Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS at UC Berkeley. She leads the Yu Group which consists of students and postdocs from Statistics and EECS. She was formally trained as a statistician, but her research extends beyond the realm of statistics. Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics and precision medicine. She and her team develop relevant theory to understand random forests and deep learning for insight into and guidance for practice.
Bin Yu is a member of the U.S. 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. She holds an Honorary Doctorate from
The University of Lausanne (UNIL), Faculty of Business and Economics, in Switzerland.
She has recently served on the inaugural scientific advisory committee of the UK Turing Institute for Data Science and AI, and is serving on the editorial board of Proceedings of National Academy of Sciences (PNAS).
Athanasios Kottas obtained a Ph.D. in Statistics from the University of Connecticut in 2000. From 2000 to 2002, he was a Visiting Assistant Professor at the Institute of Statistics and Decision Sciences at Duke University, and, since 2002, he has been at the University of California, Santa Cruz (UCSC).
He is currently Professor in the Department of Statistics at UCSC. His research interests include Bayesian nonparametrics, mixture models, nonparametric regression, point process modeling, and survival analysis, with applications in biometrics, ecology, and the environmental sciences. He has published 60 journal papers and book chapters, and has been awarded 10 research grants. He has co-advised one NSF Bioinformatics postdoctoral fellow, and supervised/co-supervised 13 Ph.D. dissertations. He has served on the Program Council of the International Society for Bayesian Analysis, and he is currently serving as co-Editor of Bayesian Analysis.
Igor Markov is currently a Research Scientist at Meta working on AI Platforms. He is an IEEE Fellow and an ACM Distinguished Scientist. Previously, he worked at Google on Search and was a Professor at the University of Michigan, where 12 students received Ph.D. degrees under his guidance.
Benjamin Nachman received his Bachelors from Cornell University in 2012, was a Churchill Scholar at the University of Cambridge in 2012-2013, and received his Ph.D. in Physics / Ph.D. minor in Statistics from Stanford University in 2016. He was the Chamberlain Fellow at Berkeley Lab from 2016-2020 and is now a Staff Scientist at Berkeley Lab / Research Affiliate at the Berkeley Institute for Data Science at UC Berkeley. He is the group leader of Machine Learning for Fundamental Physics and a founding member of the Quantum Algorithms for High Energy Physics group.