analytics of emerging sources of data for knowledge discovery to better address complex urban problems. More recent research has focused on data sharing, access to data, and building data infrastructure. Vonu is also an Honorary Professor of University of Glasgow, UK, where she was previously Ch2M Chair Professor of Transport, and (founding) director of the Urban Big Data Centre, a seven-university consortium funded by Research Council UK’s Economic and Social Research Council. The center has researchers from 10 academic disciplines in the social sciences, data sciences and engineering and operates a unique UK-wide data service to support innovations in smart and connected cities.
Caroline Uhler joined the MIT faculty in 2015 and is currently the Henry and Grace Doherty Associate Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society. She holds an MSc in mathematics, a BSc in biology, and an MEd from the University of Zurich. She obtained her PhD in statistics from UC Berkeley in 2011. After short postdoctoral positions at ETH Zurich, the Simons Institute and the Institute for Mathematics and its Applications, she spent 3 years as an assistant professor at IST Austria. She is a Sloan Research Fellow and an elected member of the International Statistical Institute. She received an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Her research focuses on statistics and machine learning, in particular on graphical models and causal inference, and applications to genomics.
David Uminsky is 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 he was a combined NSF and UC President's Fellow at UCLA, where he was awarded the Chancellor's Award for outstanding postdoctoral research. He holds a Ph.D. in Mathematics from Boston University and a BS in Mathematics from Harvey Mudd College.
Lars Vilhuber holds a Ph.D. 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 Senior Research Associate in the ILR School, Executive Director of the Labor Dynamics Institute, and affiliated with the Department of Economics. 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).
Liberty is currently a Visiting Assistant Professor at Washington University in St. Louis. She is a graduate of MIT as well as Le Cordon Blue Paris and the University of Glasgow. Her current research involves using facial shape analysis to help children with facial deformities or victims of warfare. Liberty is a regular TV and Radio contributor to many news organizations including BBC, ITV, Channel 4, PBS, and FNC, as well as having her own TV series on STV (ITV). Her opinion editorials appear in Popular Science, US News, Newsweek, Business Insider, International Business Times, CBS News, The Conversation, and Fox News. As a Royal Statistical Society Ambassador, BBC Expert Woman, and an Elected Member of the International Statistical Institute, Liberty is writing a series of popular science books on how to lie with statistics from the viewpoint of multiple professions. Liberty is also on the board of USA for the UN Refugee Agency (UNHCR) as well as being on the board for The Hive (a data initiative), one of the Fast Company's top 6 most innovative non-profits.
Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Systems Biology, and is affiliated faculty in Statistics. He is a co-founder and co-organizer of hackNY (http://hackNY.org), a nonprofit which since 2010 has organized once a semester student hackathons and the hackNY Fellows Program, a structured summer internship at NYC startups. Prior to joining the faculty at Columbia he was a Courant Instructor at NYU (1998-2001) and earned his PhD at Princeton University (1993-1998) in theoretical physics. He is a Fellow of the American Physical Society and is a recipient of Columbia's Avanessians Diversity Award.
Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. Her research is focused on machine learning, signal processing, and large-scale data science. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010.
Robert C. “Bob” Williamson is a professor in the research school of computer science at the Australian National University. Until 2017 he was chief scientist of DATA61 where he was responsible for the science vision. He was instrumental in creating NICTA, serving as Canberra laboratory director, machine learning group leader, scientific director, and CEO. He lead the writing of a report for the Chief Scientist of Australia on new technologies and their impacts. His research is focussed on three broad areas of machine learning: compositional theoretical foundations, including several recent works on fast rates in learning and the structure of loss functions and measures of information; architectural issues of large machine learning systems, and social aspects of machine learning, most recently focussing on questions of fairness. He is a fellow of the Australian Academy of Science and the Australian Mathematical Society.
Dr. Alyson Wilson is a Professor in the Department of Statistics and Principal Investigator for the Laboratory for Analytic Sciences at North Carolina State University. Her research interests include statistical reliability, Bayesian methods, and the application of statistics to problems in defense and national security. She is the coordinator of NCSU's Data-Driven Science faculty cluster and Associate Director of the NCSU Data Science Initiative. Prior to NCSU, Dr. Wilson was a Research Staff member at the IDA Science and Technology Policy Institute (2011-2013), a faculty member in the Department of Statistics at Iowa State University (2008-2011), a Scientist in the Statistical Sciences Group at Los Alamos National Laboratory (1999-2008), and a senior statistician with Cowboy Programming Resources (1995-1999). She is a Fellow of the ASA and the AAAS and a winner of the 2018 ASA Section on Statistics in Defense and National Security Distinguished Achievement Award (2018).
Sally Wyatt is Professor of Digital Cultures at Maastricht University. She originally studied economics (at McGill University in Canada and the University of Sussex in England). Her main intellectual affinity is with Science and Technology Studies (STS). For many years, her research has focused on digital technologies, both how they are used by people wishing to inform themselves about health-related issues, and how scholars themselves use digital technologies in the creation of knowledge. Her most recent book, co-authored with Anna Harris and Susan Kelly, is called CyberGenetics. Health Genetics and New Media (Routledge, 2016). Wyatt has participated in national and international policy-making activities around big and open data. She was part of the committee that prepared the international accord for Science International, called Open Data in a Big Data World (Boulton et al, 2015).
Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, as well as a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems.
Li-Chun Zhang is Professor of Social Statistics at University of Southampton, Senior Researcher at Statistics Norway, and Professor of Official Statistics at University of Oslo. His research interests include finite population sampling design and coordination, finite graph sampling, sample survey estimation, non-response, measurement errors, small area estimation, index number calculations, editing and imputation, register-based statistics, statistical matching, record linkage. His involvement in research projects include the EU framework projects EURAREA, DACSEIS, RISQ and BLUE-ETS; the ESSnet projects Small Area Estimation, Data Integration and Quality of Multisource Statistics; the H2020-project InGRID-2; the ESRC-projects ADRCE, NCRM-SAE.
Harrison Zhou is a Henry Ford II Professor and Chair of the Department of Statistics and Data Science at Yale. His main research interests include asymptotic decision theory, large covariance matrices estimation, graphical models, Bayesian nonparametrics, statistical network analysis, sparse canonical correlation analysis and principal component analysis, and analysis of iterative algorithms. His research has been acknowledged with awards including the National Science foundation Career Award, the Noether Young Scholar Award from the American Statistical Association, the Tweedie Award, the IMS Medallion lecture and IMS Fellow from the Institute of mathematical Statistics.