Sujay Sanghavi

Assistant Professor, Electrical and Computer Enigneering.
University of Texas, Austin
Room 425, ENS Building
Electrical and Computer Enigneering
University of Texas at Austin
Austin, TX 78712

(512) 475-9798
lastname "at" mail "dot" utexas "dot" edu

Brief Biography

I completed my B. Tech in Electrical Engineering from IIT Bombay in 2000, after which I joined the University of Illinois at Urbana-Champaign. There I obtained my MS in Electrical Engineering in 2002, MS in Math in 2005, and PhD in Electrical Engineering in 2006; my graduate research focused on communication network algorithms. After obtaining my PhD, I joined LIDS, MIT as a postdoc. There I worked on large-scale statistical inference and learning. In Fall 2008 I joined the ECE department at Purdue as an assistant professor, and in Fall 2009 I joined the ECE department at UT Austin. My primary research interests are in probability and optimization, as applied to the design and analyis of algorithms in networks, communications and signal processing.


NEWS: Received an NSF CAREER Award for our research on Networks and Statistical Inference (awarded January 2010)
Teaching:
Spring 2010: EE381V Sparsity, Structure and Algorithms (graduate)
Fall 2009: EE381J Probability and Stochastic Processes I (graduate)
Fall 2008 (in Purdue): ECE 302 Probabilistic Methods in Electrical and Computer Engineering (undergraduate)
Program Commitees: Infocom 2010, ICCCN 2009, MobiHoc 2009, WCNC 2009, WiCon 2008

Publications by Area, with Descriptions

Wireless and Sensor Networks
Routing, Scheduling, Gossip
Markov Random Fields, Graphical Models, Message Passing
Statistical Inference and Learning
Game Theory

Chronological List of Publications


Graduate Students

Rajaganesh Ganesh Ali Jalali Praneeth Netrapalli

Recent Publications

Sparse and Low-Rank Matrix Decompositions Venkat Chandrasekaran, Sujay Sanghavi, Pablo Parrilo, Alan S. Willsky
Accepted to the 15th IFAC Sypmposium on System Identification (SYSID), 2009
Submitted journal version: Rank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. This paper shows how to exactly decompose the given matrix into its sparse and low-rank components, via convex programming. Based on a new uncertainty principle between the rank and sparsity of a matrix.

Tightness of LP via Max-product Belief Propagation Sujay Sanghavi, Devavrat Shah
Accepted to Artificial Intelligence and Statistics (AISTATS) 2009.
Shows that for certain random instances of the weighted independent set problem, the (simple, edge-based) LP relaxation is tight with high probability. Uses belief propagation as a proof technique.

  • Node Weighted Scheduling Gagan Gupta, Sujay Sanghavi, Ness Shroff Sigmetrics 2009
  • The standard approach to scheduling in input-queued switches determines schedules based on the weights of edges. We develop an alternative approach that looks at the weights of nodes instead. This gives new 100% throughput algorithms with lower complexity and lower delay; proof involves a new Lyapunov function.