Speakers
Peter Bühlmann - ETH Zürich
Dr. Peter Bühlmann is Professor of Mathematics and Statistics, and Director of Foundations of Data Science at ETH Zürich. His main research interests are in high-dimensional and computational statistics, machine learning, causal inference and interdisciplinary applications in the bio-medical field.
He studied mathematics at ETH Zürich and received his doctoral degree in 1993 from the same institution. He was a Postdoctoral Fellow from 1994-1995 and a Neyman Assistant Professor from 1995 - 1997 at UC Berkeley, before he returned to ETH Zürich in 1997. From 2013 - 2017, he was Chair of the Department of Mathematics at ETH Zürich. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of the American Statistical Association, and was Co-Editor of the Annals of Statistics from 2010 - 2012. Other honors which he recently received include Doctor Honoris Causa from the Université Catholique de Louvain in 2017, Neyman Lecturer 2018 elected by the Institute of Mathematical Statistics, Rothschild Lecturer 2018 at the Newton Institute (Cambridge), and recipient of the Guy Medal in Silver 2018 from the Royal Statistical Society.
Judea Pearl - University of California, Los Angeles
Dr. Judea Pearl is Chancellor's professor of computer science and statistics at UCLA, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human cognition, and philosophy of science. He has authored numerous scientific papers and three books, Heuristics (1983), Probabilistic Reasoning (1988) and Causality (2000, 2009), which won the London School of Economics Lakatos Award in 2002. More recently, he co-authored Causal Inference in Statistics (2016, with M. Glymour and N. Jewell) and The Book of Why (2018, with Dana Mackenzie), which brings causal analysis to the general audience. Pearl is a member of the National Academy of Sciences and the National Academy of Engineering, a fellow of the Cognitive Science Society, and a founding fellow of the Association for the Advancement of Artificial Intelligence. In 2012, he won the Technion's Harvey Prize and the ACM Alan Turing Award for the development of a calculus for probabilistic and causal reasoning.
James M. Robins - Harvard T.H. Chan School of Public Health
Dr. James M. Robins is the Mitchell L. and Robin LaFoley Dong Professor of Epidemiology and Professor of Biostatistics at the Harvard Chan School of Public Health. Prof. Robins has pioneered analytic methods appropriate for drawing causal inferences and estimating optimal dynamic treatment regimes from complex observational and randomized studies with time-varying exposures or treatments. The methods are based on new classes of causal models including nondynamic and dynamic marginal structural models, and total effect, direct effect and optimal regime structural nested models. Furthermore, Professor Robins, with his collaborator Andrea Rotnitzky, introduced the methodology, now ubiquitous, of doubly robust estimation in causal inference and missing data models.
Donald B. Rubin - Tsinghua, Temple, and Harvard Universities
Dr. Donald B. Rubin is currently Professor in the Yau Center for Mathematical Sciences, Tsinghua University; Murray Schusterman Senior Research Fellow, Fox Business School, Temple University; and Professor Emeritus, Harvard University. He has been elected to be a Fellow/Member/Honorary Member of: the Woodrow Wilson Society, Guggenheim Memorial Foundation, Alexander von Humboldt Foundation, American Statistical Association, Institute of Mathematical Statistics, International Statistical Institute, American Association for the Advancement of Science, American Academy of Arts and Sciences, European Association of Methodology, the British Academy, and the U.S. National Academy of Sciences. As of 2020, he has authored/coauthored nearly 500 publications (including ten books), has four joint patents, and for many years has been one of the most highly cited authors in the world, with currently over 300,000 citations, and over 20,000 per year in recent years (Google Scholar). Of his many publications with over 1,000 citations each, over ten of them are solely authored by Rubin. He has received honorary doctorate degrees from Otto Friedrich University, Bamberg, Germany; the University of Ljubljana, Slovenia; Universidad Santo Tomás, Bogotá, Colombia; Uppsala University, Sweden; and Northwestern University, Evanston, Illinois. He has also received honorary professorships from the University of Utrecht, The Netherlands; Shanghai Finance University, China; Nanjing University of Science & Technology, China; Xi’an University of Technology, China; and University of the Free State, Republic of South Africa. He is a widely sought international lecturer and consultant on statistical topics.
Arman Sabbaghi - Purdue University
Dr. Arman Sabbaghi is an Associate Professor in the Department of Statistics, and an Associate Director of the Statistical Consulting Service, at Purdue University. He received his PhD in Statistics from Harvard University in 2014, his AM in Statistics from Harvard University in 2011, and his BS in Mathematics (with Honors) and BS in Mathematical Statistics from Purdue University in 2009. Dr. Sabbaghi's research interests are in Bayesian data analysis, experimental design, and causal inference. Specific major objectives of his current research are (1) the development of efficient and interpretable statistical frameworks and machine learning algorithms for modeling and quality control in additive manufacturing (AM) systems, (2) the creation of mathematical tools that facilitate the characterization of broad classes of experimental designs for the study and improvement of processes in engineering and the physical sciences, and (3) the development of new causal inference methods for the analysis of Big Observational Data and clinical trials plagued by nonadherence.