Sahand Negahban

sahand_n ||at|| eecs {{dot}} berkeley {{dot}} edu
EECS Department
University of California, Berkeley
264 Cory Hall
Berkeley, CA 94720

About me

I am a sixth year graduate student working in Wireless Foundations at the University of California, Berkeley. I am fortunate to be advised by Professor Martin Wainwright. In May 2006, I received my B.S. in Electrical Engineering and Computer Sciences from Cal.
I expect to graduate in December, 2011 with a PhD in EECS and an MA in Statistics.

In May, 2011 I received the Yahoo! KSC award and gratefully acknowledge the support.

Research Focus

I am interested in understanding the role structural constraints play in performing efficient statistical inference.

Publications

• A. Agarwal, S. Negahban, and M. J. Wainwright. Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions Arxiv (preprint), February 2011.

• A. Agarwal, S. Negahban, and M. J. Wainwright. Fast global convergence of gradient methods for high-dimensional statistical recovery. [PDF] Presented at NIPS Conference, December 2010.

• S. Negahban and M. J. Wainwright, Restricted strong convexity and weighted matrix completion: Optimal bounds with noise. [PDF], September 2010.

• S. Negahban, P. Ravikumar, M. J. Wainwright and B. Yu. A unified framework for the analysis of regularized $M$-estimators. Advances in Neural Information Processing Systems, December, 2009. Vancouver. Canada. [PDF] Presentation given at NIPS: [Slides]

  Longer Version

  Technical Report Number 797.

• S. Negahban and M. J. Wainwright. Estimation of (near) low-rank matrices with noise and high-dimensional scaling. Appeared in part at ICML 2010, Haifa, Israel. [PDF]

  Journal Version:

  S. Negahban and M. J. Wainwright (2011). Estimation of (near) low-rank matrices with noise and high-dimensional scaling. Annals of Statistics, Vol 39, Number 2, pp. 1069--1097. [PDF] and Supplementary material.

  Preliminary Versions:

  To appear in Annals of Statistics.
  

  Preliminary Verison: Arxiv Paper
  

• Joint support recovery under high-dimensional scaling: Benefits and perils of $\ell_{1,\infty}$-regularization. S. Negahban and M. J. Wainwright. Advances in Neural Information Processing Systems, December 2008. Vancouver, Canada. [PDF]

  Journal version:

  S. Negahban and M. J. Wainwright (2011), Simultaneous support recovery in high dimensions: Benefits and perils of block $\ell_1/\ell_\infty$-regularization. IEEE Transactions on Information Theory, 57(6):3841--3863, June 2011.

  Preliminary version:

  S. Negahban and M. J. Wainwright. Simultaneous support recovery in high dimensions: Benefits and perils of block $\ell_1/\ell_\infty$-regularization. UC Berkeley Technical Report 774, May 2009. [Full PDF]
  

Reading Group

We have a reading group that meets once a week and discusses various papers involving statistics and computation. The reading group has a webpage that can be found at Reading Group.

Teaching

In the Fall of 2008 I was a Graduate Student Instructor for CS 281A, Berkeley's course on statistical learning theory and graphical models. In the Fall of 2010, I was a Graduate Student Instructor for EECS20n, Structure and Interpretation of Signals and Systems.

Courses

Measure Theory and Functional Analysis, Information Theory and Statistics, Probability Theory, Advanced Statistical Learning Theory, Statistical Learning Theory, Topology and Measure Theory, Statistical Signal Processing, Large Scale Convex Optimization, Convex Optimization, Advanced Topics in Information Theory, Mathematical Statistics