Robin Burke - Professor at University of Colorado Boulder

Robin Burke

Professor at University of Colorado Boulder

Louisville, Colorado, United States

Join Prog.AI to see contacts

Summary

👤

Senior

🎓

Top School

Robin Burke is a professor of information science and seasoned researcher with over two decades of academic experience focused on social networks, recommender systems, and fairness-aware, multistakeholder algorithms. He combines deep theoretical grounding (PhD Northwestern) with practical contributions to widely used tools—such as back-end improvements to the popular LibRec Java recommender library—bridging rigorous research and reproducible software. Based in Louisville, Colorado, he has held faculty roles at University of Colorado Boulder, DePaul, and visiting positions including a Fulbright at University College Dublin. His work often emphasizes case-based reasoning and measurable fairness, and he’s known for pragmatic fixes that improve evaluators and debugging (e.g., eliminating -Inf issues and enhancing iteration diagnostics).

10 years of coding experience

15 years of employment as a software developer

PhD, Computer Science, PhD, Computer Science at Northwestern University

MS, Computer Science, MS, Computer Science at Yale University

BS, Computer Science, BS, Computer Science at Harvey Mudd College

Github Skills (13)

javas10

matrix-factorization10

collaborative-filtering10

recommender-system10

java10

matrix8

data-structure7

data-structures7

algorithm7

algorithms7

sparse7

Programming languages (5)

JavaHTMLJupyter NotebookPythonNunjucks

Github contributions (5)

guoguibing/librec

Jan 2020 - Jun 2021

LibRec: A Leading Java Library for Recommender Systems, see

Role in this project:

Back-end Developer

Contributions:7 commits, 8 PRs, 6 comments in 1 year 5 months

Contributions summary:Robin made several contributions focused on improving the LibRec recommender system library. They implemented a new feature to avoid -Inf values within the Discounted Proportional Fairness evaluator and addressed a bug in the TextDataConvertor. Furthermore, the user added output to the RankALS recommender to help determine the required number of iterations, enhancing debugging capabilities. Additionally, the user simplified the statistical parity implementation.

tensor-factorizationhaikusparsesat-solvertensor

that-recsys-lab/librec-auto-sample

Nov 2019 - Nov 2020

Contributions:7 commits, 5 pushes, 1 branch in 1 year

Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.

Request Free Trial