YoungJu Jo | Data-driven discovery and control of neural computations

Abstract The brain is a nonlinear dynamical system whose evolving population activity gives rise to computations that guide behavior. We will present a data-driven systems neuroscience framework that integrates large-scale neurophysiology, interpretable dynamical systems modeling, and targeted perturbations in cognitive animal models. This approach has enabled the discovery of cell-type-specific and brain-wide dynamical structures underlying reward and memory computations, including attractor manifolds that integrate past rewards and steer future decision-making. To enable causal tests and model-guided control of these computations, we have developed optical and genetic technologies for cellular-resolution, bidirectional control of neural populations, along with optimal control approaches for designing perturbations. Together, these converging advances set the stage for a closed-loop strategy to measure, model, and control neural dynamics and computations, toward the long-term vision of writing complex information into the brain. Bio YoungJu Jo is an Assistant Professor and the Brody Family Faculty Fellow in the Department of Electrical Engineering and Computer Sciences and the Helen Wills Neuroscience Institute at the University of California, Berkeley. His laboratory develops and applies next-generation neurotechnologies to measure, model, and control neural dynamics and computations in behaving animals. He completed his postdoctoral training in Bioengineering in 2026, Ph.D. in Applied Physics in 2024, and M.S. in Biology in 2020, all at Stanford University, where he studied experimental and computational systems neuroscience with Profs. Karl Deisseroth, David Sussillo, and Krishna Shenoy. Before transitioning to neuroscience, he received his B.S. in Physics and Mathematics from KAIST in 2018, where he worked on computational imaging and optogenetics with Profs. YongKeun Park and Won Do Heo. His early work on AI-powered holographic microscopy was recognized with the 2022 Agarwal Award and commercialized by Tomocube. About the Center for Neural Data Science Seminar Series The Center for Neural Data Science Seminar Series is a platform for trainees across campus to share insights and innovative approaches that bridge the gap between neuroscience and data science. As neuroscience continues to generate vast amounts of data—from intricate neural circuit maps to large-scale brain activity recordings—the need for interdisciplinary expertise in data science, statistics, and engineering has never been more critical. The Center for Neural Data Science mission is to advance brain research through the development of cutting-edge analytical methodologies and collaborative approaches. Stanford's affiliates are invited to join this vibrant community dedicated to transformative discoveries. This seminar series is only offered in person. Happy Hour/Reception for Attendees: Seminars attendees are invited to stay for a happy hour reception after the seminar to continue the conversation with the speaker. Sign up to learn about all our upcoming events