Stay Informed:
Baskin Engineering COVID-19 Information and Resources
Campus Roadmap to Recovery
Zoom Links: Zoom Help | Teaching with Zoom | Zoom Quick Guide

Prof. Johannes Royset, Naval Postgraduate School: Data and Decision, A Risk-Averse Perspective

Start Time: 
Monday, January 23, 2017 - 4:00pm
End Time: 
Monday, January 23, 2017 - 5:00pm
Engineering 2, room 180
Abstract: Standard deviation, mean-squared error, and regression are integral parts of even the most rudimentary data analysis. Decision making based on utility theory and risk, in the Markowitzian sense of mean-plus-standard deviation, is equally widely adopted. In this presentation, we describe far-reaching extensions of these concepts that enable alternative approaches to risk mitigation and preference-driven data analysis. We give fundamental connections between regression and decision making, which lead to the construction of measures of residual risk. These measures quantify the improved situation faced by a hedging investor compared to that of a single-asset investor, but the notion reaches further with relations to forecasting, learning, and regression. Relying on convex analysis, we establish properties of broad classes of measures of error, deviation, regret, risk, and residual risk. These measures can play central roles in the development of risk-tuned approximations of random variables, in tracking of statistics, and in estimation of the risk of conditional random variables. We illustrate the framework by develop a risk-based sparsity-inducing approach to surrogate models in high-dimensional dynamical systems as well as by learning of computationally costly simulation output from inexpensive simulations in the context of an ultra-high speed navy vessel. Bio: Dr. Johannes O. Royset is Associate Chair of Research and Professor of Operations Research at the Naval Postgraduate School. Prof. Royset's research focuses on formulating and solving stochastic optimization and variational problems arising in data science, sensor management, and engineering design. He was awarded a National Research Council postdoctoral fellowship in 2003, a Young Investigator Award from the Air Force Office of Scientific Research in 2007, and the Barchi Prize as well as the MOR Journal Award from the Military Operations Research Society in 2009. He received the Carl E. and Jessie W. Menneken Faculty Award for Excellence in Scientific Research in 2010 and was a co-recipient of the UPS George D. Smith Prize from INFORMS in 2013. He was a plenary speaker at the 14th International Conference on Stochastic Programming (2016). Prof. Royset is a Guest Editor of Mathematical Programming and an Associate Editor of Operations Research, Naval Research Logistics, Journal of Optimization Theory and Applications, and Computational Optimization and Applications. In 2015-2016, he was a Guest Editor of Journal of Optimization Theory and Applications. His research has been supported by the Office of Naval Research, Air Force Office of Scientific Research, Army Research Office, and DARPA and has resulted in one book, three book chapters, and 45 journal publications. He has a Doctor of Philosophy degree from the University of California at Berkeley (2002).