Dynamics · Control · Intelligence
From Mathematical Foundations to Reliable Autonomous and Networked Systems
Our research connects dynamical systems, operator theory, information flow, and control with the challenges of autonomy and interconnected infrastructure. We develop mathematical foundations and data-driven computational methods, then investigate their application to robotics, energy systems, and cyber-physical networks.
Mathematical foundations
We use Koopman and Perron-Frobenius operators to describe nonlinear dynamics through the linear evolution of observables and densities. Spectral methods and data-driven approximations support stability analysis, reachability, and the design of optimal and safe controllers.
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We quantify causal influence in dynamical networks and study how topology shapes information transfer. Our methods combine ergodic theory, stochastic dynamics, and control to analyze and regulate network interactions.
Explore ResearchFrom foundations to practice
We develop terrain-aware models, safety-constrained planning, and Koopman-based model predictive control for wheeled vehicles and quadruped robots navigating challenging environments.
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Using phasor measurement unit data and operator-theoretic models, we study grid stability, uncertainty from renewable generation, robust optimal power flow, and distributed voltage control.
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We investigate estimation and stabilization over uncertain communication channels, robust synchronization, and distributed optimization, connecting system dynamics and network topology to achievable performance.
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