Chang Liu
Senior Software Engineer at NVIDIA
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChang Liu is a Senior Software Engineer and PhD candidate at UT Austin specializing in applied computational electromagnetics, with seven years of experience building high-performance EM solvers and scalable simulation tools for electronic packages and interconnects. He blends deep theoretical expertise in field and transmission-line theory with practical skills in parallel/fast algorithms, port de-embedding, and visualization workflows (HFSS/Sonnet/ParaView), enabling direct support for real design validation. At NVIDIA and previously Cadence, he has moved research-grade EM methods toward production-quality software, while contributing bug fixes to notable open-source projects such as dgl to keep examples correct and user-friendly. Known for meticulous, critical thinking about commercial tool use and performance, he pairs HPC proficiency with hands-on circuit lab teaching experience, making him equally comfortable with code, models, and measurements.
7 years of coding experience
9 years of employment as a software developer
Master's degree, Electrical and Electronics Engineering, Master's degree, Electrical and Electronics Engineering at The University of Texas at AustinBachelor of Engineering - BE, Electrical and Electronics Engineering, 3.7, Bachelor of Engineering - BE, Electrical and Electronics Engineering, 3.7 at University of Electronic Science and Technology of China
Github Skills (5)
pytorch10
debug10
deep-learning10
graph-neural-network10
python10
Programming languages (4)
Github contributions (5)
dmlc/dgl
Jun 2022 - Jan 2023
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Role in this project:
Back-end Developer & Bug Fixer
Back-end Developer & Bug FixerContributions:233 reviews, 32 commits, 58 PRs in 7 months
Contributions summary:Chang primarily focused on bug fixes within the `dgl` repository, specifically addressing issues in example cases. The user made changes to improve the functionality and correctness of various examples, like cluster-gat, GCMC, ogbn-proteins, ogbn-products, and dimenet. These bug fixes included removing unused code, correcting example implementations, and reverting incorrect changes, demonstrating a focus on maintaining the integrity and usability of the library's examples.
pytorchpythondeep-learningmachine-learninggraph-neural-networks
chang-l/dgl
May 2022 - Nov 2023
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Contributions:212 pushes, 65 branches in 1 year 6 months
pytorchpythondeep-learningmachine-learninggraph
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