MAE Seminar: Trustworthy Autonomy for Safety-Critical Cyber-Physical Systems

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MAE Seminar: Trustworthy Autonomy for Safety-Critical Cyber-Physical Systems

Date: January 29, 2026
Time: 12:50 PM Location: MAE-A 303

Dr. Claus R. Danielson
Assistant Professor
Department of Mechanical Engineering
University of New Mexico

Abstract
This talk will present recent advances and ongoing work in optimization, control, and learning for trustworthy autonomy in safety-critical cyber-physical systems. We begin by motivating the continued need for mathematical rigor in the design of learning-enabled autonomy to ensure safety, reliability, and verifiability. We introduce invariant sets and barrier functions as the fundamental mathematical tools for formally certifying safety in autonomous systems. Building on this framework, we describe our research on data-driven methods for learning invariant sets and barrier functions directly from data to provide rigorous guarantees on constraint satisfaction. We then demonstrate the impact of these methods through applications in advanced manufacturing and on-orbit logistics. First, we present our research on data-driven iterative learning control for subtractive manufacturing to improve consistency and repeatability. Then, we present our research on motion planning algorithms that integrate invariant-sets to provide provable collision-avoidance guarantees for autonomous vehicles operating in crowded environments.

Biography
Dr. Claus Danielson is an Assistant Professor in the Department of Mechanical Engineering at the University of New Mexico, where he joined the faculty in August 2020. He received his Ph.D. in 2014 from the Predictive Control Laboratory at the University of California, Berkeley. He holds an M.S. degree from Rensselaer Polytechnic Institute and a B.S. degree from the University of Washington. Prior to joining UNM, Dr. Danielson was a Principal Research Scientist at Mitsubishi Electric Research Laboratories in Cambridge, Massachusetts. His research interests lie in motion planning and constrained control, with an emphasis on data-driven methods that exploit structure in extreme-scale planning, control, and optimization problems. His work has been applied to a wide range of domains, including autonomous vehicles, robotics, spacecraft guidance and control, building HVAC systems, energy storage networks, adaptive optics, atomic force microscopy, and cancer treatment.

Faculty Host: Dr. Christopher Petersen