KEYNOTE SPEAKERS

Craig Knoblock

Keston Executive Director of the Information Sciences Institute at University of Southern California

About Craig Knoblock

Craig Knoblock is the Keston Executive Director of the Information Sciences Institute, Research Professor of both Computer Science and Spatial Sciences, and Vice Dean of Engineering at the University of Southern California.

He received his Bachelor of Science degree from Syracuse University and his Master’s and Ph.D. from Carnegie Mellon University in computer science. His research focuses on techniques for describing, acquiring, and exploiting the semantics of data. He has worked extensively on source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the web, and combining all of these techniques to build knowledge graphs.

He has published more than 400 journal articles, book chapters, and conference and workshop papers on these topics and has received 7 best paper awards on this work. Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association of Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE). He is also past President of the International Joint Conference on Artificial Intelligence (IJCAI) and winner of the Robert S. Engelmore Award.

Kun Zhang

Carnegie Mellon University

About Kun Zhang

Kun Zhang is a professor at Carnegie Mellon University (CMU), and he is also a visiting professor in and the acting chair of the machine learning department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

He has been actively developing methods for automated causal discovery from various kinds of data and investigating machine learning problems including transfer learning, representation learning, and reinforcement learning from a causal perspective. He has been frequently serving as a senior area chair, area chair, or senior program committee member for major conferences in machine learning or artificial intelligence, including UAI, NeurIPS, ICML, IJCAI, AISTATS, and ICLR.

He was a general & program co-chair of the first Conference on Causal Learning and Reasoning (CLeaR 2022), a program co-chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), and is a general co-chair of UAI 2023. He currently serves as an associate editor of JASA, JMLR, IEEE TPAMI, ACM Computing Surveys, etc.

Aja Huang

Senior Staff Research Scientist at Google DeepMind

About Aja Huang

Aja Huang is a Taiwanese computer scientist specializing in artificial intelligence. He's best known for his work at DeepMind, especially as a key member of the AlphaGo project.

He was among the lead authors of DeepMind's groundbreaking papers on AlphaGo Fan (2016) and AlphaGo Zero (2017), which explained how the AI mastered the game of Go.

During the historic matches where AlphaGo defeated Go world champions, Huang was the one who placed the stones on the board for AlphaGo. More recently, Huang was also a lead author of the AlphaTensor (2022) paper, published in the journal Nature.