Welcome!

I am currently a Principal Investigator at the Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences. Prior to establishing my laboratory, I was a Swartz Postdoctoral Fellow in computational neuroscience at the University of Washington, mentored by Dr. Eric Shea-Brown and Dr. Adrienne Fairhall. I earned my Ph.D. in Computational Neuroscience from the University of California, San Diego, where I trained with Dr. Terrence Sejnowski at the Salk Institute.

My lab seeks to uncover the computational principles that enable biological and artificial agents to learn, predict, and interact in complex environments. We develop data-driven approaches to infer the latent algorithms underlying behavior and neural activity, spanning learning rules, internal representations, and network dynamics. A central goal of our research is to reverse-engineer the mechanisms by which brains acquire predictive models of the world, assign value to actions and outcomes, and adapt to social interactions.

To address these questions, we combine Bayesian inference, machine learning theory and reinforcement learning theory for three interconnected research topics: (1) discovering learning and decision-making principles directly from behavioral and neural data, including reinforcement learning and social cognition; (2) understanding how learning trajectories shape the emergence of neural representations and cognitive maps; and (3) developing theory-guided methods for revealing functional interactions within large-scale brain networks. By integrating computational theory with modern gigantic experimental datasets, we aim to build a quantitative understanding of intelligence that informs both neuroscience and biologically inspired artificial intelligence.

Besides lab research, I’m broadly interested in AI algorithms, learning theories, mathematics, and scientific puzzles. Over the years, I have accumulated a collection of research notes and side projects, where I share ideas, technical explorations, and unfinished work that may never go through the traditional publication process but have nonetheless shaped my scientific thinking.

I also maintain a curated collection of resources for students and researchers interested in computational neuroscience, including introductory materials, tutorials, software tools, public datasets, and laboratory resources that I have found useful throughout my own training and research.

If you’re interested in these topics, feel free to contact me at chenyusi151201@@gmail.com (remove @ before emailing).

Yusi Chen

Welcome!

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