“Infinite Dimensional Optimization for Safety Critical Human-in-the-Loop Systems” Dr. Ram Vasudevan, Univ. of Michigan Wednesday, February 22, 2017 2:00 – 3:00PM EEB 248 Abstract: A predominant portion of healthcare spending is devoted to the medical care of unintentional injuries, such as those arising from car accidents or falls. By incorporating automation to predict the likelihood of injury and to design and verify personalized treatment, the burden on healthcare professionals, and thus the overall cost of treatment, can be greatly reduced. Unfortunately, the adoption of automation has been forestalled due to a lack of computationally tractable tools able to identify models of human interaction with the environment and machines, analyze extracted models for perceived threats to determine when aid is required, and synthesize strategies to increase safety in unforeseen circumstances. To address these issues as part of an emerging systems theory for Human-in-the-Loop Systems (HLS), this talk will describe two new techniques each relying upon a new algorithmic framework for infinite dimensional optimization. The first technique is a provably convergent hybrid optimal control algorithm that can automatically identify an individual-specific model of locomotion. When applied to a nine person motion capture walking experiment, the models identified by the algorithm revealed morphological and neurological pathologies. The second technique is a scalable convex programming approach for simultaneous reachable set computation and personalized controller synthesis for safety critical HLS. For locomotion, this approach determines a likelihood for falling while constructing an optimal feedback control law to reduce the risk of injury. This tool is able to tractable predict those who are greatest risk of falling in a completely non-invasive manner. Bio: Ram Vasudevan is an assistant professor in Mechanical Engineering at the University of Michigan with appointments in the University of Michigan Transportation Research Institute and the University of Michigan's Robotics Program. He received a BS in Electrical Engineering and Computer Sciences and an Honors Degree in Physics in May 2006, an MS degree in Electrical Engineering in May 2009, and a PhD in Electrical Engineering in December 2012 all from the University of California, Berkeley. Subsequently, he worked as a postdoctoral associate in the Locomotion Group at MIT from 2012 till 2014 before joining the University of Michigan in 2015. His research interests include dynamical systems, optimization, and robotics especially to applications involving human interaction with Cyber Physical Systems.