My current research focuses on the intersection of experimentation, causal inference, machine learning and econometric methods applied to substantive marketing problems. In particular, my research involves building data-driven models aimed at understanding how consumers make choices and helping firms make better decisions pertaining to pricing, promotion, distribution and salesforce management issues. I am also interested in the development of scalable statistical and econometric approaches to deal with complex models calibrated on large-scale marketing data and the implmententation of such algorithms in real world decision evironments.

Prior to joining Chicago Booth I was at UCLA Anderson and the Simon School of Business at the University of Rochester. I have also been visiting faculty at the Johnson School of Management at Cornell University and the Graduate School of Business at Stanford University.