Faculty Profiles

Modern day networks, such as cellular networks and social networks, have become an integral part of our daily lives. Due to the massive growth of such networks, data-driven methodologies are essential for intelligently analyzing, optimizing, and characterizing communication links.

Dr. Sahay’s work focuses on developing intelligent data-driven prediction models for two types of networks: social learning networks (SLNs) and wireless cellular networks. The application of SLNs is in education (applied in both technical and non-technical courses), aiming to improve learning in in-person and online courses through intelligently predicting beneficial learner interactions. The application in wireless networks focuses on efficient next-generation communications (6G-and-beyond) in networks with a large number of connected of devices (e.g., in the Internet of Things network). In this capacity, Dr. Sahay primarily focusses on identifying and addressing the vulnerabilities of machine learning-based wireless networks.

Capsule Bio:

Rajeev Sahay earned his Ph.D. and M.S. in Electrical and Computer Engineering from Purdue University in 2022 and 2021, respectively, and his BS in Electrical Engineering from the University of Utah in 2018. From 2022 – 2023, he was a Senior Machine Learning Software Engineer at Saab, Inc., supporting a variety of research programs in machine learning and adversarial robustness. At Purdue, he taught and developed several undergraduate and graduate courses in a variety of settings ranging from undergraduate laboratories to graduate level machine learning courses. In 2021, he was named a Purdue Engineering Dean’s Teaching Fellow. In 2023, he joined the Jacobs School of Engineering at the University of California San Diego as an Assistant Teaching Professor in the Department of Electrical and Computer Engineering.

Selected Publications:

Google Scholar Publications