Zhenyi Wang

Zhenyi Wang is a postdoctoral associate at the University of Maryland Institute for Advanced Computer Studies (UMIACS) working with Heng Huang. Wang’s research focuses on meta-learning and trustworthy machine learning, emphasizing uncertainty quantification and robustness to task distribution shifts, as well as data-free approaches. Wang also explores distributionally robust and corruption-robust continual learning, aiming to enhance the reliability and adaptability of machine learning systems in dynamic environments.