Jonathan Shi
Founding Full Stack Software Engineer at AmpTrans
Riverside, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJonathan Shi is a founding full stack software engineer with nine years of practical engineering experience, now building products at AmpTrans while pursuing an MS in Computer Engineering at UC Riverside. He blends backend performance engineering—evidenced by substantive contributions to the high-profile Modin project that scale Pandas workflows—with hands-on research in physics-informed neural networks for controllable fluid simulation. Jonathan’s background spans microscopy hardware and algorithmic work (denoising/deconvolution in MATLAB) to tutoring and product-facing full-stack development, giving him a rare mix of experimental rigor and production sensibility. Based in Riverside, he brings an entrepreneurial mindset and a penchant for optimizing computational workflows, plus a candid sense of humor about maintaining separate personal and work identities online.
9 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Computer Engineering, Master of Science - MS, Computer Engineering at University of California, RiversideBachelor of Science, Applied Mathematics, Physics, Bachelor of Science, Applied Mathematics, Physics at University of California, Los Angeles
Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (12)
dataframes10
pandas10
performance-optimization10
python10
analytics10
data-science10
dataframe10
distributed-computing10
numpy9
benchmarking9
benchmark9
parallel-processing9
Programming languages (14)
Github contributions (5)
modin-project/modin
Jul 2022 - Dec 2022
Modin: Scale your Pandas workflows by changing a single line of code
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:176 reviews, 15 commits, 46 PRs in 4 months
Contributions summary:Jonathan contributed significantly to improving the performance and efficiency of the Modin library, specifically concerning the execution of Pandas workflows. Their commits focused on optimizing the parallel execution of computations, particularly in benchmark mode, and implementing new features, like `convert_dtypes` to enhance data type conversions across partitions. Furthermore, they contributed to error handling and improved the reliability of Modin's core functionalities, reflected in their fixes for issues like Series.duplicated and various binary operations.
analyticspythonline-of-codedata-sciencedataframe
noloerino/modin
Oct 2021 - Feb 2025
Modin: Speed up your Pandas workflows by changing a single line of code
Contributions:322 pushes, 67 branches in 3 years 4 months
pythonspeedline-of-codepython3workflows
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