Chen Zhao (赵辰)

Biography

I am an Assistant Professor in the Department of Computer Science at Baylor University. Prior to joining Baylor, I was a senior R&D computer vision engineer at Kitware Inc. I earned my Ph.D. degree in Computer Science from the University of Texas at Dallas (UTD) in 2021, advised by Feng Chen. I received dual M.S. degrees in Computer Science and Biomedical Science from the State University of New York at Albany (UAlbany), and Albany Medical College in 2016. My publications have appeared in prestigious conferences and journals, including KDD, CVPR, WWW, AAAI, IJCAI, ICDM, TKDD, etc. I have served as a PC member for several international conferences and workshops, such as NeurIPS, KDD, AAAI, IJCAI, ICML, ICLR, etc.

Education

• Ph.D. (Computer Science), The University of Texas at Dallas, 2021.
• M.S. (Computer Science), The State University of New York at Albany, 2016.
• M.S. (Biomedical Science), Albany Medical College, 2016.

Selected Publications

For a full list of publications, see Publications.

• [AAAI'25] Metric-Agnostic Continual Learning for Sustainable Group Fairness.
  Heng Lian, Chen Zhao, Zhong Chen, Xingquan Zhu, My T. Thai, Yi He
  In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
• [AAAI'25] Multi-View Unsupervised Column Subset Selection via Combinatorial Search.
  Guihong Wan, Ninghui Hao, Crystal Maung, Haim Schweitzer, Chen Zhao, Kun-Hsing Yu, Yevgeniy Semenov
  In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
• [KDD'25] MLDGG: Meta-Learning for Domain Generalization on Graphs.
  Qin Tian, Chen Zhao, Minglai Shao, Wenjun Wang, Yujie Lin, Dong Li
  In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025
• [KDD'24] Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously.
  Chen Zhao^*, Kai Jiang^*, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Earl Grant, Feng Chen.
  In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024
• [IJCAI'24] Supervised Algorithmic Fairness in Distribution Shifts: A Survey.
  Minglai Shao^*, Dong Li^*, Chen Zhao^*, Xintao Wu, Yujie Lin, Qin Tian.
  In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024
• [IJCAI'24] Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments.
  Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen.
  In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024
• [KDD'23] Towards Fair Disentangled Online Learning for Changing Environments.
  Chen Zhao^*, Feng Mi^*, Xintao Wu, Kai Jiang, Latifur Khan, Christan Earl Grant, Feng Chen.
  In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
• [CVPR'23] Open Set Action Recognition via Multi-Label Evidential Learning.
  Chen Zhao, Dawei Du, Anthony Hoogs, Christopher Funk.
  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
• [KDD'22] Adaptive Fairness-Aware Online Meta-Learning for Changing Environments.
  Chen Zhao^*, Feng Mi^*, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen.
  In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
• [AAAI'22] A Nested Bi-level Optimization Framework for Robust Few-Shot Learning.
  Krishnateja Killamsetty, Changbin Li, Chen Zhao, Rishabh Krishnan Iyer, Feng Chen.
  In Proceedings of the AAAI Conference on Artificial Intelligence, 2022
• [KDD'21] Fairness-Aware Online Meta-learning.
  Chen Zhao, Feng Chen, Bhavani Thuraisingham.
  In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021
• [WWW'21] CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining.
  Zhuoyi Wang, Yuqiao Chen, Chen Zhao, Hemeng Tao, Yu Lin, Xujiang Zhao, Yigong Wang and Latifur Khan.
  In Proceedings of the ACM International World Wide Web Conference, 2021

Research

I am generally interested in machine learning, data mining, and trustworthy AI, with a primary focus on

• Fairness-aware Machine Learning;
• Uncertainty Quantification;
• Distribution Shift and Domain Generalization;
• Graph Learning;
• Computational Biology;
• Causal Learning and Structural Causal Mechanisms.

Awards & Honors

• Outstanding Reviewer, The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2025).
• Dissertation Research Award, $1,224, The University of Texas at Dallas, 2021-2022.
• Student Travel Award, IEEE International Conference on Data Mining (ICDM), U.S. National Science Foundation, 2020.
• First Prize in the 11th "Challenge Cup" National Undergraduate Academic Science and Technology, Tianjin Municipal Education Commission, Top 5% among all works in Tianjin City, 2011.