Foundations for Automated Data Science
Lunch will be served and registration is required.
Host: Center for Data and Computing
Alexander Gray serves as VP of Foundations of AI at IBM, leading IBM’s basic AI research globally. He previously served as CEO and CTO of Skytree, which he co-founded, then at Infosys as GM of Research and Fellow. Prior to that, he served as a tenured Associate Professor at the Georgia Institute of Technology. A theme of his research work, beginning at NASA in 1993, has been on the computational aspects of machine learning for handling massive datasets, long predating the movement of “big data” in industry. His work helped enable the Science journal’s Top Breakthrough of 2003, and have won a number of research awards. He served as a member of the 2010 National Academy of Sciences Committee on the Analysis of Massive Data, a National Academy of Sciences Kavli Scholar, and a frequent advisor and speaker on topics of large-scale machine learning and data science at top research conferences, government agencies, and leading corporations. He received AB degrees in Applied Mathematics and Computer Science from UC Berkeley and a PhD in Computer Science from Carnegie Mellon University. His current interests are in automated data science, automated programming, and in new formalisms for AI beyond today’s machine learning, toward achieving reading comprehension and strong AI.