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Professor Laura Balzano
Associate Professor of Electrical Engineering and Computer Science
Associate Professor of Statistics, by courtesy

I lead the Signal Processing Algorithm Design and Analysis (SPADA) lab.

You can find my CV here, updated April 2023.

My research projects are in the areas of statistical signal processing, matrix factorization, and optimization, particularly dealing with large and messy data. My interests are theoretical, however, my favorite mathematical problems are motivated by fascinating and important engineering problems.

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Congratulations Dr. Gilman and Dr. Du!

Last fall and winter, SPADA PhD students Kyle Gilman and Zhe Du graduated. Kyle’s thesis was titled “Scalable Algorithms Using Optimization on Orthogonal Matrix Manifolds,” and he continues to make fundamental contributions to interesting modern optimization problems. He is currently an Applied AI/ML Senior Associate at JPMorgan Chase. Zhe’s thesis was titled “Learning, Control, and Reduction for Markov Jump Systems,” with lots of interesting work at the intersection of machine learning and control. He is currently a Postdoctoral researcher working with Samet Oymak and Fabio Pasqualetti. I am excited to follow their work into the future as they make an impact in optimization, machine learning, and control!

MLK Spirit Award

I am honored to have received an MLK Spirit Award from the Michigan College of Engineering. These awards are given to university members who exemplify the leadership and vision of Reverend Dr. Martin Luther King, Jr. through their commitment to social justice, diversity, equity, and inclusion. That commitment is a very high priority for me, so I am grateful that others have felt the impact of my actions. https://ece.engin.umich.edu/stories/laura-balzano-receives-2023-mlk-spirit-award

K-Subspaces Algorithm Results at ICML

I’m excited that our results for the K-Subspaces algorithm were accepted to ICML. My postdoc Peng Wang will be presenting his excellent work; you may read the paper here or attend his session if you are interested. K-Subspaces (KSS) is a natural generalization of K-Means to higher dimensional centers, originally proposed by Bradley and Mangasarian in 2000. Peng not only showed that KSS converges locally, but that a simple spectral initialization guarantees a close-enough initialization in the case of data drawn randomly from arbitrary subspaces. This makes a giant step in a line of questioning that has been open for more than 20 years. Great work Peng!

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Congratulations Dr. Gilman and Dr. Du!

MLK Spirit Award

K-Subspaces Algorithm Results at ICML

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