I’m very excited that my proposal for blind calibration in environmental sensor networks has been given the NSF BRIGE award. Now we get to roll up our sleeves and do the work! You can read the original paper that inspired this proposal and the more detailed book chapter here.


Jun He, Dejiao Zhang, and I are happy to share our preprint and our code on t-GRASTA, a variant of GRASTA which includes estimation of geometric transformations of the data (such as translations and rotations of images, so that we can deal with jitter).

GROUSE local convergence

Steve Wright and I are pleased to share our work on the local convergence of GROUSE, an algorithm for estimating a subspace from partially observed vectors, which is now on the arxiv.

UW-Madison ECE Dissertation Award

Laura is very honored to receive the 2012 award for Best Dissertation from the University of Wisconsin, Madison ECE Department. The award even came with some cash, which Rob Nowak pointed out will buy a LOT of beer when we celebrate during the SILO meeting in June. Hope to see you there.

Thanks EECS 600 Students!

Laura wants to thank her students in EECS 600 for being so awesome and dedicated. A great first class to teach at Michigan!

EPA Air Sensors Meeting

Laura gave a plenary talk at the EPA Air Sensors meeting in Raleigh in March. There are lots of exciting opportunities for signal processing in air quality sensing!

IPAM Meeting

The IPAM meeting on Adaptive Data Analysis and Sparsity was a big success. Thank you to everyone who participated.


The GRASTAcam code is availabile for download here. This code uses OpenCV to run Grasta using the camera on your computer. It was written by Arthur Szlam (with makefile by Jia Xu, Thanks Jia!) in C using the Intel MKL library.


Grassmannian Robust Adaptive Subspace Tracking Algorithm (GRASTA), is an algorithm for subspace identification and tracking in the presence of corrupted and missing data. The algorithm is derived using the Augmented Lagrangian for an l1 cost function. See our page on GRASTA for code and a description of the algorithm.



Grassmannian Rank-One Update Subspace Estimation (GROUSE), is an online algorithm for subspace identification and tracking when data vectors are incomplete. See our page on GROUSE for details about the algorithm. GROUSE can be used for matrix completion; download grouse.m for the Matlab function and rungrouse.m to see how to run it.