Summary
Yihui Ren is a Senior Computational Scientist and AI researcher leading a group at Brookhaven National Laboratory, focused on generative AI surrogates for scientific simulations, AI hardware co-design, and deep-learning based lossy compression to bridge simulation–experiment gaps. With a PhD in theoretical and mathematical physics and a decade of experience in high-performance computing, he blends first-principles quantitative modeling with practical software engineering (C++, Python, CUDA, MPI). He publishes actively in scientific venues and applies domain mapping techniques to mitigate domain shift in experimental workflows. A competitive programmer with top-percentile ratings on LeetCode and HackerRank, he brings algorithmic rigor to research problems and production-grade implementations. He is comfortable across the stack—from parallelized simulation code to PyTorch models—and often explores unconventional intersections like agent-based modeling informed by deep generative models. Based in Brookhaven, NY, he combines curiosity about physics and computing with a track record of shipping scalable, reproducible scientific software.
10 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Theoretical and Mathematical Physics, Doctor of Philosophy (Ph.D.) Theoretical and Mathematical Physics at University of Notre Dame
Bachelor's Degree Applied Physics, Bachelor's Degree Applied Physics at Hunan University
Chinese, English