Colin White
Research Scientist at Meta
United States
Summary
🤩
Rockstar🎓
Top SchoolColin White is an AI research scientist with 11 years of experience bridging rigorous theory and applied machine learning, currently working on advanced projects at Meta Superintelligence Labs. He holds a PhD from Carnegie Mellon and has led research teams and strategy as Head of Research and Distinguished Scientist at Abacus.AI, with postdoctoral work at Caltech. His expertise spans neural architecture search, scalable ML systems, and beyond-worst-case algorithmic analysis—work that blends deep theoretical insight with pragmatic engineering. Colin contributes to community tools like NASLib, improving robustness in popular NAS benchmarks and optimizers, reflecting a focus on reproducibility and practical impact. Known for mentoring and teaching throughout his career, he pairs academic rigor with product-oriented research to move ideas from papers into production.
11 years of coding experience
3 years of employment as a software developer
Bachelor of Arts - BA Mathematics and Computer Science, Bachelor of Arts - BA Mathematics and Computer Science at Amherst CollegeDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Carnegie Mellon University
Github Skills (9)
python10
nas10
neural-architecture-search10
forward-error-correction9
code-cleanup9
error-correction9
maintenance9
deep-learning8
machine-learning8
Programming languages (4)
Github contributions (5)
automl/NASLib
Nov 2020 - Aug 2022
NASLib is a Neural Architecture Search (NAS) library for facilitating NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces and optimizers.
Role in this project:
Software Engineer (Focus: Machine Learning and Neural Architecture Search)
Software Engineer (Focus: Machine Learning and Neural Architecture Search)Contributions:1 review, 320 commits, 30 PRs in 1 year 9 months
Contributions summary:Colin primarily contributed to the NASLib library, focusing on fixing typos and addressing minor bugs. The user's work included correcting errors in the Nasbench101 and Nasbench201 search spaces, and resolving an issue within the DARTS optimizer. Their commits demonstrate a focus on ensuring the accuracy and functionality of the library within the context of Neural Architecture Search. These changes, though small, contribute to the robustness and usability of the codebase for future users.
artnasneural-architecture-searchstate-of-the-artmachine-learning
realityengines/local_search
Jun 2020 - Nov 2020
Contributions:16 commits, 13 pushes in 4 months
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