Kush R. Varshney was born in Syracuse, New York in 1982. He received the B.S. degree (magna cum laude) in electrical and computer engineering with honors from Cornell University, Ithaca, New York, in 2004. He received the S.M. degree in 2006 and the Ph.D. degree in 2010, both in electrical engineering and computer science at the Massachusetts Institute of Technology (MIT), Cambridge.
He is a research staff member in the Data Science Group of the Mathematical Sciences and Analytics Department at the IBM Thomas J. Watson Research Center, Yorktown Heights, New York. He is also a data ambassador with DataKind, New York, New York. While at MIT, he was a research assistant with the Stochastic Systems Group in the Laboratory for Information and Decision Systems and a National Science Foundation Graduate Research Fellow. He has been a visiting student at Laboratoire de Mathématiques Appliquées aux Systèmes at École Centrale, Paris, and an intern at the Systems and Decision Sciences Section, Lawrence Livermore National Laboratory, Livermore, California, at Sun Microsystems, Burlington, Massachusetts, and at Sensis Corporation, DeWitt, New York. His research interests include statistical signal processing, machine learning, data mining, and image processing.
Dr. Varshney is a member of Eta Kappa Nu and Tau Beta Pi, and a senior member of IEEE. He received a Best Student Paper Travel Award at the 2009 International Conference on Information Fusion, the Best Paper Award at the 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, the Best Social Good Paper Award at the 2014 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, a Best Research Paper Honorable Mention at the 2015 SIAM International Conference on Data Mining, and several IBM awards for contributions to business analytics projects. He is on the editorial board of Digital Signal Processing and a member of the IEEE Signal Processing Society's Machine Learning for Signal Processing Technical Committee and Signal Processing Theory and Methods Technical Committee.