Kannan Ramchandran

Kannan Ramchandran is the Gilbert Henry Gates Endowed Chair Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. He holds a Ph.D. from Columbia University (1993) and has been at UC Berkeley since 1999. His research focuses on information theory, signal processing, machine learning, and AI, with contributions to distributed coding, federated learning, and robust video transmission. He leads the Berkeley Audiovisual Signal Processing and Communication Systems (BASiCS) Lab and is affiliated with the Simons Institute and CLIMB. Awards include the IEEE Kobayashi Award (2017) and the NSF CAREER Award (1997). He teaches courses like EE 229A (Information Theory) and has advised numerous students in areas like coding theory and distributed systems.

• Education:
• B.E., City College of New York (1982)
• M.S., Columbia University (1984)
• Ph.D., Columbia University (1993)

Research Interests: Distributed source coding, federated learning frameworks, robust communication systems, and AI-driven signal processing. His work bridges theory and practice, with impactful contributions to video compression, error-resilient systems, and modern machine learning paradigms.

Key Contributions: Pioneered DISCUS (Distributed Source Coding using Syndromes), developed SAFFRON for group testing, and advanced federated learning methodologies. His lab’s work on spectrum-blind sampling and diffusion models for MRI reconstruction highlights ongoing innovation.

Awards: IEEE Fellow (2004), Okawa Grant (2000), and multiple teaching awards. His research has been published in top venues like IEEE Transactions on Information Theory and Neural Information Processing Systems.