Research

I work at the intersection of applied mathematics, scientific machine learning, and biomedical modeling. My research develops structure-aware methods for predictive digital twins: models that combine mechanistic knowledge, data-driven learning, and reliable computation to infer individualized dynamics from multimodal data and support scientific prediction and decision-making.

Across applications, I integrate partial differential equations, physical laws such as energy dissipation and mass conservation, numerical analysis, physiological and pathological mechanisms, and uncertainty-aware inference. My long-term goal is to develop efficient, trustworthy, and interpretable digital twins for health.

Future Goals

My future research will advance hybrid digital twins for health through uncertainty-aware inference, structure-constrained discovery of unknown mechanisms, and fast operator-learning surrogates. In parallel, I will develop scalable and structure-preserving neural solvers for multiscale PDEs. Together, these directions aim to connect rigorous applied mathematics with consequential problems in health, medicine, and scientific computing.

Community Activities

• Mini-Symposium Lead Organizer (with Zengyan Zhang and Xiaofeng Xu), SIAM Conference on Computational Science and Engineering (CSE27) , Pittsburgh, Pennsylvania, U.S., February 22-26, 2027.
  Mini-symposium topic: Scientific Machine Learning Methods and High-Performance Computing for Complex PDEs and Applications (2 sessions, 8 talks).
• Mini-Symposium Lead Organizer (with Zengyan Zhang and Yanfang Liu), SIAM Conference on Mathematics of Data Science (MDS26) , Salt Lake City, Utah, U.S., November 16-20, 2026.
• I co-organized the ACM Student Seminar at the Department of Mathematics at the University of South Carolina, 2021-2023.
  
• Vice President, SIAM Student Chapter at the University of South Carolina September 2020-September 2022.
• Proctor, annual USC High School Math Contest, 2019-2020.