Invited Speakers

Nathan Kallus is an Associate Professor in the School of Operations Research and Information Engineering and Cornell Tech at Cornell University. His research interests include personalization; optimization, especially under uncertainty; causal inference; sequential decision-making; credible and robust inference; and algorithmic fairness. He has received the INFORMS 2018 Data Mining Best Paper Award and the 2013 MIT Operations Research Center Best Student Paper Award.

Kun Zhang is currently an associate professor in Department of Philosophy at Carnegie Mellon University and a Professor of Machine Learning at Mohamed bin Zayed University of Artificial Intelligence. His research on causal discovery and inference spans theory and applications. He serves as an Associate Editor of ACM Computing Surveys and Pattern Recognition. He also co-organized multiple workshops at UAI, NeurIPS, ICCV, ACM SIGKDD and SDM. He was a program co-chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), and a general co-chair of UAI 2023, and is a \textbf{program co-chair of ICDM 2024}.

Jun Wang is a Chair Professor in the Department of Computer Science at University College London and the Founding Director of MSc Web Science and Big Data Analytics. His primary research areas are in the theory and applications of AI decision-making and reinforcement learning. He has published over 200 research papers and awarded 8 times best paper awards and nominations at international conferences (including SIGIR Test of Time Award Honourable Mention, 2021; AAMAS 2021 Best Paper; CORL 2020 Best Paper award; and SIGIR Best Paper Award Honourable Mention, 2017). He served as a co-chair of the Artificial Intelligence, Semantics, and Dialog track in ACM SIGIR 2018.