His papers have won best paper awards, and his contributions have been recognized by multiple awards at IBM. He also holds several patents.
Research Experience
He works as a principal research scientist in IBM Research AI, where he has been involved in various research projects, including trustworthy machine learning and applied algebraic topology.
Education
He holds a PhD in electrical engineering from Arizona State University.
Background
He is a principal research scientist in IBM Research AI at the Thomas J. Watson Research Center. His current research interests are in the areas of trustworthy machine learning, applied algebraic topology, and networked data models. He is intrigued by the interplay between humans, machines, and data and the societal implications of machine learning. He was involved in the initial development of the open source AI Fairness 360 toolkit and is still an active contributor/maintainer.