Faculty Directory

Ryzhov, Ilya O.

Ryzhov, Ilya O.

Associate Professor
Robert H. Smith School of Business
The Institute for Systems Research
4347 Van Munching Hall

Ilya Ryzhov is an Associate Professor of Operations Management in the Department of Decision, Operations and Information Technologies. He received his Ph.D. in Operations Research and Financial Engineering from Princeton University in 2011. His research deals with the role of information in decision analysis, exploring the way in which new information influences and improves decision-making strategies. Information has an economic value that can be measured and balanced against other economic concerns as part of the decision-making process. Dr. Ryzhov develops efficient ways to achieve this balance, with applications in pricing, revenue management, and optimization of energy costs. His work has appeared in Operations Research.

He works in the general area of business analytics, encompassing topics in optimization, probability, and statistics. More specifically, his research deals with decision-making under uncertainty, often with the additional dimension of information collection. In many applications, decisions are made sequentially over time and the perception of the "optimal" course of action changes as we observe the outcomes of past decisions. Understanding the role, and the value, of information in these problems requires three elements. First, we need stochastic models to describe the uncertain environment in which decisions are implemented. Second, we require principled statistical models to represent our beliefs about this environment, and the way in which new information changes these beliefs. Third, we need optimization to identify potential good decisions, often by making an explicit tradeoff between short-term earnings and information with long-term benefits.

He is a co-author (with W.B. Powell) of the book Optimal Learning, available on Amazon.com. The book Optimal Learning discusses how to model and solve many different types of learning problems, beginning with the classic models of ranking and selection and multi-armed bandits, and moving on to more sophisticated decision problems.

His CV is available here.

Theoretical, methodological, and applied contributions to problems in business analytics. Resource allocation problems in revenue management, logistics, marketing and e-commerce. Theoretical and methodological work focuses on optimal learning in stochastic optimization.