From Code to Cure, Building Foundation Models and AI Co-Scientists for Precision Genomic Medicines

✨ From Code to Cure, Building Foundation Models and AI Co-Scientists for Precision Genomic Medicines

💬 Le Cong

🏫 Associate Professor of Pathology and Genetics, Stanford University School of Medicine; Co-founder, Phylo and Acelegen

📅 Thursday, September 17

⏰ 4 – 5 p.m.

📍 Yellowstone (75A – 2001)

Abstract: Modern biology is becoming programmable, yet closing the loop between biological data and real-world validated insights and therapeutics remains slow. I will discuss our efforts to build foundation models and AI co-scientists that connect these steps across the continuous discovery cycle. RNAGenesis represents a generalist RNA foundation model for representation and de novo design of functional RNAs; CRISPR-GPT uses LLM to reason and design gene-editing experiments; LabOS combines multimodal AI, agentic reasoning, and extended reality to connect digital intelligence with real-world lab workflows. Together, these systems point toward an end-to-end framework in which AI can design, reason, see, and collaborate with scientists to accelerate gene-editing, cell engineering, and precision therapeutics.

Bio: Dr. Le Cong is an Associate Professor with Tenure at Stanford Medicine. His research spans CRISPR gene-editing, immune and stem cell biology, and AI for biomedical discovery, with the goal of making biology increasingly programmable. His laboratory developed technologies for large-scale genome engineering and for programming immune and stem cells, while pioneering the use of foundation models and AI agents in functional genomics. His team developed AI co-scientists including CRISPR-GPT, AutoScreen, and LabOS. His long-term vision is to build AI that can reason, design, see, and work alongside scientists to accelerate precision genomic medicines.

Details

• Date: Thursday, September 17
• Time: 4:00 pm - 5:00 pm
• Category: EECS Seminar
• Location: Yellowstone (75A - 2001)