Adel Javanmard

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Department of Electrical Engineering

Stanford University


Email: adelj at

Google Scholar Profile

About Me

I am currently a CSoI Postdoctoral Fellow with worksite at Stanford University and UC Berkeley. Starting this summer, I will be an Assistant Professor at USC Marshall in the department of Data Sciences and Operations (Statistics group).

I completed my Ph.D. in Electrical Engineering at Stanford University, where I worked with Andrea Montanari. Before joining Stanford in 2009, I received B.S. degrees in Electrical Engineering and Mathematics from Sharif University of Technology.

I am broadly interested in the design and analysis of algorithms that reveal latent structures in data sets. My focus is to exploit such structures to develop both computationally and statistically efficient procedures for inference. In my research, I use and improve on techniques from various fields such as optimization, graphical models, statistics and probability.

Research Interests

  • High-dimensional statistical inference

  • Design and analysis of algorithms for large-scale data

  • Iterative methods, message passing algoritms, optimization


Winner of the 2015 Thomas Cover Dissertation Award, from the IEEE Information Theory Society. [Link]

Selected Publications

For the complete list of publications, check here.

Confidence Intervals and Hypothesis Testing for High-Dimensional Regression [Website]
Adel Javanmard and Andrea Montanari
To appear in Journal of Machine Learning.
Short version published in Advances in Neural Information Processing Systems Foundation (NIPS), 2013.

Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory
Adel Javanmard and Andrea Montanari
To appear in IEEE Transaction on Information Theory.

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.

Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Animashree Anandkumar, Daniel Hsu, Adel Javanmard, and Sham M. Kakade
International Conference on Machine Learning (ICML), 2013.

Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
Morteza Ibrahimi, Adel Javanmard, Benjamin Van Roy
Advances in Neural Information Processing Systems Foundation (NIPS), 2012.

Localization from Incomplete Noisy Distance Measurements
Adel Javanmard, Andrea Montanari
Foundations of Computational Mathematics, vol. 13, no. 3, pp 297-345, June 2013.