Machine Learning, Signal Processing & Information Theory

How can we teach our machines to reason? Researchers in machine learning, signal processing, and information theory develop algorithms and computer-based procedures to mimic human perceptions, thoughts, and actions. By looking at large amounts of data, learning methods are able to extract relevant information from video, photographs, audio, text, ultrasound, medical images, as well as a wide range of sensors and experimental measurements. But what is “information,” and how is it gathered, stored, and processed? ECE researchers tackle these kinds of fundamental questions, applying mathematical reasoning, optimization procedures, and computational tools to engineering challenges. We seek technological solutions to problems in a variety of domains such as communications, medicine, the physical sciences, and the humanities.

Azadeh Davoodi

Position title: Professor

John Gubner

Position title: Associate Chair for Operations, Professor

Yu Hen Hu

Position title: Professor

Younghyun Kim

Position title: Assistant Professor

Ramya Korlakai Vinayak

Position title: Assistant Professor

Kangwook Lee

Position title: Assistant Professor

Bernard Lesieutre

Position title: Associate Chair for Undergraduate Studies, Professor

Mikko Lipasti

Position title: Philip Dunham Reed Professor

Paul H. Milenkovic

Position title: Associate Professor

Pedro Morgado

Position title: Assistant Professor

Robert Nowak

Position title: Keith and Jane Morgan Nosbusch Professor of Electrical and Computer Engineering

Dimitris Papailiopoulos

Position title: Assistant Professor

Parameswaran (Parmesh) Ramanathan

Position title: Professor, Associate Dean

Line Roald

Position title: Assistant Professor, Grainger Institute Fellow

William Sethares

Position title: Professor

Barry Van Veen

Position title: Associate Chair for Graduate and Online Studies, Lynn H. Matthias Professor

Andreas Velten

Position title: Assistant Professor