Seminar by Ozlem Cavus

Seminar by
Ozlem Cavus
Assistant Professor of Industrial EngineeringWe use Markov risk measures to formulate a risk-averse version of the undiscounted total cost problem for a transient controlled Markov process. We derive risk-averse dynamic programming equations and we show that a randomized policy may be strictly better than deterministic policies, when risk measures are employed. We propose two solution methods, value and policy iteration, and analyze their convergence. We illustrate the results on an optimal stopping problem, an organ transplant problem, and a credit limit control problem.

 

Biographical Sketch
Ozlem Cavus is currently an Assistant Professor of Industrial Engineering at Bilkent University. She received her B.S. and M.S. degrees in Industrial Engineering from Bogazici University in 2004 and 2007, respectively, and the Ph.D. degree in Operations Research from Rutgers Center for Operations Research (RUTCOR) at Rutgers University in 2012. Her research interests include risk-averse dynamic optimization, Markov decision processes, dynamic programming, and stochastic optimization under dominance constraints.

 

This is a joint work with Dr. Andrzej Ruszczynski from Rutgers Business School.