I work at the intersection of AI, geometry modeling and computational physics. One of my major application is cardiovascular biomechanics, developing next-generation tools for medical image segmentation, vascular geometry synthesis, and fast, accurate flow modeling. My research pushes the boundaries of image-based CFD by integrating deep learning, generative models, and differentiable solvers to make cardiovascular analysis more robust, scalable, and clinically impactful. I also explore uncertainty quantification and AI-driven design applications. If you’re interested in similar topics, feel free to reach out—I’m always excited to brainstorm ideas, exchange methods, and spark new collaborations!
- Postdoc in Mechanical Engineering, University of California, Berkeley
- Ph.D. in Aerospace and Mechanical Engineering, University of Notre Dame
- Master’s in Mechanical Engineering and Materials Science, Washington University in St. Louis
- Bachelor’s in Thermal Engineering, Tsinghua University
- Our GPU enabled solver "DiFVM" will be online soon.
- (Feb 2026) I will be joining Dr. Shawn Shadden's lab at University of California, Berkeley starting Feb 9.
- (Jan 2026) I will defend my Ph.D. dissertation at Jan26 2026 on the topic "From Medical Imaging to Hemodynamics: AI-Enabled Modeling of Cardiovascular Flow"
- (Nov 2025) I will be attending APS DFD as chair host for Biological Fluid Dynamics: Cardiac Flow II. I will also present our latest GPU-enabled solver titled "Differentiable Graph-Based Finite Volume Solver for Patient-Specific Cardiovascular Flow Simulation"
- (Sep 2025) I am entering the academic job market this year for faculty positions focused on scientific machine learning, computational fluid dynamics, and cardiovascular flow.
- (Jul 2025) I will be attending USNCCM18 on July 20 at Chicago! See you guys there!
- (Jul 2025) I will be teaching AME 40411: Introduction to Artificial Intelligence this fall at the University of Notre Dame.