Wednesday, April 19, 2022 from 12pm – 2pm EST Speakers Prof. Alexander Gammerman, Royal Holloway University of London Prof. Matteo Sesia, University of Southern California, Marshall School of Business Prof. Lihua Lei, Stanford University About the Speakers Prof. Alexander Gammerman is a Fellow of the Royal Statistical Society, Fellow of the Royal Society of Arts, and Member of British Computer Society. He chaired and participated in organizing committees of many international conferences and workshops on Machine Learning and Bayesian methods in Europe, Russia and in the United States. He is also a member of the editorial board of the Law, Probability and Risk journal. Professor Gammerman’s current research interest lies in application of Algorithmic Randomness Theory to machine learning and, in particular, to the development of confidence machines (conformal predictors). Areas in which these techniques have been applied include medical diagnosis, forensic science, genomics, environment and finance. Professor Gammerman has published about two hundred research papers and several books on computational learning and probabilistic inference. Prof. Matteo Sesia is an assistant professor in the department of Data Sciences and Operation, at the USC Marshall School of Business. His research is focused on developing data science methods combining the power of machine learning algorithms with the reliability of rigorous statistical guarantees. While pursuing this goal, he enjoys dividing his time between theoretical, methodological, computational, and applied work. His doctoral research earned the Jerome H. Friedman Applied Statistics Dissertation Award from the Stanford Statistics Department in 2020. Prof. Lihua Lei is an Assistant Professor of Economics and Assistant Professor of Statistics at the School of Humanities and Sciences at Stanford University, Graduate School of Business. He mainly works at the intersection of econometrics and statistics. A large portion of his research focuses on empowering statistical reasoning with machine learning and augmenting machine learning with statistical reasoning. The central theme is preserving the statistical rigor of confirmatory analysis without compromising the freewheeling nature of exploratory analysis.