He received a Bachelor's degree in Mathematics from University of Waterloo, Canada, and Ph.D. degree in EECS from Massachusetts Institute of Technology (MIT). His research interests include statistical machine learning and artificial intelligence, high-dimensional statistics and optimization theory. Among other awards, he has received a Medallion Lectureship (2013) from the Institute of Mathematical Statistics (IMS); a Section Lecturer at the International Congress of Mathematicians (2014); the COPSS Presidents' Award (2014) from the Joint Statistical Societies; and the IMS David Blackwell Lectureship (2017).

Substantial progress has been made in machine learning and artificial intelligence in recent years. Using machine learning techniques, we can now design fully automated systems for face recognition, language translation, inventory management, competitive game playing, and self-driving cars, among other applications. Nonetheless, there remain a number of major roadblocks to real-world deployments of machine learning systems. First, they are highly usceptible to adversarial instances, specially constructed inputs that cause the system to fail, and present substantial security risks. Second, it can be extremely difficult to interpret the outputs of large-scale predictive systems, which raises legal problems and raises barriers to adoption. We discuss these challenges and provide some vignettes into recent research aiming to tackle them.