Uncertain systems that evolve over time (called stochastic dynamical systems) are all around us. Examples include a patient that is fighting cancer and the combined sewer system in the city of Toronto. Typically, we design systems to operate under uncertainty by invoking one of two perspectives. Either we assume the worst possible circumstances, or we assume average circumstances. More broadly, we may wonder whether it is possible to equip systems with a flexible awareness of the vast spectrum of possibilities between the average case and the worst case. Towards this long-term goal, I will introduce the area of risk-aware control theory and highlight our methodology of risk-aware safety analysis. I will present the core mathematical machinery, which uses conditional value-at-risk (a risk measure from finance) and dynamic programming on an augmented state space (to record incurred costs). I will also discuss current directions to alleviate the curse of dimensionality.

Biography: Margaret Chapman is an Assistant Professor with the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto. Her research focuses on risk-aware stochastic control theory, with emphasis on safety and applications in healthcare and sustainable cities. She earned her B.S. degree with Distinction and M.S. degree in Mechanical Engineering from Stanford University in 2012 and 2014, respectively. Margaret earned her Ph.D. degree in Electrical Engineering and Computer Sciences from the University of California Berkeley in May 2020. In 2021, Margaret received the Leon O. Chua Award for outstanding achievement in nonlinear science from her doctoral alma mater. In 2023, Margaret was nominated by her department for the Early Career Teaching Award (Faculty of Applied Science and Engineering, University of Toronto). In addition, she is a recipient of the US National Science Foundation Graduate Research Fellowship (2014), the Berkeley Fellowship for Graduate Study (2014), and the Stanford University Terman Engineering Scholastic Award (2012).