Nai Chiang is a computational mathematician with a decade of experience developing high-performance optimization algorithms and solvers for large-scale programming at national labs and industry. Currently at Lawrence Livermore National Laboratory, Nai applies expertise in HPC, energy infrastructure modeling, and machine learning to drive scalable, production-ready optimization tools. Prior roles at Argonne National Laboratory and United Technologies Research Center reflect a strong track record translating academic research into engineered solutions for complex systems. Nai holds a PhD in Operational Research and Optimization from the University of Edinburgh and combines rigorous theory with practical engineering to tackle real-world resilience and efficiency challenges. Colleagues note Nai’s ability to bridge deep mathematical insight with hands-on code optimization—making algorithms run faster on modern supercomputers without sacrificing correctness.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD), Operational Research and Optimization, Doctor of Philosophy (PhD), Operational Research and Optimization at The University of Edinburgh
Bachelor of Science (BS), Mathematics and Computer Science, Bachelor of Science (BS), Mathematics and Computer Science at Fudan University
A flexible package manager that supports multiple versions, configurations, platforms, and compilers.
Contributions:2 branches in 10 months
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