Ian Char
Senior Machine Learning Scientist 1 at Lila Sciences
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolIan Char is a Senior Machine Learning Scientist with 12 years of engineering and research experience, currently applying ML and uncertainty quantification expertise at Lila Sciences while pursuing a PhD in Machine Learning at Carnegie Mellon. He blends practical production work—shipping models and tooling in a startup environment—with deep academic grounding in applied mathematics and machine learning. Ian has a track record of improving developer-facing tooling, evidenced by substantial contributions to the open-source uncertainty-toolbox for predictive uncertainty, calibration, and visualization. His background includes internships at Google and Terra Bella where he built simulation and large-scale geospatial analysis pipelines, demonstrating an ability to translate complex data problems into reliable systems. Colleagues describe him as someone who connects rigorous probabilistic thinking with pragmatic engineering to make model uncertainty actionable in real-world products.
11 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Applied Mathematics, Master of Science - MS, Applied Mathematics at University of Colorado BoulderDoctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Carnegie Mellon University
Github Skills (10)
visualization10
uncertainty-quantification10
visualizations10
calibration10
python10
metric10
bayesian-network9
scikit8
scikit-learn8
documentation8
Programming languages (3)
Github contributions (5)
uncertainty-toolbox/uncertainty-toolbox
Oct 2020 - Aug 2022
Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization
Role in this project:
ML Engineer
ML EngineerContributions:30 reviews, 20 commits, 17 PRs in 1 year 9 months
Contributions summary:Ian contributed primarily to the development and maintenance of the `uncertainty-toolbox` Python package, focusing on predictive uncertainty quantification. Their commits include initial setup files, updates to package dependencies, and the addition of extensive documentation and type hints across multiple modules, particularly related to metrics, visualization, and calibration. These changes indicate an emphasis on refining the usability and documentation of the toolbox's functionalities.
pythonpython-toolboxpredictive-uncertaintyvisualizationsrecalibration
IanChar/InterviewPractice
May 2015 - Feb 2024
Contributions:12 pushes, 1 branch in 8 years 10 months
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