I am a Mechanical Engineering PhD student in Micro and Nano Mechanics Group at Stanford University. I was a Research Scientist Intern at Tokyo Electron working on computational modeling of semiconductor engineering. Previously, I got my MS in Mechanical Engineering from Cornell University and BS in Theoretical and Applied Mechanics from Shanghai University. I'm from Beijing.
I am interested in solving problems in mechanics of materials from atomistic to continuum scales. My research interests are combining computational mechanics, computational materials science, scientific machine learning, and inverse optimization for structural and materials modeling and design, with potential impact on energy, biotechnology, and advanced manufacturing.
2024: Collaboration with MSR AI4Science on optimization for materials design being published as a Featured Article in APL Materials.
2022: Poster for molecular dynamics of graphe fracture being presented at Molecular ML Conference.
Highlights
 Materials Discovery Benchmark: A comprehensive comparison of different inverse optimizations for materials discovery. |
 Bayesian Geometric Design: Anti-microbial nanosurfaces generated from digital twins & Bayesian optimization. |
 Optimal Experiment Design: Exploring inverse optimization of mechanical metamaterials for biofilm control. |
 Multiscale & Multiphysics Mechanics: A theoretical and data-driven understanding of scale-bridging for graphene fracture. |
Master's Thesis Defense
In this presentation, I highlight my previous research & works intersect in multiscale modeling, scientific computing, and design optimization during my studies at Cornell. These works roadmap future endeavors in developing predictive materials design and system evaluation tools via computational approaches. More details can be found in research.
Outreach & Fun
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