Kurt Keutzer

Kurt Keutzer is a Professor Emeritus and Professor in the Graduate School at the University of California, Berkeley, affiliated with the EE department. His research focuses on Artificial Intelligence (AI), Computer Architecture & Engineering (ARC), and Scientific Computing (SCI). He is associated with the Berkeley Artificial Intelligence Research Lab (BAIR) and Berkeley Deep Drive (BDD). Keutzer has conducted research in making machine learning and AI algorithms more computationally efficient, with a current emphasis on Large Language Models and complex agentic systems. He has also been involved in industry, including roles at Synopsys and co-founding several companies. His work is supported by industrial sponsors, and he has received awards, including the 'Most Influential Paper of the 1980s' from the Design Automation Conference.

Kurt Keutzer is a Professor of the Graduate School, EECS, at the University of California, Berkeley, known for his research in artificial intelligence systems, deep learning, and efficient computation. His work focuses on developing compact, high-performance neural network models that enable real-time applications in autonomous systems and computer vision. Keutzer's research includes optimizing model size and computational efficiency, as seen in projects like SqueezeNet, Squeezeseg, and FBNet. He also explores hardware-aware design and quantization techniques to enhance neural network inference. His contributions span both theoretical and applied aspects of deep learning, with an emphasis on practical deployment in resource-constrained environments.