Hyunsu Cho
Senior Systems Software Engineer at NVIDIA
Las Vegas, Nevada, United States
Summary
🤩
Rockstar🎓
Top SchoolPhilip Hyunsu Cho is a Machine Learning Engineer based in Los Angeles with nine years of experience and serves as a lead maintainer of XGBoost. He specializes in ML systems and algorithms, driving core performance improvements such as a histogram-based tree grower and loop-unrolling/dense-data optimizations to accelerate training. His open-source work spans model interchange (Treelite), compiler-level ops for sequence modeling in TVM, and GPU-accelerated tooling in cuML, demonstrating a rare full-stack fluency from parsers to accelerators. Philip pairs systems rigor with user-facing polish—he's improved data loaders (including NOEOL handling and CSV/LibSVM indexing/weight options) and contributed to educational ML content—making complex ML infrastructure both faster and more usable.
10 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Computer Science and Engineering, Master of Science - MS, Computer Science and Engineering at University of WashingtonBachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at Trinity College-Hartford
Github Skills (40)
markdown10
tvm10
gbm10
notebook10
xgboost10
c-language10
python10
technical-writing10
machine-learning10
ipython10
machine-learning-algorithms10
file-io10
data-parsing10
nvidia10
regression10
Programming languages (20)
Github contributions (5)
dmlc/treelite
Jul 2017 - Jan 2023
Universal model exchange and serialization format for decision tree forests
Role in this project:
Back-end Developer
Back-end DeveloperContributions:36 releases, 156 reviews, 547 commits in 5 years 7 months
Contributions summary:Hyunsu implemented front-end and in-memory representations, adding support for XGBoost and LightGBM formats. They focused on creating the initial architecture of the project, which included adding parser classes for xgboost.cc and implementing data structures for handling model formats. The work involved developing components for universal model exchange and serialization format, laying the groundwork for decision tree forest manipulation.
compilerdecision-treeensemblesdecision
learnml/machine-learning-specialization
Nov 2015 - Jan 2017
Role in this project:Data Scientist
Data ScientistContributions:119 commits, 27 PRs, 124 pushes in 1 year 2 months
Contributions summary:Hyunsu added blank IPython Notebook (ipynb) assignment files for the machine learning specialization course. These files provide a structure for students to complete assignments on topics such as ridge regression and LASSO. The files contain markdown and code cells, which guide students through the implementation and interpretation of these concepts within the specified course.
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