Speakers

Elias Bareinboim

TITLE: TBD

Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence Lab at Columbia University. He obtained his Ph.D. under Judea Pearl at the University of California, Los Angeles. His research interests span artificial intelligence, machine learning, statistics, robotics, cognitive science, and the philosophy of science. His work focuses on the foundations of causal inference and its applications to artificial intelligence as well as to data science (including health and social sciences).

Hengrui Cai

TITLE: TBD

Hengrui Cai is an Assistant Professor in Statistics in the Donald Bren School of Information and Computer Sciences at University of California Irvine. She obtained her Ph.D. degree in Statistics at North Carolina State University (NCSU), co-advised by Dr. Wenbin Lu and Dr. Rui Song. Hengrui has broad research interests in methodology and theory in causal inference, reinforcement learning, graphical model, and their interchanges, to establish reliable, powerful, and interpretable solutions to wide real-world problems.

Biwei Huang

TITLE: Causality-Driven Advances for More Generalizable Reinforcement Learning

Biwei Huang is an Assistant Professor at Halicioğlu Data Science Institute (HDSI), UC San Diego (UCSD). Her research interests include Causal Discovery and Inference, Causality-Empowered ML/AI and Foundation Models, Multi-Agent Operating System, and Computational Science. Biwei received her Ph.D. degree at Carnegie Mellon University (CMU) in 2022 under the supervision of Prof. Kun Zhang and Prof. Clark Glymour.

Nathan Kallus

TITLE: TBD

Nathan Kallus is an Assistant Professor in the School of Operations Research and Information Engineering and at Cornell Tech, Cornell University. His research spans causal inference, robust and stochastic optimization, machine learning, personalization, sequential decision-making, and algorithmic fairness. He holds a Ph.D. in Operations Research from MIT and dual undergraduate degrees in Mathematics and Computer Science from UC Berkeley. Prior to joining Cornell, he was a Postdoctoral Associate at MIT and a Visiting Scholar at USC.

Peter Stone

TITLE: Minimal and Reusable Causal State Abstractions for Reinforcement Learning

Peter Stone is the founder and director of the Learning Agents Research Group (LARG) in the Artificial Intelligence Laboratory at the University of Texas at Austin, where he also serves as Associate Department Chair and Director of Texas Robotics. His research focuses on building complete intelligent agents with capabilities in adaptation, interaction, and embodiment, spanning areas such as machine learning, multiagent systems, and robotics. Perter's work bridges theory and application, with contributions to domains including robot soccer, autonomous bidding, self-driving vehicles, and human-interactive agents.

Junzhe Zhang

TITLE: TBD

Junzhe Zhang is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. Previously, he was a postdoctoral researcher in the Causal AI Lab at Columbia University, where he also completed his Ph.D. in Computer Science under the supervision of Prof. Elias Bareinboim. Junzhe's research lies at the intersection of causal inference, reinforcement learning, and machine learning, with a focus on robust decision-making under biased data collection and distribution shifts.

Kun Zhang

TITLE: TBD

Kun Zhang is an associate professor in the CMU philosophy department and an affiliate faculty member in the machine learning department. His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He develops methods for automated causal discovery from various kinds of data, investigates learning problems including transfer learning and deep learning from a causal view, and studies philosophical foundations of causation and machine learning. On the application side, he is interested in neuroscience, computational finance, and climate analysis.

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