Ian Fox
Research Scientist at Meta
Shrewsbury, England, United States
Summary
🤩
Rockstar🎓
Top SchoolIan Fox is a research scientist with 11 years of experience applying machine learning to real-world problems across healthtech and recommender systems. At Meta he develops computer vision and signal-processing algorithms for mobile cardiovascular monitoring and previously built RL and offline-evaluation techniques for content ranking and notifications, contributing to the open-source ReAgent platform. His PhD work at the University of Michigan produced published advances in modeling and control of physiological time series, including deep RL for an artificial pancreas, and he has applied similar techniques to sports analytics. Ian combines strong theoretical foundations (Math/CS degrees with near-perfect GPAs) with production-minded engineering—refactoring core ReAgent trainers to PyTorch Lightning, adding CRR support and robust evaluation tests—bringing research into reliable, deployable systems. Based in Shrewsbury, he blends academic rigor with practical impact on health and large-scale recommendation problems.
11 years of coding experience
5 years of employment as a software developer
Master's degree, Computer Science, GPA 3.95, Master's degree, Computer Science, GPA 3.95 at University of MichiganBachelor's degree, Mathematics and Computer Science, GPA 3.99, Bachelor's degree, Mathematics and Computer Science, GPA 3.99 at University of Massachusetts Amherst
Github Skills (8)
dqn10
machine-learning10
pytorch10
pytorch-lightning10
tdd10
python10
reinforcement-learning10
unit-testing8
Programming languages (2)
Github contributions (5)
facebookresearch/ReAgent
Oct 2020 - Sep 2021
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
Role in this project:
ML Engineer
ML EngineerContributions:24 commits, 17 PRs, 1 branch in 11 months
Contributions summary:Ian's primary contribution focused on refactoring and converting existing TD3 and DQN trainers within the ReAgent framework to utilize PyTorch Lightning. They introduced new reporter classes and modified existing code to integrate with the new Lightning modules. Further contributions include adding a CRR trainer, modifying the model registration process, fixing bugs related to the Evaluator and reward boosts within the DQN and QR-DQN trainers, and adding unit tests.
reinforcement-learningcontextualbanditscontextual-banditsreinforcement
igfox/multi-output-glucose-forecasting
May 2018 - Dec 2021
Contributions:9 commits, 5 pushes, 1 branch in 3 years 6 months
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