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 (173)
benchmark10
python10
deep-learning10
gpu10
numpy10
neural-network10
benchmarking10
machine-learning10
acceleration9
tensor9
computer-vision9
image-processing9
meter9
apollo-client9
gpu-acceleration9
Programming languages (9)
Github contributions (5)
stanford-futuredata/loa
Nov 2021 - Oct 2022
Public code for LOA
Contributions:10 commits, 1 PR, 8 pushes in 10 months
public-code
stanford-futuredata/noscope
Mar 2017 - Mar 2020
Accelerating network inference over video
Contributions:31 commits, 2 PRs, 23 pushes in 3 years
pytorchdeep-learninginferencemachine-learningvideo
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