Control, learning, and computation in networked systems.
Research on dynamical systems theory, machine learning, and computational neuroscience, from brain-inspired computing to multi-robot autonomy.
University of California, Irvine
Director, UCI Robotarium
The common thread: how networks compute, coordinate, and fail.
Learning & dynamical systems
Methods for modeling and controlling dynamical systems from data, using Koopman operators, diffusion-based planners, and learning-enabled controllers that come with stability guarantees.
Brain-inspired computing
Architectures for learning and computation built on dynamical systems theory, including oscillator networks, associative memory, and physical intelligence.
Robotics & autonomous systems
Multi-agent coordination, path planning, and learning-based control, tested on hardware in the UCI Robotarium.
Network neuroscience
Control theory, dynamical systems, and spectral methods applied to neural data: cortical oscillations, effective connectivity inferred from recordings, and how structure shapes brain dynamics.
Cyber-physical systems
Observability, resilience, and security of networked infrastructure such as power grids, transportation networks, industrial control, and autonomous vehicles.
Recent & selected work
A full filterable list with 180+ entries lives on the publications page.
A facility for multi-robot experimentation, brain-inspired computing, and physical AI.
Open to researchers, students, and industry partners. A motion-tracked arena, 3D fabrication, and on-site compute, so an idea can go from theory to hardware in the same building.
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From the group
Feature article on computing with oscillator networks submitted to IEEE Control Systems Magazine
With Ahmed Allibhoy, we submitted a tutorial on how synchronization in coupled oscillator networks can compute, covering combinatorial optimization, associative memory, and attention.
New preprint on multi-agent flow matching with guarantees
Ruoyu Lin's work with Magnus Egerstedt on DeGG-Flow, a framework that guides multi-agent generative models to satisfy hard coupled requirements, is now on arXiv.
Attention by Synchronization published in TMLR
The oscillator-attention paper with Taosha Guo is out in Transactions on Machine Learning Research.
Provably Safe Generative Sampling published in TMLR with J2C certification
Darshan Gadginmath and Ahmed Allibhoy's paper on constricting barrier functions is out in Transactions on Machine Learning Research, and received a J2C certification (top 10% of accepted papers).
NSF award on control-theoretic foundations for trustworthy generative AI
NSF CMMI supports work on making generative AI safe, coordinated, and reliable in safety-critical engineering systems. Runs October 2026 to September 2029.
New preprint on programmable sequential memory in oscillator networks
Taosha Guo's work on storing and recalling sequences in coupled oscillator networks is now on arXiv.