I am a Ph.D. candidate at the University of Notre Dame, working under the supervision of Professor Jian-Xun Wang. My research centers on the intersection of Deep Learning (DL) and Physics, with a particular focus on modeling and controlling spatiotemporal dynamics governed by Partial Differential Equations (PDEs).

I believe that the fundamental differences between PDE-governed systems and tasks in Natural Language Processing (NLP) or Computer Vision (CV) necessitate the development of specialized network architectures. These systems require tailored approaches that go beyond conventional networks optimized for NLP or CV. This conviction drives my research into both DL algorithms and traditional numerical methods for PDEs, aiming to explore the unique connections and distinctions between these “trendy” networks and classical numerical techniques. Throughout my PhD, I have explored numerous strategies to incorporate physics-based priors into DL architectures, improving both performance and efficiency. The deeper I delve into this field, the more convinced I become of the potential—and the necessity—of integrating physical principles into DL. On the other hand, emerging DL methods such as diffusion models, which share deep connections with physical processes, further suggest that leveraging physics can unlock even greater potential for deep learning approaches.

In addition to my research, I am proud to have individually set up and maintained CoMSAIL, a high-performance GPU cluster for my research group at Notre Dame. This highly scalable system, equipped with a distributed file system, has grown seamlessly from a single workstation to an 8-node cluster. Over the past three years, it has supported more than 30 researchers, enabling advanced computational work across various projects.

Education

• Ph.D. in Mechanical Engineering, University of Notre Dame, May 2025
• B.S. in Energy and Power Engineering, Xi’an Jiaotong University, China, 2019