Yadong Zeng is a researcher and software engineer with eight years' experience specializing in CFD, multiphysics simulation, and ML-enhanced modeling of complex fluid and electrodynamic systems. He holds a Ph.D. in Mechanical Engineering from the University of Minnesota and is currently a researcher at UT Austin, after internships and visiting roles at Berkeley Lab where he delivered GPU-accelerated hybrid MPI/CUDA codes achieving ~17x speedups. His work spans large C++/Fortran codebases (including a 50k+ LOC adaptive level set framework) and coupling of immersed boundary and particle collision models for fluid-structure interaction. Comfortable moving research into performant software, he has applied uncertainty quantification and advanced numerical methods to next-generation electronics and two-phase flow problems. Based in Albany, NY, he blends deep academic training with practical high-performance computing experience and an affinity for both CFD and machine learning. Colleagues note he’s self-described “lazy” in a productive way—favoring elegant, efficient solutions over unnecessary complexity.
8 years of coding experience
1 year of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at The University of Texas at Austin
Doctor of Philosophy - PhD, Mechanical Engineering, Doctor of Philosophy - PhD, Mechanical Engineering at University of Minnesota
Bachelor's degree, Naval Architecture, Civil, and Ocean Engineering, Top 1%, Bachelor's degree, Naval Architecture, Civil, and Ocean Engineering, Top 1% at Shanghai Jiao Tong University
Contributions:2 PRs, 130 pushes, 10 branches in 2 months
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Yadong Zeng - Researcher at The University of Texas at Austin