Klaske van Heusden of School of Engineering, UBC Okanagan

Abstract: New automated systems with high levels of autonomy, such as self-driving cars and autonomous robots, are increasingly enabled by machine learning. These applications highlight the safety-critical nature of control systems technology, both due to their close proximity to the public and their level of autonomy. Safe control technology aims to provide guarantees that these systems will not do harm. Interest in safety filters, a modular approach to safe control, has increased in response to safety concerns associated with learning-based control often employed in robotics and autonomous driving. Such safety filters commonly rely on accurate mathematical models, contradicting the intended use to enhance data-driven learning solutions. This reliance on accurate models also limits the use of this technology in uncertain environments and in applications other than robotics. In this seminar I will highlight some of the challenges encountered when applying safe control to automated drug delivery. I will present recent results on data-driven safety filters that can extend the applicability of safe control technology.

Biography:

Bio: Klaske van Heusden is an assistant professor at The University of British Columbia (Okanagan campus). She holds a PhD from the EPFL, the Swiss Federal Institute of Technology in Lausanne and an MSc from Delft University of Technology. Dr. van Heusden was a postdoctoral fellow at the University of California, Santa Barbara, where she worked on algorithms for the artificial pancreas. Prior to joining the School of Engineering, Dr. van Heusden was a research associate at UBC where she worked on closed-loop anesthesia and automated welding. Her current research focuses on data-driven methods for constrained control, safety-critical systems and vision-based feedback systems.

 

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