Justin Domke
Assistant Professor at University of Massachusetts Amherst
Greater Boston United States
Summary
👤
Senior🎓
Top SchoolJustin Domke is an assistant professor of computer science at UMass Amherst with over a decade of experience advancing probabilistic machine learning and Bayesian inference. His research blends rigorous statistical methods—Markov chain Monte Carlo, variational techniques, and probabilistic programming—with a practical interest in combining data-driven models and human expertise to improve predictions. Prior to academia he was a researcher at NICTA and holds a PhD in computer science from the University of Maryland and a physics BA, giving him a strong mix of theoretical and applied foundations. Based in Greater Boston, he is known for translating complex inference ideas into usable algorithms and tools that bridge research and real-world decision making.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of MarylandBachelor's degree, Physics, Bachelor's degree, Physics at Washington University in St. Louis
Github Skills (38)
compilation10
probabilistic10
bayesian-methods10
probabilistic-programming9
probabilistic-reasoning9
python9
machine-learning9
statistics9
cpu9
pyro9
diffraction9
gpu9
optimisation9
deep-learning9
bayesian-inference9
Programming languages (2)
Github contributions (5)
justindomke/marbl
Sep 2014 - Nov 2014
Contributions:44 commits, 15 comments, 1 issue in 2 months
justindomke/pangolin
Nov 2022 - Feb 2023
Contributions:26 commits, 84 pushes, 14 comments in 2 months
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