Kshitij Fadnis is a research staff engineer at IBM's T.J. Watson Research Center in Yorktown Heights, New York in the AI & Optimization group within the Cognitive Learning department. His research interests lie in the field of abductive reasoning and reinforcement learning, within the wider umbrella of Artificial Intelligence (AI), and in examining the issues inherent in task driven conversations as catalyst for human-machine collaboration. Specifically, this covers topics such as natural langugage understanding, context association, knowledge abstraction and reasoning and cost-sensitive and temporal planning. He also has research interests in knowledge representation, transfer learning and explainable AI.
He received his M.S. in Computer Science in Summer 2013 from The Ohio State University, where he worked on extending the metaresoning methods for abductive inference engines. His research focused on understanding, analyzing, and extending the corrective strategies for object level abductive inference systems in partially observable worlds.