I am a postdoc in the Department of Mathematics, University of California, Irvine, working with Qing Nie. Previously, I was a postdoc at Michigan State University, supervised by Guo-Wei Wei. I received Ph.D. (2020) at University of California, Irvine.
My research use computational mathematics and data science to bridge the gap between intricate biological phenomena and their mathematical representation. Specifically, I build interpretable artificial intelligence (AI) models by developing and combining deep/machine learning, multiscale modeling, and topological data analysis (TDA) methods to understand fundamental biological mechanisms, such as gene regulation and cell communications, and design treatment to diseases. These topics span across diverse range of areas from molecular biology to cell and development biology.
- Data-driven modeling for spatio-temporal dynamics of single-cell omics data
- Graph neural networks
- Deep learning-based solver for high dimensional PDEs
- AI-aided protein designs
- Topological data analysis
- Deep protein language models (e.g., Transformer, NLP models)
- Bayesian optimization
- Multiscale modeling on embryonic spatial pattern formation
- Stochastic PDEs
- Subcellular element methods