The Michigan Engineer News Center

Laura Balzano receives ARO Young Investigator Award to improve high-dimensional big data problems

Applications include managing large networked systems, such as sensor networks, power grids, or computer networks.| Short Read

Prof. Laura Balzano received a Young Investigator Program (YIP) award from the Army Research Office (ARO) to support research leading to improved methods for solving big data problems using statistical signal processing tools. The project is called “Mathematics for Learning Nonlinear Generalizations of Subspace Models in High Dimensions.” Applications include automatic feature identification in videos (similar to face ID in photos) and management of large networked systems, such as sensor networks, power grids, or computer networks.

In this project, Balzano is extending some of the very powerful tools for linear modeling to nonlinear models, using two mathematical ideas, one called the “single index model” and the other “algebraic variety model.” Both are nonlinear models that have important relationships to well-studied linear models, and she hopes this will lead to general but impactful new mathematical tools. Her approach is expected to lead to, for example, improved scene analysis in video and a better understanding of problems happening in networked systems.

The type of data Balzano considers is considered high-dimensional and massive; it is also “messy; nearly every data collection effort has issues with missing data, faulty and corrupted data, and uncalibrated measurement devices. When these data are collected in dynamic and even hostile environments, the inability to collect, store, and process every measurement of interest affects the data fidelity,” according to Balzano.

“The proposed research,” says Balzano, “will extend recently developed mathematics of high-dimensional signal processing to challenging new contexts where the data models and/or the measurement models may be nonlinear.”

EnlargeLaura Balzano

Balzano directs the Signal Processing Algorithm Design and Analysis (SPADA) lab, which studies algorithms for statistical signal processing and machine learning with applications in data analysis, computer vision, environmental monitoring, image processing, control systems, power grids, genetic expression data analysis, consumer preference modeling, and computer network analysis.

She has received an AFOSR Young Investigator Award, an Intel Early Career Faculty Honor Program Award and a 3M Non-Tenured Faculty Award.

According to ARO, “YIP awards are one of the most prestigious honors bestowed by the Army on outstanding scientists beginning their independent careers. The objective of the YIP is to attract outstanding young university faculty members to pursue fundamental research in areas relevant to the Army, to support their research in these areas, and to encourage their teaching and research careers.”

Laura Balzano
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The electrons absorb laser light and set up “momentum combs” (the hills) spanning the energy valleys within the material (the red line). When the electrons have an energy allowed by the quantum mechanical structure of the material—and also touch the edge of the valley—they emit light. This is why some teeth of the combs are bright and some are dark. By measuring the emitted light and precisely locating its source, the research mapped out the energy valleys in a 2D crystal of tungsten diselenide. Credit: Markus Borsch, Quantum Science Theory Lab, University of Michigan.

Mapping quantum structures with light to unlock their capabilities

Rather than installing new “2D” semiconductors in devices to see what they can do, this new method puts them through their paces with lasers and light detectors. | Medium Read