Professor Mankad's research focuses on the intersection between data analytics and economic decision making using machine learning techniques. Specifically, he aims to create and apply data mining, machine learning, and visualization techniques for economic modeling with unstructured and complex structured data. His research has been featured in journals and over a dozen media outlets, including the Wall Street Journal and the Chicago Tribune.
Prior to joining Johnson in 2015, Professor Mankad was an assistant professor in the Robert H. Smith School of Business at the University of Maryland. He was a consultant with the U.S. Commodity Futures Trading Commission and also worked at the Federal Reserve Board on characterizing market activity with visual analytic tools.
His undergraduate degree is from Carnegie Mellon University and he received his PhD in Statistics from the University of Michigan.