Ph.D. Candidate in Computer Engineering
I am currently a fifth-year Ph.D. candidate in the Elmore Family School of Electrical and Computer Engineering at Purdue University, advised by Dr. Xiaoqian Wang. Before coming to Purdue, I acquired an M.A. in Mathematics at University of Wisconsin-Madison. Prior to that, I obtained my B.S. in Computational Mathematics at Nanjing University.
My research focuses on building trustworthy machine learning systems. I work on developing reliable ML models, with an emphasis on rigorous evaluations of Explainable AI methods and their real-world applications. Recently, I have also been investigating robustness issues arising from natural distribution shifts, such as spurious correlations, and their relationship to adversarial robustness. Beyond these areas, I maintain broad interests in tackling challenges across various domains of machine learning.
Wang, Y., Siskind, J. & Wang, X. (2024) Great Minds Think Alike: The Universal Convergence Trend of Input Salience. In The 38th Annual Conference on Neural Information Processing Systems.
Wang, Y., Hou, S., & Wang, X. (2021). Reinforcement learning‐based bird‐view automated vehicle control to avoid crossing traffic. Computer‐Aided Civil and Infrastructure Engineering, 36(7), 890-901.
Wang, Y. & Wang, X. (2022). A unified study of machine learning explanation evaluation metrics. arXiv preprint arXiv:2203.14265.
Email: wang4865@purdue.edu
LinkedIn: My Profile