Aryan Mokhtari

Assistant Professor, Dept. of Electrical & Computer Engineering (ECE), UT Austin
Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Computer Engineering
Affiliated with:
- - NSF AI Institute for Foundations of Machine Learning (IFML)
  - NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE)
  - Wireless Networking & Communications Group (WNCG)
Google Scholar / Twitter / CV

My current research focuses on the theory and applications of convex and non-convex optimization in large-scale machine learning and data science problems.

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

- Jan. ’23: Paper at AISTATS 2023: “A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem”
- Nov. ’22: Survey on “Conditional Gradient Methods”. The website for our monograph here.
- Oct. ’22: New talk at TILOS & OPTML++ Seminar Series at MIT on “The Power of Adaptivity in Representation Learning: From Meta-Learning to Federated Learning” at [Slides] [Video]
- Sep. ’22: Paper at NeurIPS 2022: “FedAvg with Fine Tuning: Local Updates Lead to Representation Learning”
- Aug. ’22: Paper at Mathematical Programming: “Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods”

***(For the full list of news please check the News tab.)

Support

My research is supported by NSF Grants CCF-2007668 and ECCS-2127697, an ARO Early Career Program (ECP) Award, the Machine Learning Laboratory at UT Austin, and the NSF AI Institutes IFML and AI-EDGE.