• 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)

  • Recent Teaching
  • Fall 2023. CS4033/5033: Machine Learning
  • Fall 2023. CS2413: Data Structure

  • Recent Service
  • Reviewer: ICLR'24, NeurIPS'22-23, UAI'23, AISTATS'23, ICML'22.
  • Associate Editor: ACM Transactions on Probabilistic Machine Learning

  • Honors
  • Top Reviewer, UAI, 2023
  • Top Reviewer, AISTATS, 2023
  • Top Reviewer, NeurIPS, 2022
  • Distinguished Lecture, NSF NRT@KU, 2021
  • Distinguished Paper Award, ACSAC, 2021
  • Top PC, AAAI. 2021
  • NSF CRII Award, 2019
  • UAI Scholarship, 2016
  • Robb Award, University of Kansas (KU), 2014, 2015
  • Data Science Summer Institute Scholarship, UIUC, 2012

  • Contact
  • Office: DEH 210
  • Phone: (405)325-5735