Constrained Reinforcement Learning for Data Assimilation.
Multi-model Data Assimilation in Latent Spaces.
Integrating physical models with machine learning techniques to enable digital twins and interpretable machine learning.
High-order time integration for nonlinear dynamical problems.
Madison Experimental Mathematics Lab:
Project title: Integrating ML with DA for complex dynamical systems (Link )
In this project, I supervised four undergraduate students. We developed a framework using ML and DA that addresses critical challenges such as capturing intermittent behaviors and extreme events, often inadequately represented in traditional DA frameworks or physical modeling approaches.
In addition to my formal appointments, I have held visiting researcher positions at several internationally recognized institutions. These include the School of Computing at the Australian National University; the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin; the Department of Mathematics at the University of Nottingham; the Department of Mechanical Engineering at the Technical University of Eindhoven; and the Department of Mathematics at Monash University. These experiences have broadened my collaborative network and deepened my engagement with interdisciplinary research across applied mathematics, engineering, and computational sciences.