Learning objectives

• Understand how generative AI frameworks integrate transcriptomic, spatial, and temporal information to model dynamic cell state transitions beyond the limits of current profiling technologies.
• Learn how diffusion-based models and condition-aware architectures can predict and generate cellular states across diverse biological contexts, from embryogenesis to disease progression.
• Explore the concept of virtual cell modeling as a principled in silico platform for studying how cells respond to perturbations, environmental signals, and therapeutic interventions.

Speaker Bio

Dr. Siyu He is a Katharine McCormick postdoctoral fellow in the Department of Biomedical Data Science at Stanford University, co-advised by Dr. James Zou and Dr. Stephen Quake. Her research lies at the intersection of AI and biomedicine, with a focus on developing generative AI models to learn and predict the spatiotemporal dynamics of cells in both healthy and diseased states. She has created several computational tools, including Starfysh, CORAL, Squidiff, and Devo, and has published her work in Nature Biotechnology and Nature Methods. Siyu earned her PhD in Biomedical Engineering from Columbia University, where she was co-advised by Dr. Kam Leong and Dr. Elham Azizi, and her B.S. in Physics from Xi’an Jiaotong University in China.