Jason Fries
Assistant Professor at Snorkel AI
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJason Fries is an assistant professor and AI researcher who blends computer science, medical informatics, and hospital systems to develop and evaluate foundation models trained on longitudinal EHR data. With 13 years of experience spanning postdoc to research scientist roles at Stanford and consulting for Snorkel AI, he focuses on reproducibility in healthcare AI by releasing novel EHR datasets (INSPECT, EHRSHOT, MedAlign, FactEHR) and publishing models on Hugging Face. He brings practical engineering chops—contributing to core pieces of the influential Snorkel weak-supervision framework—alongside deep domain expertise in clinical NLP and health surveillance. Based in Palo Alto, he pairs academic rigor with open-source impact to move models from real-world hospital data to standardized evaluation benchmarks.
13 years of coding experience
17 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at University of IowaPostdoctoral Fellowship Computer Science, Postdoctoral Fellowship Computer Science at Stanford University
German
Github Skills (6)
data-labeling10
machine-learning10
python10
snorkel10
data-science9
data-augmentation8
Programming languages (5)
Github contributions (5)
snorkel-team/snorkel
Apr 2016 - Jul 2018
A system for quickly generating training data with weak supervision
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:83 commits, 14 PRs, 234 pushes in 2 years 2 months
Contributions summary:Jason's contributions primarily involve modifications to the `ddlite.py` and `__init__.py` files, suggesting a focus on the core functionalities of the Snorkel framework. These changes include fixing module initialization issues and merging branches, pointing to code integration tasks. Furthermore, the user demonstrates a working knowledge of the project's domain (weak supervision, machine learning, and data labeling), evidenced by the modifications to the codebase involved in candidate extraction and relation modelling.
weak-supervisionpythondata-sciencemachine-learninglabeling
HazyResearch/ddbiolib
Mar 2016 - Apr 2017
Contributions:121 commits, 2 PRs, 75 pushes in 1 year 1 month
bioinformaticsbiomedical
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