Rajiv Khanna
Graduate Student, University of Texas at Austin
Rajeev finished his PhD at the University of Texas, Austin in summer 2018. He will be joining UC Berkeley in fall 2018 as a Simons Fellow, followed by a NSF Tripods postdoc. His research is on recovering parsimonious structures, such as sparsity, and exploiting it for prediction. Rajeev has worked in areas of discrete, continuous and Bayesian optimization, often tackling problems that are at the intersection of these subfields. More recently, he has been interested in the problem of interpretation of black/gray box models. Previously, he worked on problems involving prediction of buying propensity based on marketing touches, large scale recommendation systems, Ad Click prediction, and ranking using SVMs.