About the camp
A focused week for the next generation of ML-for-health researchers
The AHLI Health AI Summer Camp is a week-long intensive designed to give late-stage PhD students and early-career postdocs the rigor, tools, and community to do excellent machine learning for health.
Our goal
Machine learning for health sits between two demanding fields. Doing it well means understanding both the methods and the clinical realities they touch. The camp exists to help late-stage PhD students and early-career postdocs build that combined fluency in a concentrated, supportive setting.
The week is hands-on by design. Concept sessions are paired with guided lab work, and the cohort is kept small — 40 researchers — for genuine discussion and mentorship.
Who it's for
The camp is aimed at late-stage PhD students and early-career postdocs whose research involves machine learning for health (ML4H). Participants typically have a working foundation in machine learning and want to deepen their practice in the health setting specifically — data, evaluation, study design, and translation.
What makes it different
- Project-driven. Every day pushes your own project forward, not just your notes.
- Realistic data. Labs use synthetic clinical data modeled on MIMIC-IV structure.
- Current methods. Coverage spans classical models through the foundation-model landscape.
- Small cohort. 40 participants, for real conversation with instructors and peers.
The Project Workbook. A private thinking aid you keep for your own project — four short parts across Days 1–4. It isn't graded; its job is to sharpen how you think, and to leave you with a structured reference you can bring back to your own research.
Support and partners
The camp is supported by the Gordon and Betty Moore Foundation and held in Seattle alongside the Conference on Health, Inference, and Learning (CHIL), connecting participants to the wider health-AI research community.