Research Bio

Anant Sahai's research straddles machine learning, information theory, control theory, and wireless communications. He investigates the fundamental nature of deep learning, cognitive radio and spectrum sensing, and how agents coordinate under partial information. He is well known for contributions to overparameterized learning, in-context learning, cooperative spectrum sharing, and the theoretical foundations control with communications constraints.

He is Professor in the Electrical Engineering and Computer Sciences (EECS) Department at UC Berkeley, holding the Qualcomm Chair, and has led the Data & Machine Learning working group in the NSF Center SpectrumX.

Research Expertise and Interest

machine learning, artificial intelligence, information theory, communications theory, wireless communication, cognitive radio, distributed control, spectrum sensing, spectrum sharing, spectrum policy, power consumption in communications systems