Michael Wood-vasey
Professor at University of Pittsburgh
Greater Pittsburgh Region United States
Summary
👤
Senior🎓
Top SchoolMichael Wood-vasey is a Professor of Physics and Astronomy at the University of Pittsburgh with over a decade of experience translating large astronomical datasets into scientific discovery, particularly through leadership on LSST and supernova cosmology. He combines hands-on software and data-science expertise—evidenced by significant contributions to the core astropy library optimizing cosmological calculations—with a deep commitment to building people and infrastructure that scale research impact. A dedicated teacher and mentor, he focuses on developing the next generation’s technical skills and scientific judgment so they can chart their own paths. Based in the Greater Pittsburgh region, he blends rigorous academic training (PhD Berkeley, BS Harvey Mudd) with practical engineering that makes complex cosmology computations both faster and more robust.
12 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at University of California, BerkeleyBachelor of Science - BS, Physics and Math, Bachelor of Science - BS, Physics and Math at Harvey Mudd College
Stackoverflow
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0reached
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Github Skills (11)
astropy10
scipy10
cosmology10
python10
numpy10
astronomy9
data-structure9
astrophysics9
algorithm9
data-structures9
algorithms9
Programming languages (14)
Github contributions (5)
astropy/astropy
Jan 2018 - Jul 2018
Astronomy and astrophysics core library
Role in this project:
Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:51 commits, 6 PRs, 37 comments in 6 months
Contributions summary:Michael significantly contributed to the `astropy/astropy` repository, focusing on improving the `FlatLambdaCDM` cosmology model. They implemented optimizations using elliptic integrals and hypergeometric functions for distance and age calculations, leading to substantial performance improvements. Furthermore, the user added special case handling for critical cosmological parameters, ensuring the model's accuracy. The user also focused on ensuring the code's robustness by adding tests and incorporating references for the implemented solutions.
astrologypythonscienceastrophysicsastrodynamics
lsst/validate_drp
Jan 2016 - Feb 2020
Validate an output data repository against LSST Science Requirements Document Key Performance Metrics.
Contributions:94 PRs, 438 pushes, 98 branches in 4 years 1 month
lsstdata-repositoryscienceperformance-metricsperformance
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