Recent advances in machine learning have sparked considerable developments in ML for health in both its computational methods and theoretical foundations.

Driven by the new availability of massive imaging, sequencing, multi-omics and EHR datasets, large-scale machine learning techniques have been adapted to address various tasks such as survival prediction or perturbation response prediction.

At the same time, medical data raises some specific challenges such as missing modalities, inter-patient variability and privacy requirements, which hinder straightforward adaptation of commonly used algorithms.

This workshop aims at bringing together researchers and practitioners working on these topics. This year’s talks will focus specifically on foundation models for healthcare data and survival analysis.

Registration will open soon.

The event will also feature a poster session - more details to come soon.