Adel Javanmard

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Associate Professor

Data Sciences and Operations Department (Statistics group)

Marshall School of Business

(by courtesy) Department of Computer Science

University of Southern California


Office: 300A Bridge Hall, University of Southern California, Los Angeles, CA 90089
Tel: (213)821-4193
Email: ajavanma(at)

Here are links to my Google Scholar Profile, LinkedIn Profile and a short bio in the third-person.

About Me

I am an Associate Professor (with tenure) in the department of Data Sciences and Operations, Marshall School of Business at the University of Southern California (USC). I also hold a courtesy appointment at the Computer Science, USC Viterbi School of Engineering.

I am broadly interested in design and analysis of statistical methods for large-scale data, high-dimensional inference, network analysis, non-convex optimization and personalized decision-making. For more details, please see my publications.

Prior to joining USC, I was NSF CSoI postdoctoral fellow with worksite at Stanford University and UC Berkeley. I obtained my Ph.D. in Electrical Engineering from Stanford University advised by Andrea Montanari.


I am Associate Editor for Operations Research in the “Machine Learning and Data Science” department. I am also serving on the 2022 Dantzig Dissertation Prize Committee.

What's new?

  • September 2022: Received an Adobe Data Science Faculty Research award. Thanks Adobe for your support!

  • September 2022: A new paper on GRASP, a goodness of fit test for classification.

  • May 2022: I am honored to receive a Golden Apple Award from USC Marshall. One of the two recipients in the school for teaching in undergraduate core classes!

  • January 2022: New paper on the curse of overparametrization in adversarial training.

  • November 2021: A new paper on PCR test. (No! nothing to do with COVID :) )

  • October 2021: New paper on theoretical understanding of adversarial training for latent models.

Selected Publications

For the complete list of publications, check here

Precise Tradeoffs in Adversarial Training for Linear Regression
Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani
Annual Conference on Learning Theory (COLT), 2020

Analysis of a Two-Layer Neural Network via Displacement Convexity
Adel Javanmard, Marco Mondelli, Andrea Montanari
Accepted for publication in Annals of Statistics, 2019

Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Negin Golrezaei, Adel Javanmard and Vahab Mirrokni
Accepted for publication in Operations Research, 2019.
(Preliminary version of this paper accepted to NeurIPS 2019.)

Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Mahdi Soltanolkotabi, Adel Javanmard and Jason D. Lee
in IEEE Transaction on Information Theory, 65(2), pages 742-769, 2018.

Phase Transitions in Semidefinite Relaxations [Website]
Adel Javanmard, Andrea Montanari and Federico Ricci-Tersenghi
In Proceedings of the National Academy of Sciences (PNAS), 113(16): E2218-E2223, 2016

Confidence Intervals and Hypothesis Testing for High-Dimensional Regression [Website]
Adel Javanmard and Andrea Montanari
in Journal of Machine Learning Research, 15(1): 2869-2909, 2014.

Information-Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing
David L. Donoho, Adel Javanmard, Andrea Montanari
IEEE Transaction on Information Theory, vol. 59, no. 11, pp 7434-7464, Nov 2013.