I am a professor in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engeineering (ISE) at the University of Southern California. I am also the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Prior to joining USC I spent a year as a postdoc in the AMPLAB at UC Berkeley mentored by Ben Recht and Martin Wainwright. I obtained my Ph.D. in Electrical Engineering from Stanford in 2014 advised by Emmanuel Candes.
On the theoretical side, my research focuses on developing the mathematical foundations of modern data science spanning recent developments in generative AI to more classical deep learning, machine learning, signal processing, and computational imaging. To this aim I often draw upon and develop eclectic new tools in (non)convex optimization, high-dimensional probability, statistical estimation/inference, empirical processes, and learning theory.
On the applied side, my focus is on developing reliable AI for applications in science, healthcare and medicine. In colloaboration with domain scientists and physcians we aim to develop new architectures and data curation pipelines that accelarate scientific discovery, enhance mathematical and spatial reasoning capabilities of GenAI, improve their reliability, and develop rigorous evaluation and statistical uncertainty quantification techniques that truely tests their capabilities and limitations.
On the weekends, you're most likely to find me cycling on the Pacific coast ranging from Malibu/Santa Monica mountains/Palos Verdes in SoCal to the Marine district in NorthCal.
I'm also an amatuer artist, dabbling in Persian calligraphy. I'm very interested in coming up with creative ways to preserve this and other ancient artforms with responsible use of generative AI.