Kenny Choo
Algorithm Developer at Hudson River Trading
Singapore, Singapore
Summary
🎓
Top SchoolKenny Choo is an algorithm developer and computational physicist with eight years of experience applying machine learning to many-body quantum systems and numerical simulations in condensed matter physics. He holds a PhD from the University of Zurich and has combined academic research (PhD, teaching, Simons Foundation work) with industry roles at IBM and Hudson River Trading, bringing research-grade rigor to production algorithm development. Kenny contributed to NetKet—an influential open-source library for ML applied to quantum systems—implementing FFNN layers, initialization and unit tests, underscoring his strength in reliable, testable ML infrastructure. Based in Singapore, he bridges quantum algorithms and practical engineering, with a track record of turning complex physics problems into maintainable software. An often-overlooked asset is his teaching experience in Python/C++ and ML for physics, which sharpens his ability to communicate complex algorithms to diverse teams.
7 years of coding experience
1 year of employment as a software developer
St Andrew's Junior CollegeDoctor of Philosophy - PhD, Condensed Matter Physics, Distinction, Doctor of Philosophy - PhD, Condensed Matter Physics, Distinction at University of ZurichBachelor's degree, Physics, First Class, Bachelor's degree, Physics, First Class at Imperial College LondonMaster's degree, Physics, Distinction, Master's degree, Physics, Distinction at ETH Zurich
English, Chinese
Github Skills (7)
neural-network10
machine-learning10
jax10
python10
deeplearning-ai9
deep-learning9
tensorflow8
Programming languages (4)
Github contributions (5)
netket/netket
Jun 2018 - Apr 2020
Machine learning algorithms for many-body quantum systems
Role in this project:
ML Engineer
ML EngineerContributions:141 commits, 27 PRs, 14 pushes in 1 year 10 months
Contributions summary:Kenny primarily contributed to the implementation of feedforward neural networks (FFNNs) and associated layer initialisation with JSON configuration within the NetKet framework, focusing on improvements and new functionality. Key changes involve refactoring of the `LogVal` function, incorporating activation functions, and the addition of unit testing to validate layer functionality. The modifications reflect a focus on expanding the library's capabilities for machine learning applications, specifically related to many-body quantum systems.
markov-chain-monte-carlovariational-monte-carlomonte-carlo-methodsquantum-computingvariational-method
kchoo1118/netket
May 2018 - Dec 2021
Machine learning algorithms for many-body quantum systems
Contributions:2 PRs, 177 pushes, 34 branches in 3 years 7 months
quantum-computingmachine-learning-algorithmsquantum-many-bodybodylearning-algorithms
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