I’m currently a Swartz postdoctoral fellow for computational neuroscience in University of Washington, mentored by Dr. Eric SheaBrown. Previously, I got my PhD from University of California, San Diego under the supervision of Dr.Terrence Sejnowski at Salk. My research focuses on revealing latent varibles via network learning theories and Bayesian inference methods. I mainly work with foraging tasks ranging from random walks to social collaborative foraging.
Besides lab research, I’m broadly interested in AI algorithms, theories and puzzles. During the years, I accumulated pages of research notes (network dynamics, causality estimation, ICA algorithms) and side projects (Object recognition, SLAM, puzzle sovling!) and equivalently two M.S. degrees!
I’m posting them here so that they don’t have to go through the painful and lengthy review process to qualify as publications. If anyone is interested in these material, feel free to drop me an email (cyusi@uw.edu).
Latest Publication

Yusi Chen, Huanqiu Zhang, Mia Cameron and Terrence J. Sejnowski, Hippocampus as a generative circuit for predictive coding of future sequences. (2024) Accepted to Neuron. [paper]
 Yusi Chen, Burke Q. Rosen and Terrence J. Sejnowski, Dynamical differential covariance recovers directional network structure in multiscale neural systems. Proceedings of the National Academy of Sciences (2022). [paper][code] [5min video]
 Our ability to think, feel, and react depends on the underlying interaction patterns of distinct brain regions. This paper defines the interaction patterns in a dynamical system and derived an efficient algorithm to estimate it from timeseries data.

Yusi Chen, Qasim Bukhari, Tiger W. Lin, Terrence J. Sejnowski; Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times. Network Neuroscience 2022; [paper]
 All publications
Fun Projects
 Simultaneous Localization and Mapping (SLAM)
 Core question in autonomous driving: how to represent the street map and self location with information from invehicle cameras and kinetic data (e.g. linear/angular velocity). Following are two Bayesian filters transforming visual/kinetic streams into latent variable series representing the map and location
 Particle Filter: [code] [report]
 Unscented Kalman Filter: [code] [report]
 Image Classification through neural networks: [code]
 Seahorse puzzle: [code]
 Nine square tiles to place in a 3by3 grid
 Around 100 billion possibilities with different positions and poses
 Only ONE possibility to match all patterns on the edges
Notes
 Mathematical models for oscillatory signals in the brain
 The past and future of Dynamical Differential Covariance (DDC)
 The many algorithms of ICA
 Realvalued ICA: infomax, FastICA, et al. [note] [code]
 Complexvalued ICA: how to extend definitions to the complex field and translate it back to realvalued ICA. [note] [code]
Recommended Courses
 Probability Theory
 by Prof. Todd Kemp from UCSD Mathematics
 oneyear sequence of courses to define probability concepts from the sample space
 Modern Physics
 by Prof. Leonard Sussikind from Stanford
 multiple lecture sequences introducing classical mechanics, quantum mechanic, et al.
 Robotics
 by Prof. Nikolay Atanasov from UCSD Engineering
 a clear and straightforward introduction of cuttingedge robotic algorithms.
 Information Theory
 Computational Neuroscience
Service
 Reviewer for Proceedings of the National Academy of Sciences
 Reviewer for Neural Computation
 Graduate instructor assistant for system computational neuroscience, UCSD
 Graduate instructor assistant for bioinformatics, UCSD
Awards
 KavliHelinski Fellowship (08/2021)
 National Scholarship of China (2016)