- 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
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