Daniel Kang
Research Scientist
Summary
🤩
RockstarDaniel Kang is an assistant professor at UIUC with 15 years of software engineering and research experience spanning industry internships at Google, Apple, and Dropbox and deep academic work at Stanford and Cambridge. He focuses on the intersection of machine learning analytics and zero-knowledge proofs (ZKML), bringing systems-level thinking from video codec and multimedia optimization contributions early in his career to privacy-preserving ML research today. Daniel combines rigorous PhD training with hands-on engineering—his past open-source and codec work involved low-level assembly and threading optimizations that hint at a penchant for performance-critical systems. Based in California, he bridges academia and production practice, translating theoretical advances into practical tools for scalable, private ML analytics.
15 years of coding experience
Github Skills (145)
benchmark10
python10
deep-learning10
gpu10
squad10
numpy10
neural-network10
benchmarking10
machine-learning10
tensor9
image-processing9
meter9
gpu-acceleration9
discord9
inference9
Programming languages (10)
Github contributions (5)
stanford-futuredata/blazeit
May 2018 - Jun 2021
Its BlazeIt because it's blazing fast
Contributions:23 commits, 2 PRs, 17 pushes in 3 years 1 month
blazing-fastblazing
stanford-futuredata/loa
Nov 2021 - Oct 2022
Public code for LOA
Contributions:10 commits, 1 PR, 8 pushes in 10 months
public-code
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