Our research

We focus on modeling, estimation, and control of complex uncertain systems.

Our work is organized around three focus areas: space systems, robotics and autonomous systems, and applications of AI/ML.

Focus areas

Space systems

Satellite-to-satellite power beaming

We are developing and researching wireless power beaming between spacecraft in orbit to enable new capabilities for space infrastructure. Our current emphasis is system-level application. We are moving toward physics-based simulations, controls for accurate pointing, networking of constellations, and thermal analysis.

Uncooperative body characterization

We work on characterizing the dynamics of tumbling and uncooperative objects, relevant for space debris, rendezvous, and scenarios involving complex rigid-body motion. Our approach includes vision-based methods for 3D reconstruction and feature recognition, using both traditional and machine learning methods.

Robotics and autonomous systems

Machine learning for robotic and autonomous systems

We develop machine learning methods for modeling, perception, and control in robotic and autonomous systems, including learned dynamics and policies, perception, and state estimation.

Multi-agent planning and coordination

We work on multi-agent planning and coordination for complex uncertain systems: distributed decision-making, coordinated control, and planning under uncertainty.

Applications of AI/ML

Intelligent, online adaptive systems

We work on systems that update models and policies in real time as data arrives. Applications include online learning and adaptive control for satellite systems, online system identification for spacecraft dynamics, continual learning for perception, and adaptive control for robotics and autonomy.

Fault detection, isolation, and recovery

We work on fault detection, isolation, and recovery for space systems and other safety-critical platforms. We use hybrid (model-based and data-driven) methods. Work includes detection and isolation of faults, recovery and reconfiguration to maintain safe operation, and health monitoring.

Biological systems for medical applications

We apply dynamical systems, control theory, and AI/ML to biological networks. Topics include neurodegenerative disease networks, protein interaction modeling, and stability and control of disease-relevant networks. The focus is on modeling and network-level analysis.

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