News

When: March 6th @ 11am

Where: TMCB 1170

Speaker: Chen Zhao

Advances in Fairness and Robustness in Machine Learning Under Distribution Shifts

In today's dynamic environments, machine learning models are increasingly deployed in contexts where data distributions evolve over time, posing significant challenges for both fairness and robustness. This talk explores cutting-edge methodologies designed to address these challenges, with a focus on supervised fairness-aware learning under distribution shifts. I will present novel frameworks that disentangle domain-specific and semantic information, enabling models to generalize across domains while maintaining fairness. Additionally, the discussion will highlight the impact of various types of distribution shifts—such as covariate, label, and concept shifts—on model performance and fairness. By leveraging fairness-aware optimization techniques, we aim to mitigate biases in dynamic data environments, ensuring ethical and equitable decision-making. The insights shared in this talk are drawn from both theoretical developments and empirical validations, underscoring the importance of fairness in machine learning in real-world applications, including hiring, healthcare, and financial services.

Dr. Chen Zhao is an Assistant Professor in the Department of Computer Science at Baylor University. Prior to joining Baylor, he was a senior R\&D computer vision engineer at Kitware Inc. He earned his Ph.D. degree in Computer Science from the University of Texas at Dallas in 2021. His research focuses on machine learning, data mining, and trustworthy AI, particularly fairness-aware machine learning, novelty detection, uncertainty quantification and domain generalization. His publications have been accepted and published in premier conferences, including KDD, CVPR, IJCAI, AAAI, WWW, etc. Dr. Zhao served as program committee members of top international conferences, such as KDD, NeurIPS, IJCAI, ICML, AAAI, ICLR, etc. He has organized and chaired multiple workshops on topics of Ethical AI, Uncertainty Quantification, Distribution Shifts, and Trustworthy AI for Healthcare at KDD (2022, 2023, 2024), AAAI (2023), and IEEE BigData (2024). He serves as the co-chair of the Challenge Cup of the IEEE Bigdata 2024 conference and tutorial co-chair for the Pacific-Asia Conference on Knowledge Discovery and Data Mining 2025.