Generative AI in Genomics (Gen^2): Barriers and Frontiers

Generative AI (GenAI) is transforming biology, with breakthrough applications like directed evolution in protein science. The parallel ambition to engineer cellular and tissue states in genomics is now a major frontier, yet progress is hampered by domain-specific roadblocks. Our workshop is designed to bridge this gap between GenAI's promise and its practical applications towards this goal. With recent large-scale data initiatives launched to support GenAI models creating an inflection point for the field, timing is ideal. Through a field-grounding keynote by a genomics expert, invited talks by GenAI practitioners, contributed presentations, and a moderated debate, we will bring together experts and early-career scientists from machine learning and experimental genomics to collaboratively define a roadmap for progress. Our program will target core, interconnected challenges across the development pipeline: from data generation priorities and model design for genomic hierarchies to biologically-grounded evaluation frameworks and interpretability. By defining promising research directions and critical evaluations, our ultimate goal is to catalyze a new generation of models for tangible biological impact.