Liwei Huang
Principle Reliability Engineer at Microsoft
Redmond, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolLiwei Huang is a Principal Reliability Engineer at Microsoft with over a decade of hands-on experience in product-level reliability, accelerated stress testing, vibration/shock characterization, and FMEA. He combines deep materials science expertise from a PhD with practical skills in micro-characterization (SEM/TEM/XPS/AFM, neutron scattering, DMA, XRD, Raman, etc.) to root-cause complex failures across mechanical, electrical, and printed-organic device domains. Proficient in MatLab, LabVIEW, Java and Excel VBA, he builds instrumentation and data pipelines for automated acquisition and analysis, and has applied those tools to prototype organic transistors and device fabrication. Based in Redmond, he progressed through multiple reliability roles at Microsoft to lead reliability strategy, bringing a rare lab-to-production perspective that blends advanced microscopy with fielded product testing. He also contributes to open-source ML documentation, improving educational materials for the SpikingJelly spiking neural network project.
5 years of coding experience
20 years of employment as a software developer
Ph. D Materials Science and Engineering, Ph. D Materials Science and Engineering at Binghamton UniversityBS Physics, BS Physics at Peking University
Github Skills (6)
pytorch10
spiking-neural-networks10
documentation10
technical-writing10
deep-learning8
deeplearning-ai8
Programming languages (1)
Github contributions (5)
fangwei123456/spikingjelly
Aug 2020 - Sep 2020
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Role in this project:
Technical Writer
Technical WriterContributions:24 commits, 24 pushes in 27 days
Contributions summary:Liwei's primary contributions center around updating the documentation for the "SpikingJelly" project, specifically focusing on tutorials related to encoding in spiking neural networks. They've made numerous revisions to the `2_encoding.rst` file, adding and refining explanations, code examples, and mathematical formulas related to different encoding methods such as Periodic, Latency, Poisson, and Gaussian tuning curves. These changes indicate a focus on improving the clarity and completeness of the project's educational materials. Additionally, they fixed some equations in the source code comments.
pytorchspiking-neural-networksdeep-learningdvsmachine-learning
Grasshlw/SNN-Neural-Similarity-Movie
Jul 2023 - Dec 2024
Contributions:6 pushes, 2 branches in 1 year 4 months
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