AISTATS 2021 Invited Speakers

Speaker Name: Professor Emmanuel Candès, Stanford University

Biography:

Emmanuel Jean Candès is the Barnum-Simons Chair in Mathematics and Statistics and Professor of Mathematics, of Statistics, and of Electrical Engineering at Stanford University, where he is also Director of Data Science. His research interests include compressive sensing, mathematical signal processing, computational harmonic analysis, statistics, and scientific computing. Candès is also interested in applications to the imaging sciences and inverse problems, as well as theoretical computer science, mathematical optimization, and information theory. A graduate of the École Polytechnique, Candès earned a Ph.D. in statistics from Stanford University. Candès has received numerous awards, including the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing, the Vasil A. Popov Prize, the Alan T. Waterman Award, the George Pólya Prize (with Terence Tao), the ICIAM Collatz Prize, the Lagrange Prize in Continuous Optimization, the Dannie Heineman Prize, the George David Birkhoff Prize, and a MacArthur Fellowship. Candès is a fellow of SIAM and the American Mathematical Society, and was elected to the National Academy of Sciences.

Speaker Name: Professor Bin Yu, UC Berkeley

Biography:

Bin Yu obtained her BS degree in mathematics from Peking University, and MS and PhD degrees in statistics from UC Berkeley. She was assistant professor at UW-Madison, visiting assistant professor at Yale University, member of technical staff at Lucent Bell-Labs, and Miller Research Professor at Berkeley in 2004 and 2015, respectively. Bin was a visiting faculty at MIT, ETH, Poincare Institute, Peking University, INRIA-Paris, Fields Institute at University of Toronto, Newton Institute at Cambridge University, and Flatiron Institute. She is past chair of department of statistics at UC Berkeley. Bin is a member of the National Academy of Sciences and of the American Academy of Arts and Sciences. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott prize winner.

Speaker Name: Professor Kyle Cranmer, NYU

Biography:

Kyle Cranmer is a Professor of Physics and Data Science at New York University. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. Professor Cranmer obtained his Ph.D. in Physics from the University of Wisconsin-Madison in 2005 and his B.A. in Mathematics and Physics from Rice University. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July, 2012. His current interests are at the intersection of physics, statistics, and machine learning.