Charles H. Kellstadt Professor of Marketing
Chicago Booth
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.