Bio
Jason Fries' research focuses on training and evaluating foundation models for healthcare, positioned at the intersection of computer science, medical informatics, and hospital systems. His work explores the use of electronic health record (EHR) data to contextualize human health, leveraging longitudinal patient information to inform model development and evaluation. His research has been published in venues such as NeurIPS, ICLR, AAAI, Nature Communications, and npj Digital Medicine.
Current Role at Stanford
I'm currently working as a staff research scientist in the Shah Lab and research scientist at Snorkel AI. My interests fall in the intersection of computer science and medical informatics. My research interests include:
• Foundation models and generative AI for healthcare
• Data-centric AI, focusing on training data curation, data generation, and quality assessment
• Learning with limited labeled data (e.g., weak supervision, zero/few-shot learning)
• Human-in-the-loop machine learning systems
