Published multiple papers on core graph/relational learning methods, democratizing graph learning, graph-inspired machine learning, and interdisciplinary applications in crop yield prediction, drug discovery, recommender systems, financial transactions, and relational databases.
Research Experience
Built the first graph learning predictive system for relational databases as a core founding member at Kumo AI from 2021 to 2023.
Education
Received Ph.D. and M.S. degrees from the Department of Computer Science, Stanford University, advised by Prof. Jure Leskovec. Supported by JPMC PhD Fellowship and Baidu Scholarship during his PhD.
Background
Currently an Assistant Professor at UIUC CS, leading the U Lab. Research interests include deep learning for graphs, relational data, and databases. Also excited about knowledge-augmented LLMs and multi-modal foundation models.
Miscellany
Welcomes students to propose new directions and insights. Looking for self-motivated PhD students and remote intern students with strong ML or ML system backgrounds.