ARYAN MOKHTARI
I am an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Texas at Austin (UT Austin) where I hold the Fellow of Texas Instruments/Kilby Endowed Chair.
I am a core member of the Machine Learning Laboratory (MLL), the NSF AI Institute for Foundations of Machine Learning (IFML), and the Wireless Networking & Communications Group (WNCG) at UT Austin. I am also a member of the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE).
My current research focuses on the theory and applications of convex and non-convex optimization in large-scale machine learning and data science problems.
Here are links to my Google Scholar profile, Twitter account, and CV.
If you are interested in working with me: Please apply to the ECE graduate program and mention my name in your application. If you are already at UT Austin, please send me an email and we can arrange a time to meet.
Selected Recent News
October 2022: New talk on “The Power of Adaptivity in Representation Learning: From Meta-Learning to Federated Learning” at TILOS & OPTML++ Seminar Series at MIT. [Slides] [Video]
September 2022: Our following paper is accepted to NeurIPS 2022:
August 2022: Our paper “Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods” is accepted for publication in Mathematical Programming.
May 2022: Check out my talk on “The Connections between MAML and Representation Learning” at ITA 2022.
May 2022: Our following papers are accepted to ICML 2022:
May 2022: Our following paper is accepted to COLT 2022:
Jan. 2022: Our following paper is accepted to AISTATS 2022 for Oral Presentation:
***(For the full list of news please check the News tab.)
Support
My research is supported by NSF Grants CCF-2007668, ECCS-2127697, and IIS-2112471, an ARO Early Career Program (ECP) Award, the Machine Learning Laboratory at UT Austin, and the NSF AI Institutes IFML and AI-EDGE.
