Michael Waskom
Member Of Technical Staff at Modal
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Waskom is a research-driven software and machine learning engineer with 16 years of experience building reproducible scientific tools and data products in academia and industry. He has contributed core features and datasets to the widely used Seaborn visualization library, bridged neuroimaging tooling at NiPype, and improved computational neuroscience teaching materials for Neuromatch Academy—demonstrating fluency across visualization, scientific data processing, and research workflows. After a PhD from Stanford and research positions at NYU and the Simons Foundation, he applied that expertise to production ML work at Flatiron Health and now contributes as a Member of Technical Staff at Modal in New York. Colleagues know him for clarifying complex analyses into robust, well-tested code and for quietly improving usability in widely adopted open-source projects.
16 years of coding experience
12 years of employment as a software developer
Bachelor of Arts - BA, Bachelor of Arts - BA at Amherst CollegeDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Stanford University
Github Skills (17)
data-visualizations10
nipype10
seaborn10
python10
matplotlib10
pandas10
data-visualisation10
fs10
neuroimaging10
data-processing10
data-wrangling10
data-visualization10
documentation10
test-automation10
data-analysis10
Programming languages (12)
Github contributions (5)
mwaskom/seaborn
Jun 2012 - Jan 2023
Statistical data visualization in Python
Role in this project:
Data Scientist
Data ScientistContributions:31 releases, 102 reviews, 2763 commits in 10 years 9 months
Contributions summary:Michael contributed to the development of the seaborn library, focusing on improvements to its statistical visualization capabilities. Their work included implementing new features such as a text mark, refactoring existing code for improved efficiency, and enhancing functionalities like the integration with pandas for handling categorical variables. The commits also reflect efforts to improve the clarity and accuracy of the visualizations, including fixing potential issues with log scales and handling of missing data.
data-analysispythonstatisticsdata-sciencestatistical
mwaskom/seaborn-data
Jan 2014 - Jan 2023
Data repository for seaborn examples
Role in this project:Data Scientist
Data ScientistContributions:44 commits, 20 PRs, 25 pushes in 9 years 1 month
Contributions summary:Michael primarily contributed to adding and processing datasets related to various scientific and real-world phenomena for the seaborn-data repository. Their work involved creating Python scripts to transform raw data files (CSV format) into a processed format suitable for use with the seaborn visualization library. The datasets added ranged from demographic data (Titanic), to scientific data (exercise, attention, gammas, seaice), and real-world data (planets, flights, mpg, geyser, taxis, dowjones, healthexp). The user also made minor adjustments to existing datasets.
seaborndataframesdata-repositorydata-analysis
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