Kaze Wong
AI Software Engineer at Self-employed
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolKaze Wong is an AI software engineer and astrophysicist with a decade of experience applying deep learning to gravitational-wave data and black hole population inference. After earning a PhD in Physics at Johns Hopkins, he helped pioneer use of neural methods in astrophysics, producing well-cited work and continuing research at the Flatiron Institute. He combines research-grade modeling with production-minded software engineering—contributing to high-performance open-source projects like a distributed symbolic regression library where he improved evaluation, differentiation, and constant-tracking features. Based in New York, he now builds robust AI products for clients while still communicating science through visualizations and films of astrophysical systems. Notably, his background blends hands-on detector and data analysis experience from undergraduate experiments with advanced ML techniques, giving him unusual breadth across experiment, theory, and software.
10 years of coding experience
2 years of employment as a software developer
Johns Hopkins UniversityThe Chinese University of Hong Kong (CUHK)
Japanese, Chinese, Chinese, English
Github Skills (7)
machine-learning10
julia10
evolutionary-algorithms9
data-science8
automl8
linear-algebra7
python7
Programming languages (11)
Github contributions (5)
MilesCranmer/SymbolicRegression.jl
Jul 2021 - Jul 2021
Distributed High-Performance Symbolic Regression in Julia
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 10 commits, 1 PR in 3 days
Contributions summary:Kaze made multiple contributions focused on enhancing the core functionality of the symbolic regression library. Their work included fixing bugs in the evaluation and derivative calculations, along with refactoring output parsing to improve efficiency. The user also added a feature to track constants within the symbolic equations and implemented reverse automatic differentiation techniques. The user's contributions indicate a focus on refining and extending the capabilities of the symbolic regression algorithms.
regressionsymbolic-computationsymbolic-regressionscimlmachine-learning
kazewong/flowMC
Nov 2021 - Jan 2023
Normalizing-flow enhanced sampling package for probabilistic inference in Jax
Contributions:21 releases, 25 reviews, 413 commits in 1 year 2 months
normalizing-flownormalizing-flowsstatistical-inferencenormalizingstat
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