Yonggan Fu
Research Scientist at NVIDIA
California, United States
Summary
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Rockstar🎓
Top SchoolYonggan Fu is a Research Scientist at NVIDIA Research with about eight years of experience at the intersection of computer science and applied physics, holding a PhD from Georgia Tech and doctoral work at Rice. He has a strong research internship pedigree including multiple stints at NVIDIA and Meta and focuses on full-stack optimization and software frameworks for cognitive systems. Based in California, he translates deep academic training into practical systems research that targets performance and deployment challenges for AI workloads. Notably, his trajectory includes SRC Research Scholars work bridging hardware-aware optimization with software frameworks, signaling a knack for end-to-end co-design rather than isolated algorithmic work.
8 years of coding experience
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Georgia Institute of TechnologyDoctor of Philosophy - PhD Electrical & Computer Engineering, Doctor of Philosophy - PhD Electrical & Computer Engineering at Rice UniversityBachelor of Science - BS Applied Physics & Computer Science, Bachelor of Science - BS Applied Physics & Computer Science at University of Science and Technology of China
Github Skills (45)
language-model10
generative-adversarial-network10
chatgpt9
pytorch9
rainbow9
quantile-regression9
neural-architecture-search9
foundation-models9
compression9
transformer8
cuda8
deep-learning8
robustness8
inference8
llm7
Programming languages (4)
Github contributions (5)
VITA-Group/AGD
Jul 2020 - Oct 2020
[ICML 2020] "AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks" by Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan (Celine) Lin, and Zhangyang (Atlas) Wang.
Contributions:8 commits, 10 pushes, 34 comments in 3 months
generative-adversarial-networkpytorchcompressionneural-architecture-search
GATECH-EIC/FracTrain
Dec 2020 - Feb 2022
[NeurIPS 2020] "FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training" by Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lin
Contributions:3 commits, 57 pushes, 1 branch in 1 year 1 month
pytorchefficient-traininglow-precision-training
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