Prof. Nadakuditi joined U-M faculty in 2009, and has already developed significant improvements to the EECS curriculum. He has taken subject matter that has seemed accessible to hard-core specialists only and made it understandable and relevant to a wider group of students.
Most notably, he revamped EECS 551: Matrix Methods for Signal Processing, Data Analysis and Machine Learning, from an introductory graduate class taken primarily by signal processing students to a course taken by a large array of students from other departments. Enrollment jumped from about 50 graduate students several years ago to a peak of more than 200 students.
He also created the course EECS 453: Applied Matrix Algorithms for Signal Processing, Data Analysis and Machine Learning to make the subject matter accessible to undergraduate students. Undergraduate and graduate students attend the same lectures, but have separate discussion sections, homework, and exams.
He will also be teaching the new EECS 598: Computational Data Science. This course will introduce "Bookalive", a platform Prof. Nadakuditi has been building, which unlocks book sections as students correctly implement algorithms for signal processing, machine learning and data analysis. "The idea is to have students build up complicated algorithms for pattern recognition step-by-step," says Nadakuditi. "This is so they can get the thrill of discovery when the algorithm 'clicks' into place and starts performing a task like handwriting recognition."
Prof. Nadakuditi is known for his passion for reaching as many students as possible, and enriching their education. He revived a weekly fall seminar series, EECS 500, in which faculty present research to first-year graduate students.
Prof. Nadakuditi became very involved in the Engineering Graduate Symposium, where engineering graduate students showcase their work. He expanded the symposium to include recent PhD alumni to serve as poster judges, as well as offer networking opportunities for participants.
And he has been involved in separate summer camps on data science that were designed for high school students and those with advanced knowledge in the field.
In his research, Prof. Nadakuditi focuses on statistical estimation and learning, signal processing for sensing and sensor networks, random matrix theory and applications, random graphs and light transport through opaque random media.
Prof. Nadakuditi has received the DARPA Directors Award, DARPA Young Faculty Award, IEEE Signal Processing Society Best Young Author Paper Award, Office of Naval Research Young Investigator Award, and the Air Force Research Laboratory Young Faculty Award.