Chao Lan

Chao Lan is an Assistant Professor at the School of Computer Science, University of Oklahoma. His research focuses on Machine Learning, particularly in fairness, multi-view learning, kernel methods, and privacy-preserving techniques. He holds a Ph.D. in Computer Science from the University of Kansas, and M.S. and B.S. degrees from Nanjing University of Posts and Telecommunication, China.

Key professional roles include Area Chair for ICML'25 and NeurIPS'24, and Senior Program Committee member for PAKDD'25. He serves as an Associate Editor for ACM Transactions on Probabilistic Machine Learning (2024–present) and has received recognition as a Top Reviewer for multiple conferences.

• Education:
• Ph.D., Computer Science, University of Kansas
• M.S., Computer Science, Nanjing University of Posts and Telecommunication, China
• B.S., Computer Science, Nanjing University of Posts and Telecommunication, China

His research emphasizes theoretical and applied machine learning, including fairness-aware algorithms, efficient kernel methods, and distributed systems with privacy guarantees. Recent work explores randomized learning frameworks and debiasing techniques in semi-supervised and multi-view contexts.

Lauded for contributions, he has earned awards such as the NSF CRII Award (2019), Distinguished Paper Award at ACSAC 2021, and multiple scholarship recognitions. His publications span top venues like ICML, NeurIPS, KDD, and IJCAI.