Invited Talks

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.

Professor Eric P. Xing is the founding President and Professor at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), where he has led the University’s rapid growth in fundamental AI research and the recruitment of a world-class faculty. At MBZUAI, he has built a strong research platform aligned with national priorities while maintaining excellence in both basic research and translational R&D, and has helped establish broad partnerships across academia, industry, and government. A world-leading computer scientist, Professor Xing’s contributions span statistical machine learning and large-scale ML systems, including foundational work in distance metric learning, network and graphical models, and distributed machine learning. His recent efforts include advancing foundation and world models and open, reproducible large language models, as well as AI for science initiatives in computational biology and healthcare. He is a recipient of major early-career awards including the NSF CAREER Award and a Sloan Fellowship, and is a Fellow of AAAI, IEEE, and ASA, and an ACM Fellow (2023). Professor Xing holds PhDs from Rutgers University and UC Berkeley, and completed his undergraduate studies at Tsinghua University.

Daiji 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. He has been a frequent instructor in international summer schools on deep learning theory and optimization, and previously held faculty appointments at Tokyo Institute of Technology before joining the University of Tokyo.