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Department of Industrial and Systems Engineering
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  • ISE Seminar - Risk-Adaptive Approaches to Learning and Decision Making

ISE Seminar - Risk-Adaptive Approaches to Learning and Decision Making

Date & Time

Wednesday, October 16, 2024, 12:10 p.m.-1:10 p.m.

Category

Seminar

Location

Virtual Zoom

Contact

Weihong Guo

Information

Zoom link:

https://rutgers.zoom.us/j/95698803586?pwd=LcFO8MeEeKbNzlEcink5a5WYZrHQdB.1
Meeting ID: 956 9880 3586 Password: 101101

Seminar Speaker

 

Dr. Johannes O. Royset
University of Southern California

Abstract: Uncertainty is prevalent in engineering design, data-driven problems, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative optimization models expressed using measures of risk and related concepts. We discuss the rapid development of risk measures and their spread to nearly all areas of engineering and applied mathematics. Solidly rooted in convex analysis, risk measures furnish a general framework for handling uncertainty with significant computational and theoretical advantages. We describe the key facts and recall several concrete models and algorithms. The presentation surveys connections with utility theory and distributionally robust optimization, and points to emerging applications areas such as fair machine learning.

Bio: Johannes O. Royset is a professor in the Daniel J. Epstein Department of Industrial and Systems Engineering at University of Southern California. 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 the Goodeve Medal from the Operational Research Society in 2019. Professor Royset was a plenary speaker at the International Conference on Stochastic Programming in 2016, the SIAM Conference on Uncertainty Quantification in 2018, and the INFORMS Security Conference in 2022. He has a Doctor of Philosophy degree from the University of California at Berkeley (2002). Professor Royset has been an associate or guest editor of SIAM Journal on Optimization, Operations Research, Mathematical Programming, Journal of Optimization Theory and Applications, Naval Research Logistics, Journal of Convex Analysis, Set-Valued and Variational Analysis, and Computational Optimization and Applications. He has published two books and more than 100 articles.