Contact Laura

Laura Balzano
Office: EECS 4114
1301 Beal Ave, Ann Arbor, MI 48109
Phone: (734) 615-9451

Army Young Investigator

I am very excited that my project “Mathematics for Learning Nonlinear Generalizations of Subspace Models in High Dimensions” has won the Army Young Investigator award! Subspace models are widely used due to simplicity and ease of analysis. However, while these linear models are very powerful in many high-dimensional data contexts, they also often miss out on important nonlinearities in real data. This project aims to extend recent advances in signal processing to the single-index model and the nonlinear variety model. Read the department’s announcement here.

Postdoc Opportunity at the University of Michigan

to begin in spring 2019.

Please email Laura Balzano <girasole@umich.edu> with the subject “Joining the Balzano lab — postdoc 2019” if you are interested.

We are seeking a postdoc who is interested in applying machine learning techniques to real-time dynamic data analysis. While machine learning has advanced significantly over the last decade, its application to dynamic time-varying data is still in its infancy. This project will focus on three ML areas: online learning, stochastic gradient methods, and streaming PCA. We will work on theory to understand how the standard approaches behave when the data are time-varying, develop appropriate models for time-varying data, and develop novel approaches along with convergence theory. Our main applications focus will be power systems engineering and computer vision. In power systems, we will develop methodologies to infer the real-time behavior of aggregations of distributed energy resources from hierarchical, heterogeneous, and incomplete measurements of power system quantities. In computer vision, we will develop real-time algorithms for object tracking and activity recognition in video.

Optimally Weighted PCA for High-dimensional Heteroscedastic Data

Today I had the opportunity to speak about very recent results by my student David Hong (joint work also with Jeff Fessler) in analyzing asymptotic recovery guarantees for weighted PCA for high-dimensional heteroscedastic data. In the paper we recently posted online, we have asymptotic analysis (as both the number of samples and dimension of the problem grow to infinity, but converge to a fixed constant) of the recovery for weighted PCA components, amplitudes, and scores. Those recovery expressions allow us to find weights that give optimal recovery, and the weights turn out to be a very simple expression involving only the noise variance and the PCA amplitudes. To learn more, watch my talk here, and let us know if you have any questions!

Contact Laura

Recent News

Army Young Investigator

Postdoc Opportunity at the University of Michigan

Optimally Weighted PCA for High-dimensional Heteroscedastic Data

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