Kurt Keutzer

Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP.

Educations:

• 1984, PhD, Computer Science, Indiana University

Kurt Keutzer's research interests span Artificial Intelligence, Computer Architecture, and Scientific Computing, with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers, LLM inference efficiency, and autonomous driving. He also contributes to multimodal AI and self-supervised learning frameworks.

Scientific Awards:

• Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996)
• DAC's Most Influential Paper Award (2023)
• Top Ten Cited Author and Paper at DAC
• Best Paper Awards at Embedded Vision Workshop and ICPP

Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models, sparse attention mechanisms, and multi-agent coordination for complex tasks.