Accessible and Efficient Foundation Models for Biological Discovery

There is a growing gap between machine learning (ML) research on biology-inspired problems and the actual broad-based use of ML in the lab or the clinic. This gap is especially pressing in the context of foundation models and other large ML models. Accessibility and efficiency concerns limit the adoption of these models by biologists and clinicians. Large ML models may require extensive GPU clusters to train, while most biological labs only have access to much more modest computational resources. The usability of these models for non-expert users is also a concern, as is the need to iteratively adapt these models based on lab discoveries. This workshop seeks to bring ML and biomedical researchers together to identify interdisciplinary approaches to design and apply large, complex ML models for biomedical discovery. We invite researchers from academia and industry to submit original papers to bridge the accessibility and efficiency gap between ML research and wet lab use. All accepted papers will be invited to present posters at the workshop, and a few will be invited to give individual spotlight presentations.