Invited Talks

About the speaker Eric Xing

Professor Eric Xing is the President and University Professor of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a Professor of Computer Science at Carnegie Mellon University and co-Founder and Chief Scientist at Genbio AI. His main research interests are the development of machine learning and statistical methodology, and large-scale distributed computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in in artificial, biological, and social systems. In recent years, he has been focusing on building large language models, world models, agent models, and foundation models for biology. Prof. Xing has served on the editorial boards of several leading journals including JASA, AOAS, JMLR; was a recipient of several awards including NSF Career, Sloan, Carnegie Science Award, and best papers in conferences such as ACL, ISMB, NeurIPS, and OSDI; and is a fellow of several societies including AAAI, ACM, ASA, IEEE, IMS, and ISCB.

About the speaker Emma Brunskill

Emma is an Associate Professor (tenured) in the Computer Science Department at Stanford University, where she leads a research group developing AI systems that learn from few samples and make robust decisions, motivated by applications in healthcare and education. Her lab is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford, and she is an Associate Director of the Stanford Causal Science Center. Previously, she was an Assistant Professor at Carnegie Mellon University. Her work has been recognized with early-career awards from the National Science Foundation, the Office of Naval Research, and Microsoft Research, and has received multiple best paper nominations and awards across venues including UAI, CHI, EDM, LAK, RLDM, and ITS. She serves on the International Machine Learning Society Board and advisory and leadership committees including the Khan Academy Research Advisory Board and the Stanford Faculty Women’s Forum, and she was Co-Program Chair for ICML 2023.

About the speaker Taiji Suzuki

Taiji Suzuki is a Professor in the Department of Mathematical Informatics at The University of Tokyo and a researcher with RIKEN’s Center for Advanced Intelligence Project. His work sits at the intersection of machine learning and statistics, with a focus on statistical learning theory, deep learning theory, kernel methods, nonparametric convergence analysis, and optimization; particularly stochastic optimization and optimization for deep learning. Previously, he worked as an Assistant Professor in the Department of Mathematical Informatics, the University of Tokyo, and then he was an Associate Professor in the Department of Mathematical and Computing Science, Tokyo Institute of Technology. He has been a frequent instructor in international summer schools on deep learning theory and optimization, and served as area chairs of premier conferences such as NeurIPS, ICML, ICLR and AISTATS, a program chair of ACML2019, and an action editor of the Annals of Statistics. He received the Outstanding Paper Award at ICLR in 2021, the JSPS prize, the MEXT Young Scientists’ Prize, and Outstanding Achievement Award in 2017 from the Japan Statistical Society.