Emmanuel Candès (Stanford University, US): A Tutorial on Conformal Inference.

Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, and Professor of Electrical Engineering (by courtesy). His research interests lie at the interface of statistics, information theory, signal processing and computational mathematics. He received his Ph.D. in statistics from Stanford University in 1998. Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by NSF to early-career scientists, and the MacArthur Fellowship, popularly known as the ‘genius award’. He has given over 80 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014.

Nati Srebro (TTI and University of Chicago, US): Tutorial on Mathematics of Deep Learning.

Nati (Nathan) Srebro is a professor at the Toyota Technological Institute at Chicago, with cross-appointments at the University of Chicago’s Department of Computer Science, and Committee on Computational and Applied Mathematics. He obtained his PhD from the Massachusetts Institute of Technology in 2004, and previously was a postdoctoral fellow at the University of Toronto, a visiting scientist at IBM, and an associate professor at the Technion. Dr. Srebro’s research encompasses methodological, statistical and computational aspects of machine learning, as well as related problems in optimization. Some of Srebro’s significant contributions include work on learning “wider” Markov networks, introducing the use of the nuclear norm for machine learning and matrix reconstruction, work on fast optimization techniques for machine learning, and on the relationship between learning and optimization. His current interests include understanding deep learning through a detailed understanding of optimization, distributed and federated learning, algorithmic fairness and practical adaptive data analysis.

Vladimir Vovk (Royal Holloway University, UK): Conformal prediction and testing.

Vladimir Vovk is Professor of Computer Science at Royal Holloway, University of London. His research interests include machine learning and the foundations of probability and statistics. He was one of the founders of prediction with expert advice, an area of machine learning avoiding making any statistical assumptions about the data. In 2001 he and Glenn Shafer published a book (“Probability and Finance: It’s Only a Game”, New York: Wiley) on new game-theoretic foundations of probability; the sequel (“Game-theoretic Foundations for Probability and Finance”, Hoboken, NJ: Wiley) appeared in 2019. His second book (“Algorithmic Learning in a Random World”, New York: Springer, 2005), co-authored with Alex Gammerman and Glenn Shafer, is the first monograph on conformal prediction; a second edition is to appear in 2022. His current research centres on applications of game-theoretic probability to statistics and machine learning.