Tools for managing dynamic systems typically invoke one of two perspectives. In the worst-case perspective, the system is assumed to behave in the worst possible way; this perspective is used to provide formal safety guarantees. In the risk-neutral perspective, the system is assumed to behave as expected; this perspective is invoked in reinforcement learning and stochastic optimal control. While the worst-case perspective is useful for safety analysis, it can lead to quite conservative decision support tools, especially in settings where uncertainties are non-adversarial. The risk-neutral perspective is less conservative and useful for optimizing the system’s performance on average. However, optimizing average performance is not guaranteed to protect against harmful outcomes and thus is not appropriate for safety-critical applications. In this talk, I will first present an analytical and computational toolkit for managing breast cancer that I have developed with cancer biologists at the Oregon Health and Science University by invoking the worst-case perspective. In addition to providing biological insights, this work has motivated the need for quantitative decision support tools based on systems theory and data analysis that balance the worst-case and risk-neutral perspectives. Thus, to facilitate more protective and practical management of safety-critical systems, I have developed a new risk-sensitive tool for safety analysis that provides a tunable balance between these perspectives by leveraging risk measure theory. The next part of my talk will focus on this new risk-sensitive tool and its application to evaluating safety of urban water infrastructure in joint work with water resources specialists at the Berkeley Water Center. Then, I will conclude by presenting my research vision on the development of more realistic decision support tools that are sensitive to rare harmful outcomes and the transfer of these tools to practical problems in healthcare, smart cities, and additional safety-critical settings.
Margaret Chapman is a PhD candidate advised by Claire Tomlin in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley. She is grateful to be a recipient of the Fulbright Scholarship, the NSF Graduate Research Fellowship, and the Berkeley Fellowship for Graduate Study. She earned her BS degree (with Distinction) and her MS degree in Mechanical Engineering from Stanford University. Margaret’s research lies at the intersection of stochastic and robust optimal control with a focus on risk analysis for safety-critical systems. She aims to develop quantitative decision support tools that are more sensitive to rare outcomes by leveraging systems theory, data analysis, risk measures, and more realistic assumptions about uncertainties. Her application areas of interest include healthcare and smart cities. Margaret is delighted to be a participant of 2019 Rising Stars in EECS hosted by the University of Illinois Urbana-Champaign, and she aims to become a professor at a research-focused university. https://www.margaretpfeifferchapman.com/