Yichen (James) Zhao

Yichen (James) Zhao

Yichen (James) Zhao is a third year PhD student studying Robotics and Mathematics in the Georgia Institute of Technology. His research interests include perception and motion planning, dynamical systems, distributed optimization, non-convex optimization, Riemannian optimization, Morse theory, sheaf theory, and algebraic topology.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Predicting Chaotic System Behavior Using Machine Learning Techniques Published
Nonlinear Science and Control Engineering, Vol. 1
Huaiyuan Rao, Yichen Zhao, Hsuan-Pin Chen (2025). "Predicting Chaotic System Behavior Using Machine Learning Techniques." Nonlinear Science and Control Engineering vol. 1, num. 1.
We investigate the accuracy, efficiency, and robustness of three recurrent neural network architectures: (i) next-generation reservoir computing, (ii) reservoir computing, (iii) long short-term memory (LSTM) for chaotic system prediction.
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A Distributed Asynchronous Generalized Momentum Algorithm Without Delay Bounds arXiv
arXiv · Submitted to IEEE TCNS
Ellie Pond*, Yichen Zhao*, and Matthew Hale (2025). "A Distributed Asynchronous Generalized Momentum Algorithm Without Delay Bounds." arXiv preprint arXiv:2508.08218. *Equal contribution.
We introduce a distributed generalized momentum algorithm that allows for arbitrary delays in communications and computations, and it converges faster than comparable existing algorithms.
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Conference Papers

STAR-Filter: Efficient Convex Free-Space Approximation via Starshaped Set Filtering in Noisy Environments Accepted
Accepted · WAFR 2026
Yuwei Wu, Yichen Zhao, Dexter Ong, and Vijay Kumar (2026). "Star-filter: Efficient convex free-space approximation via starshaped set filtering in noisy environments."arXiv preprint arXiv:2604.26626.
In this paper, we propose STAR-Filter, a lightweight framework that employs starshaped set construction as a fast filter for convex region generation in collision-free space.
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Asynchronous Nonlinear Sheaf Diffusion for Multi-Agent Coordination Accepted
arXiv · Accepted to ACC 2026
Yichen Zhao*, Tyler Hanks*, Hans Riess*, Samuel Cohen, Matthew Hale, and James Fairbanks (2025). "Asynchronous Nonlinear Sheaf Diffusion for Multi-Agent Coordination." arXiv preprint arXiv:2510.00270. *Equal contribution.
We introduce an asynchronous nonlinear sheaf diffusion algorithm. Specifically, we show that under mild assumptions on the coordination sheaf and bounded delays in communication and computation, nonlinear sheaf diffusion converges to a minimizer of the Dirichlet energy of the coordination sheaf at a linear rate proportional to the delay bound.
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Options Matter: Accelerating Multi-Robot Navigation via Changing Safety Filters In Preparation
In Preparation
In preparation, authors: Yichen Zhao and Ellie Pond and Sean Wilson and Matthew Hale.
We introduce a framework for using time-invariant CBFs to form a time-varying CBF that guarantees safety while accelerating multi-robot navigation tasks. We first show that a time-varying convex combination of time-invariant CBFs forms a valid time-varying CBF, which renders the time-dependent safe set forward invariant.