Ryan Dew is an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania. He received his B.A. in Mathematics from the University of Pennsylvania, and his M.Phil. and Ph.D. in Marketing from Columbia University. His research explores how machine learning and Bayesian statistical methodologies can solve real world marketing problems, with a particular interest in the domains of customer relationship management, preference dynamics and estimation, and data-driven design. His recent work, Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations, has been published in Marketing Science. His on-going research focuses on understanding and predicting how consumer preferences change over time, through novel hierarchical nonparametric models, and on understanding the underpinnings of effective logo design from a data-driven perspective, utilizing image processing and machine learning techniques. His dissertation, Machine Learning Methods for Data-driven Decisions, was a winner of the ISMS Doctoral Dissertation Proposal Competition, the Marketing Section of the American Statistical Association’s Doctoral Research Award, and was an honorable mention in the Marketing Science Institute’s Alden G. Clayton Doctoral Dissertation Proposal Competition.