I am an assistant professor in the Electrical and Computer Engineering Department at Iowa State University.

I am interested broadly in problems related to data processing and machine learning. My research focuses on developing fast and robust algorithms for diverse problems in data sensing and inference.

Prior to joining ISU, I was a post-doctoral associate in the Theory of Computation (TOC) group at MIT where I worked with Piotr Indyk. I received my Ph.D. at Rice University under the supervision of Rich Baraniuk.

[CV]   [Google Scholar]  


January 2017

New paper on graph-based outage identification accepted to IEEE PESGM 2017.

December 2016

NVIDIA Corporation kindly donated a Titan X Pascal to our group as part of their GPU Grant Program.

December 2016

New paper on recovery from sinusoidal features accepted at ICASSP 2017.

November 2016

GPUFish, a new parallel computing toolbox for very large-scale matrix completion problems, is now public. Attendant paper here.

October 2016

I am a recipient of the 2016 Warren Boast Undergraduate Teaching Award.

September 2016

I am part of a team of transportation engineers and data scientists that got awarded an NSF PFI:BIC grant for a project focussing on traffic incident management. Thanks to Anuj Sharma (CCEE Department and INTRANS) for leading the team!

August 2016

More new papers on signal demixing from nonlinear observations.

June 2016

New papers on matrix recovery, signal demixing, and structured sparsity.

April 2016

I am a recipient of the NSF CRII Award. Thanks NSF!!

recent publications
For a full list, click here.

Stable Recovery of Sparse Vectors from Random Sinusoidal Feature Maps
with Mohammadreza Soltani.
ICASSP, March 2017.
Fast Recovery from a Union of Subspaces
with Piotr Indyk and Ludwig Schmidt.
Neural Information Processing Systems (NIPS), December 2016.
Data-Driven Prognostics of a Li-Ion Rechargeable Battery Using Bilinear Kernel Regression
with Charles Hubbard, John Bavslik, and Chao Hu.
Proc. Annual Conference of the Prognostics and Health Management (PHM) Society, October 2016.
Fast Algorithms for Demixing Sparse Signals from Nonlinear Observations
with Mohammadreza Soltani.
Preprint, August 2016.
NuMax: A Convex Approach for Learning Near-Isometric Linear Embeddings
with Aswin Sankaranarayanan, Wotao Yin, and Richard Baraniuk.
IEEE Trans. Signal Processing, November 2015.
Fast Algorithms for Structured Sparsity
with Piotr Indyk and Ludwig Schmidt.
Bulletin of the European Association of Theoretical Computer Science, October 2015.
Approximation Algorithms for Model-Based Compressive Sensing
with Piotr Indyk and Ludwig Schmidt.
IEEE Trans. Information Theory, September 2015.
A Nearly Linear-Time Framework for Graph-Structured Sparsity
with Piotr Indyk and Ludwig Schmidt.
In Proc. Int. Conf. Machine Learning (ICML), July 2015.

For detailed descriptions, click here.

Under construction.