Chao Lan

I am an assistant professor in the School of Computer Science, Gallogly College of Engineering, at the University of Oklahoma (OU). I am also affiliated with the Data Science and Analytics Institute at OU.
I work on machine learning. My research explores those provable probabilistic natures of a learning task and exploits them to better understand or improve task efficiency and trustworthiness such as model fairness, data privacy and learner security. My current research focuses on randomized learning.
I received my Ph.D. degree from the University of Kansas. Here is my CV.

What's New
09/2023: I'll serve as a reviewer for ICLR'24.
08/2023: Shayan received the Gallogly College of Engineering Scholarship. Congratulations.
05/2023: I'm selected as a Top Reviewer for UAI'23.
05/2023: Yiting successfully defended her doctoral dissertation and graduated. Congratulations!
03/2023: I'll serve as an associate editor for ACM Transactions on Probabilistic Machine Learning (TOPML). It is a new journal dedicated to studies that exploit the probabilistic nature of problems.
02/2023: I'm selected as a Top Reviewer for AISTATS'23.
12/2022: Mohamed received the OU Engineering Research Fellowship. Congratulations.
10/2022: Invited talk at the Oklahoma Conference for Statistics, Biostatistics and Data Science.
05/2022: Paper accepted by UAI'22. Congratulations to Yiting.
05/2022: Paper accepted by ICML'22. Congratulations to Yiting.
05/2022: Mohamed received the OU Provost's Undergraduate Research and Creative Activity Fellowship. Congratulations.
04/2022: Paper accepted by IJCNN'22. Congratulations to Yiting.

Selected Publications
[ICML'22] Yiting Cao and Chao Lan. A model-agnostic randomized learning framework based on random hypothesis subspace sampling. in Proc. of the 39th International Conference on Machine Learning (ICML), 2022.
[UAI'22] Yiting Cao and Chao Lan. Active approximately metric-fair learning. in Proc. of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
[UAI'16] Chao Lan, Jianxin Wang and Jun Huan. Towards a theorectical understanding of negative transfer in collective matrix factorization. in Proc. of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), 2016.
Research Group
Shayan Shafaei (Ph.D. Student)
Luyuan Yang (Ph.D. Student)
Mohamed Abdelnaby (M.S. Student)