About me

I am a Ph.D. student in Applied Mathematics and Computational Science supervised by Dr. Nat Trask at the University of Pennsylvania. I received my M.A. from the same program in 2023, and B.S. in Applied Mathematics and B.A. in Economics from the University of California, San Diego in 2021.

My broad research interests involve in scientific machine learning (SciML), AI for medicine, and federated learning, including theory, algorithms, and applications to real life problems.

I worked on the interdisciplinary methods across Physics-Informed Neural Networks (PINNs), operator learning and federated learning during my master period at UPenn, where I applied data-driven methods constrained by physical knowledge to solve partial differential equations (PDEs) or learn differential operators. I also worked on developing and applying machine learning strategies (SVM, transformers and other large language models), numerical methods and federated learning for analyzing medical data for one year. More recently, I’m working on structure-preserving methods in SciML.