Willie Neiswanger
Assistant Professor Of Computer Science at University of Southern California
Los Angeles, California, United States
Summary
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Rockstar🎓
Top SchoolWillie Neiswanger is an Assistant Professor of Computer Science at USC Viterbi with 14 years of experience bridging machine learning research and decision-making under uncertainty. Trained at Carnegie Mellon (PhD) and with postdoctoral stints at Stanford and CMU, he focuses on probabilistic modeling, uncertainty quantification, and generative AI, bringing rigorous statistical foundations to practical ML systems. His open-source contributions include visualization and calibration metrics for the widely used Uncertainty Toolbox, improving how predictive uncertainty is evaluated and communicated. Based in Los Angeles, he blends academic leadership and hands-on tooling work to help teams make better, more calibrated decisions from ML models.
14 years of coding experience
4 years of employment as a software developer
Bachelor of Science Applied Mathematics / Computer Science, Bachelor of Science Applied Mathematics / Computer Science at Columbia EngineeringDoctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Carnegie Mellon UniversityPostdoc, Postdoc at Stanford University
Stackoverflow
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Github Skills (5)
visualization10
calibration10
visualizations10
python10
metric10
Programming languages (5)
Github contributions (5)
uncertainty-toolbox/uncertainty-toolbox
Sep 2020 - Jan 2023
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Role in this project:
Data Scientist
Data ScientistContributions:34 reviews, 85 commits, 49 PRs in 2 years 4 months
Contributions summary:Willie primarily contributed to the development of visualization tools and metrics for evaluating predictive uncertainty within the Uncertainty Toolbox. Their work focused on creating functions for plotting prediction intervals, calibration plots, and sharpness plots. The user also implemented and refined metrics such as the mean absolute calibration error, and the miscalibration area. The contributions enhance the toolbox's ability to analyze and visualize the performance of uncertainty quantification methods.
pythonpython-toolboxpredictive-uncertaintyvisualizationsrecalibration
realworldml/icml2022
Feb 2022 - Jan 2023
Contributions:44 commits, 33 pushes in 10 months
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