About me
Georgia Tech · Computational Science & Engineering
Learning dynamics.
Building digital twins.
Data-driven methods that connect sparse observations to fast, reliable predictions of complex physical systems.
About me
Nice to meet you! I’m a third-year Ph.D. student in Computational Science and Engineering at Georgia Tech, advised by Prof. Peng Chen. Following the idea of reduced-order modeling, I am interested in learning latent dynamics in a low-dimensional latent space for dynamical systems and real-world problems. The ultimate goal is to achieve fast and accurate prediction for these systems, i.e., reliable digital twins, that benefit a number of related tasks.
I got my B.S. and B.Eng. from Tsinghua University, majoring in Mathematics and Physics + Energy and Power Engineering. I was honored to be advised by Prof. Xingjian Wang from Department of Energy and Power Engineering at Tsinghua University. Feel free to contact me via social media or e-mail if you are interested in working with me or just want to make friends.
Latest research
All publications
C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories
An observation-driven digital twin that turns sparse stream-gauge histories, terrain, and rainfall into basin-wide flood predictions.