Ben Chugg is a PhD candidate in Machine Learning at Carnegie Mellon with a decade of research and applied experience across academia and policy-focused labs. He’s led and contributed to algorithmic and regulatory research at Stanford RegLab and RegLab @ Stanford, and has international research stints at RIKEN and the American University of Beirut. Trained in rigorous math and CS at Oxford and UBC, his work spans theoretical computer science, discrete optimization, and molecular computation through to practical ML applications. Based in the San Francisco Bay Area, he combines deep technical grounding with a knack for translating complex research into actionable insights for regulators and practitioners. Colleagues describe him as patient and precise—he appreciates Hofstadter’s Law and designs projects with realistic timelines. Beyond publications, he founded a student coding nonprofit and has a track record of teaching and mentoring in algorithms and CS.
10 years of coding experience
2 years of employment as a software developer
Master's degree, Mathematics and Foundations of Computer Science, Master's degree, Mathematics and Foundations of Computer Science at University of Oxford
Bachelor of Science (B.Sc.) (Hons), Mathematics and Computer Science, Bachelor of Science (B.Sc.) (Hons), Mathematics and Computer Science at The University of British Columbia
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Carnegie Mellon University
Contributions:100 commits, 14 PRs, 80 pushes in 1 year 5 months
ruby-on-railsrailsruby-onruby
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