Dustin Lang
Computational Scientist
Waterloo, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolDustin Lang is a computational scientist and astronomer with 18 years of experience building scalable analysis pipelines for billion-object sky surveys and contributing to flagship projects like DESI, CHIME/FRB, and CHORD. He combines a PhD-level computer science background with hands-on research at Perimeter Institute and prior postdoctoral roles at Princeton and CMU to deliver robust image analysis, probabilistic inference, and generative-model workflows for large astrophysical datasets. A practical coder and open-source contributor, he has improved core scientific tools such as the widely used emcee MCMC library, adding multiprocessing and sampler initialization features that boost real-world performance. Dustin excels at finding unexpected signals in massive data, repurposing datasets beyond their original design, and translating statistical rigor into production-ready software. He splits time between advancing cosmology research and enabling researchers to leverage advanced computing resources, and—less obviously—has an abiding talent for caffeinating large collaborations through coffee-fueled persistence.
18 years of coding experience
6 years of employment as a software developer
Master of Science (M.Sc.), Computer Science, Master of Science (M.Sc.), Computer Science at University of British ColumbiaDoctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at University of Toronto
Github Skills (6)
mcmc10
multiprocessing10
multi-process10
python-multiprocessing10
python10
numpy8
Programming languages (18)
Github contributions (5)
dfm/emcee
Sep 2011 - Apr 2012
The Python ensemble sampling toolkit for affine-invariant MCMC
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 5 comments, 1 issue in 6 months
Contributions summary:Dustin contributed primarily to the back-end functionality of the `emcee` library, a Python ensemble sampling toolkit. Their work involved enhancing the core `EnsembleSampler` class, including adding a `pool` option for multiprocessing and introducing a static method for walker initialization. They also addressed bugs related to multiprocessing and made general code improvements, showing a focus on improving the library's capabilities and efficiency. The contributions suggest a strong focus on the mathematical and computational aspects of the library, crucial for its intended use in probabilistic data analysis.
mcmcpythonmcmc-samplerprobabilistic-data-analysis
legacysurvey/obsbot
Nov 2015 - Apr 2026
Script to plan subsequent Mosaic and DECam observations based on current conditions
Contributions:12 PRs, 780 pushes, 30 branches in 10 years 6 months
planmosaicobservationssubsequentconditions
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