Andrew Connolly
Director Of The EScience Institute at University of Washington
Seattle, Washington, United States
Summary
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Senior🎓
Top SchoolAndrew Connolly is a professor of astronomy and the Director of the eScience Institute at the University of Washington, combining 14+ years of academic leadership with deep expertise in statistics and machine learning for data-intensive astrophysics. He holds the William P. and Ruth Gerberding University Professorship and serves as Associate Vice Provost for Data Science, bridging research, education, and institutional strategy. An internationally recognized researcher, he has authored over 150 peer-reviewed papers with 30,000+ citations and co-wrote the award-winning book "Statistics, Data Mining and Machine Learning in Astronomy." He applies practical Bayesian methods—demonstrated by hands-on contributions to LSSTC data science materials and MCMC notebooks—to make advanced statistical tools accessible to the astronomy community. Based in Seattle and trained at Imperial College London (PhD), he is as comfortable leading large research programs as he is crafting reproducible code and teaching complex computational techniques.
14 years of coding experience
Doctor of Philosophy (Ph.D.), Physics and Astronomy, Doctor of Philosophy (Ph.D.), Physics and Astronomy at Imperial College London
Github Skills (9)
bayesian-statistics10
mcmc10
jupyter-notebook10
bayesian10
python10
data-analysis9
scipy8
matplotlib8
theano6
Programming languages (6)
Github contributions (5)
LSSTC-DSFP/LSSTC-DSFP-Sessions
Sep 2017 - Sep 2017
Lecture slides, Jupyter notebooks, and other material from the LSSTC Data Science Fellowship Program
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 1 PR in 1 day
Contributions summary:Andrew primarily contributed to Jupyter notebooks containing lecture slides, code, and examples focused on Markov Chain Monte Carlo (MCMC) methods and their applications. Their work involved providing a practical guide to MCMC, discussing Bayesian approaches, and demonstrating the use of PYMC3 for fitting distributions and data. Furthermore, they implemented code for testing MCMC techniques with animation and explored revisions and formatting of the notebook.
data-sciencejupyter-notebook
connolly/A302_2019
Dec 2018 - Jan 2019
Python for Astronomy
Contributions:21 commits, 2 PRs, 45 pushes in 28 days
python
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