I am an Associate Professor in the Department of Decision, Operations, and Information Technologies at the Robert H. Smith School of Business, University of Maryland. I have a joint appointment with the Institute for Systems Research at UMD. I received my Ph.D. from Princeton University in 2011. I am a co-author (with W.B. Powell) of the book Optimal Learning, available on Amazon.com.
I work in the general area of business analytics, encompassing topics in optimization, probability, and statistics. More specifically, my 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 our 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.
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.