Student Spotlight: Nick Asendorf – Matrix Musician

Nick specializes in the area of machine learning and statistical signal processing.

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Nick Asendorf
Specialty: Machine Learning and Statistical Signal Processing
Advisor: Prof. Raj Nadakuditi
Honors: U-M Graduate Symposium Best Poster Award (2012, 2013); U-M Rackham Merit Fellowship
: President of ECE Graduate Student Council, Carillonneur

Nick Asendorf visited a lot of graduate schools after receiving his bachelor’s degree in computer engineering at the University of Maryland in 2010. He knew he preferred a large state school to a small private school and wanted to be sure to find the right fit. In the end, Nick said, “It was Raj [Prof. Raj Nadakuditi] that brought me to Michigan. He said we would get started right away working on problems. That sold me.”

Nick specializes in the area of machine learning and statistical signal processing. More specifically, he has been employing random matrix theory to create new algorithms that aim to improve multi-modal correlation analysis.

“For example,” said Nick, “if your two modes are an audio stream and associated images, you would assume that the audio is somehow correlated with the images. I am working on how to best extract that correlation using theoretically sound algorithms. There are some fundamental algorithms that people use to do this, but if you actually go through and derive that algorithm in a different manner you see that you can improve the outcome by twiddling some knobs a little differently.”

The same theory can be applied to multiple applications, such as insuring that a search for songs that have been tagged by Pandora will generate a more accurate result than is possible with traditional algorithms.

This past year, Nick has tried to improve the life of ECE graduate students through his role as President of the ECE Graduate Student Council (GSC). The GSC organizes socials, intramural sports teams and lectures. “Our events help us get to know each other better,” said Nick.

He also participates in SPeecs, an informal lecture series and peer review group created to contribute to the professional development of students in signal processing.

Nick says that Ann Arbor alone is a great reason to choose Michigan. “Ann Arbor is a really fun college town with a lot of little nooks and crannies to explore,” said Nick, “and ample opportunities to escape research.”

One of the ways Nick has found to take a break and recharge is by playing the carillon in the bell tower on North Campus. He was a paid organist at his local church in high school, and took two classes at Michigan to learn to be a carillonneur. “The carillon lets you force everyone in town to listen to whatever you want to play,” said Nick, “which is a nice perk.”

What’s in Nick’s future?

While eager to go straight into industry at a large company like Google or Amazon, he also dreams of combining his theoretical systems expertise with his love for basketball and doing sports analytics.