I develop Bayesian models that disaggregate data to address individuals. I also study Bayesian nonparametric methods and currently consider shape constraints. I teach and use data mining methods such as recursive partition and neural networks.
My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.
Amitabh Sinha, PhD, is Associate Professor of Technology and Operations in the University of Michigan Stephen M. Ross School of Business, Ann Arbor, and Co-Director of the Tauber Institute for Global Operations.
Amitabh’s current research primarily focuses on the operational aspects of ecommerce/omnichannel retail. For instance, one of his ongoing research projects explores the optimization of order fulfillment by online retailers; another examines the design of retail stores and inventory management for omnichannel fulfillment through stores. Another recent research project examines the potential impact of platform capitalism (also called the sharing economy) in a business-to-business setting for ecommerce warehousing. The primary methodological tools are optimization, simulation, and machine learning.