Abraham Flaxman
Professor at University of Washington
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolAbraham Flaxman is a professor and data scientist with 17 years of experience blending academic leadership at the University of Washington with practical health-metrics work at CareInsights. He applies rigorous statistical and probabilistic modeling to real-world problems, contributing to high-profile open-source projects such as pandas and PyMC where he improved percentile-based summaries and plotting/testing robustness. Based in Seattle, he bridges research and product: developing reproducible analysis methods in academia while translating them into actionable metrics for clinicians. His background includes training at MIT and Carnegie Mellon, and an often-overlooked strength is hands-on contributions to core library behavior and test suites that improve reliability for thousands of downstream users.
17 years of coding experience
15 years of employment as a software developer
Massachusetts Institute of TechnologyCarnegie Mellon University
Stackoverflow
21reputation
5kreached
2answers
0questions
Github Skills (21)
probabilistic-programming10
pytest10
python10
data-science10
testing10
pandas10
numpy10
mcmc10
bayesian10
bayesian-inference10
data-analysis10
matplotlib9
statistic9
statistics9
git8
Programming languages (11)
Github contributions (5)
pymc-devs/pymc
Jun 2011 - Jun 2015
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Data Scientist & QA Engineer
Data Scientist & QA EngineerContributions:9 commits, 3 PRs, 27 comments in 4 years
Contributions summary:Abraham's contributions primarily focused on improving the PyMC library's plotting and testing functionalities. They enhanced the `Matplot.py` module by incorporating autocorrelation plots and making figures save all subplots. The user also addressed a bug related to precision calculations in the `get_tau_sd` function and updated testing procedures to ensure all tests run successfully, even on networked drives. These commits involved updating existing testing suites and fixing issues identified during the testing phase.
bayesianprobabilistic-programmingpythonstatistical-analysisbayesian-inference
pandas-dev/pandas
Mar 2012 - Dec 2012
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:Data Scientist
Data ScientistContributions:20 commits, 2 comments in 8 months
Contributions summary:Abraham primarily contributed to enhancing the `describe` method in the pandas library for both Series and DataFrame objects. Their work involved adding the `percentile_width` parameter, allowing users to specify the desired uncertainty interval for percentile calculations. They also implemented tests to validate the new functionality and ensure correct percentile calculation. This involved modifying core DataFrame and Series methods and creating corresponding tests to verify functionality.
data-analysisdata-structuredata-structurespythonpandas
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