Ian Bunner
PhD Candidate
Durham, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolIan Bunner is a PhD candidate in quantitative biology with a strong interdisciplinary foundation in computer science and linguistics, combining eight years of technical and teaching experience across academia and open-source. He contributes to meta-learning research as an ML engineer—adding reinforcement learning grid-world environments to the popular learn2learn PyTorch library—demonstrating practical RL implementation and framework integration. Previously he supported undergraduate computer science courses at USC, blending hands-on instruction, lab facilitation, and grading with research experience from Rohs Lab. Now based in Durham and pursuing advanced studies at NCSU after an MMath in Statistics from Waterloo, he brings rigorous mathematical training to applied ML problems. Colleagues value his ability to translate theoretical concepts into reproducible code and educational materials, and his background suggests a knack for building research-grade tooling that scales from classroom to open-source projects.
8 years of coding experience
1 year of employment as a software developer
Bachelor’s Degree, Quantitative Biology, Computer Science, Freshman, Bachelor’s Degree, Quantitative Biology, Computer Science, Freshman at University of Southern CaliforniaHigh School, High School at North Carolina School of Science and MathematicsMMath, Statistics, MMath, Statistics at University of Waterloo
Github Skills (7)
gymnasium10
openai-gym10
meta-learning10
pytorch10
python10
reinforcement-learning10
few-shot-learning9
Programming languages (2)
Github contributions (5)
learnables/learn2learn
Aug 2019 - Sep 2019
A PyTorch Library for Meta-learning Research
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 8 PRs, 22 pushes in 1 month
Contributions summary:Ian primarily contributed to the development of reinforcement learning environments, specifically for meta-learning research. They implemented a new grid world environment, including defining actions, states, and rewards, as well as integrating it into the existing framework. The user also integrated and tested new environments, and made updates to the environment interfaces to match standard protocols.
pytorchmeta-learningmetalearningmeta-optimizationdeep-learning
ian-bunner/.Config
Aug 2018 - Feb 2019
Contributions:6 pushes, 1 branch in 6 months
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