I pursue interdisciplinary research at the interface of mathematics, physics, chemistry, biology, medicine, and the life sciences.
My work integrates data science, machine learning, and mathematical modeling with classical numerical analysis.
By combining theoretical rigor with computational innovation, I aim to develop high-performance algorithms and
analytical frameworks that address real-world scientific challenges—ultimately advancing the connection between scientific computing and human health.
My primary research interest is
Data-driven modeling and computation, which combines machine learning with partial differential equations and dynamical systems
to solve interdisciplinary problems such as the design of a digital twin for health.
Develop physics-informed learning to discover and solve partial differential equations as well as
structure-preserving deep learning methods during this process.
Design high-order energy stable numerical schemes for partial differential equations, especially the ones coupled with dynamical boundary conditions which are natural in many small scaled systems.
Community Activities
I co-organized the
ACM Student Seminar
at the Department of Mathematics at the University of South Carolina, 2021-2023
Please contact us if you'd like to give a talk!
Vice president at SIAM student chapter of UofSC, 09/2020-09/2022
Proctor for the annual USC High School Math contest, 2019 - 2020