Yueming Hao
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYueming Hao is a GPU and performance-focused ML engineer with 11 years of experience specializing in compiler-backed acceleration for deep learning workloads. Based in Menlo Park, he contributes to PyTorch—improving the TorchInductor compiler, Triton kernel integration, and profiling tooling—to boost correctness and runtime efficiency in one of the most widely used ML frameworks. He holds advanced CS training including PhD studies and brings deep expertise in computer architecture, HPC, and performance profiling that bridges research and production systems. His open-source work includes fixing subtle uint8 and buffer-reuse bugs and adding profiler integrations that reveal real-world emphasis on measurable speedups. Colleagues describe him as someone who turns low-level optimization insight into tangible, framework-level improvements that scale across GPU hardware.
11 years of coding experience
Doctor of Philosophy - PhD, CS, Doctor of Philosophy - PhD, CS at North Carolina State UniversityMaster of Engineering - MEng, CS, Master of Engineering - MEng, CS at Shandong UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at William & Mary
English
Github Skills (10)
pytorch10
machine-learning10
triton10
gpu10
performance-optimization10
python9
deep-learning9
cuda9
neural-network8
autograd7
Programming languages (15)
Github contributions (5)
pytorch/pytorch
Jul 2022 - Dec 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:166 reviews, 6 commits, 48 PRs in 4 months
Contributions summary:Yueming contributed to the PyTorch repository by addressing bugs and improving the performance of the TorchInductor compiler. They fixed a uint8 bug in torchinductor, corrected redundant kernel generation, and resolved an issue with buffer reuse. Further contributions include fixing a grid z bug for large grids, adding a device argument to a unit test, and integrating a profiler into the operatorbench. These changes indicate a focus on optimizing the performance and correctness of PyTorch's compilation and execution pipeline, especially concerning the Inductor compiler and its integration with Triton kernels.
pythongpu-accelerationdeep-learninggpunumpy
FindHao/CoolHosts
Jan 2015 - Aug 2017
Contributions:5 releases, 28 pushes, 5 tags in 2 years 8 months
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