Research

What we work on.

01 / Dynamical systems

Learning & dynamical systems

The work combines classical control theory with modern machine learning to model and control dynamical systems from data. Topics include Koopman operator methods for nonlinear dynamics, diffusion-based planners, transfer learning for control, and guarantees of stability and safety for learning-enabled controllers.

Representative work

  1. Data-Driven Control of Complex Networks
    Nature Communications
    2021
  2. Controllability Metrics, Limitations and Algorithms for Complex Networks
    IEEE Transactions on Control of Network Systems
    2014
  3. Fragility Limits Performance in Complex Networks
    Scientific Reports
    2020

Slides

Data-driven and Brain-inspired Autonomous Systems 2024 · PDF, 2.7 MB
02 / Brain-inspired

Brain-inspired computing

We take inspiration from the brain to design architectures for learning, decision making, and computation. The tools come from dynamical systems, control, and optimization, and the aim is algorithms that stay biologically plausible while remaining efficient enough to build.

Representative work

  1. Non-Stationary Representation Learning in Sequential Linear Bandits
    IEEE Open Journal of Control Systems
    2022
  2. Astrocytes as a Mechanism for Contextually-guided Network Dynamics and Function
    PLOS Computational Biology
    2024
  3. Controllable Neural Architectures for Multi-Task Control
    European Control Conference
    2025
03 / Robotics

Robotics & autonomous systems

Our lab develops control and optimization algorithms for robotic systems, with a focus on multi-agent coordination, path planning, and learning-based control. We combine theoretical advances in control theory with practical implementation in robotic platforms.

Representative work

  1. On Cooperative Patrolling: Optimal Trajectories, Complexity Analysis and Approximation Algorithms
    IEEE Transactions on Robotics
    2012
  2. Stochastic Surveillance Strategies for Spatial Quickest Detection
    International Journal of Robotics Research
    2013
  3. Multi-Agent Persistent Monitoring via Time-Inverted Kuramoto Dynamics
    IEEE Control Systems Letters
    2022
04 / Neuroscience

Network neuroscience

We apply tools from control theory, dynamical systems, and spectral analysis to understand neural systems. The work spans modeling cortical oscillations and phase-amplitude coupling, inferring effective connectivity from electrophysiological recordings, and developing a mathematical theory of how the brain's structural network shapes its functional dynamics.

Representative work

  1. Controllability of Structural Brain Networks
    Nature Communications
    2015
  2. Functional Control of Oscillator Networks
    Nature Communications
    2022
  3. Phase-amplitude Coupling in Neuronal Oscillator Networks
    Physical Review Research
    2021

Slides

Analysis and Control of Functional Brain Networks 2023 · PDF, 3 MB
05 / Cyber-physical

Cyber-physical systems

The work develops theoretical frameworks and computational tools for analyzing and controlling cyber-physical systems: networked infrastructure where computation and communication are tightly coupled to physical dynamics. Research topics span observability and controllability of large-scale systems, resilience under faults and adversarial conditions, and security of power grids, transportation networks, industrial control systems, and autonomous vehicles.

Representative work

  1. Attack Detection and Identification in Cyber-Physical Systems
    IEEE Transactions on Automatic Control
    2013
  2. Secure Trajectory Planning Against Undetectable Spoofing Attacks
    Automatica
    2020
  3. Data-driven Meets Geometric Control: Zero Dynamics, Subspace Stabilization, and Malicious Attacks
    IEEE Control Systems Letters
    2022

Slides

Analysis and Design of Secure Cyber-Physical Systems 2023 · PDF, 1.9 MB