Kathleen Champion
Machine Learning Engineer at Stripe
Bellingham, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolKathleen Champion is a machine learning engineer with a decade of experience translating applied mathematics research into production ML systems, currently at Stripe after roles as Senior Applied Scientist and Applied Scientist at Amazon. She holds a PhD in Applied Mathematics from the University of Washington and spent her doctoral work developing hybrid ML approaches—combining neural networks and sparse regression—to model complex physical dynamical systems from data. Kathleen is an active contributor to the pysindy open-source project, having implemented core optimization algorithms (STLSQ, SR3, LASSO, ElasticNet) and multi-trajectory fitting, reflecting deep expertise in sparse identification of nonlinear dynamics. Her background spans academia and industry, including postdoctoral research and participation in IPAM’s Machine Learning for Physics program, which informs her knack for turning physics-informed models into scalable, production-ready tools.
10 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Applied Mathematics, Doctor of Philosophy (Ph.D.), Applied Mathematics at University of WashingtonBachelor of Arts (B.A.), Mathematics, Bachelor of Arts (B.A.), Mathematics at Dartmouth College
Github Skills (5)
machine-learning10
python10
scikit-learn9
scikit9
mathematical-optimization9
Programming languages (1)
Github contributions (5)
dynamicslab/pysindy
Oct 2019 - Aug 2020
A package for the sparse identification of nonlinear dynamical systems from data
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:4 reviews, 120 commits, 23 PRs in 9 months
Contributions summary:Kathleen primarily contributed to the core functionalities of the pysindy package by implementing new optimization algorithms (STLSQ, SR3, LASSO, ElasticNet) for the sparse identification of dynamical systems. They added the capability of fitting and predicting with multiple trajectories. Additionally, the user worked on refactoring and updating code to align with the changes in core classes. The user also updated the model to support discrete time systems.
nonlinear-dynamicsnonlinearstructural-engineeringsparse-regressiondynamical-systems
kpchamp/widefield
Apr 2016 - May 2017
Contributions:496 pushes, 1 branch in 1 year
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