Regular biography
Rajeev Sahay is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at the Jacobs School of Engineering, University of California, San Diego. His research interests include networking, social learning networks, machine learning, and wireless communications. Dr. Sahay's work focuses on developing data-driven prediction models for social learning networks and wireless cellular networks, with applications in education and next-generation communications. He joined UCSD in 2023 after serving as a Senior Machine Learning Software Engineer at Saab, Inc., and previously teaching at Purdue University.
Scholar-generated biography
Rajeev Sahay is an Assistant Teaching Professor at UC San Diego, specializing in Adversarial Machine Learning, Deep Learning, Uncertainty Quantification, and Wireless Communications. His research focuses on developing robust machine learning systems capable of defending against adversarial attacks in critical applications such as automatic modulation classification and power allocation in massive MIMO. He also explores the integration of federated learning for privacy-preserving and debiased learning ecosystems, particularly in education and mental health. His work emphasizes the use of deep ensembles and autoencoders for uncertainty quantification and signal processing in complex environments.