I’m recruiting postdocs who are excited to work with real clinical data and partner closely with clinicians.
If you’ll be at ML4H or the first day of NeurIPS, let’s connect!
More about my work: web.stanford.edu/~jfries/

My lab will focus on multimodal foundation models for healthcare, combining CS and clinical collaboration to understand and treat complex diseases like cancer.

Core interests: representation learning, synthetic data generation, longitudinal benchmarks, and agentic clinical AI.

[4/4] This was a massive collaboration involving multiple offices and champions across Stanford University and @stanfordmedicine.bsky.social

Thanks to our research team: Michael Wornow, Ethan Steinberg, Zepeng Frazier Huo, Hejie Cui, Suhana Bedi, Alyssa Unell, Nigam Shah and many others.

[3/4] 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀
Each dataset includes a set of standardized tasks exploring a technical challenge area in AI.
🎯 Few-shot Learning
🤖 Multimodal Learning & Time-to-Event Modeling
⌛ Long Context Instruction Following & Temporal Reasoning

[2/4] 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝗲𝘀
📊 3 longitudinal EHR datasets
• Scale: 25,991 patients | 441,680 visits | 295M clinical events (median: 4,882 events/patient)
• Timeframe: 1997–2023 (median: 10 years/patient)
• Multimodal: structured EHR data, 3D medical imaging, and clinical notes