Rajeev Sahay

Rajeev Sahay is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds a Ph.D. and M.S. from Purdue University (2022 and 2021) and a B.S. from the University of Utah (2018). Prior to UCSD, he worked as a Senior Machine Learning Software Engineer at Saab, Inc., and taught courses at Purdue, earning the Purdue Engineering Dean’s Teaching Fellow award in 2021.

His research bridges machine learning and networking, focusing on social learning networks (SLNs) for personalized education and robust wireless communications in adversarial environments. Key areas include federated learning for privacy preservation, adversarial attack mitigation, and next-generation 6G wireless systems. His work emphasizes data-driven methodologies to enhance learning outcomes and secure communication networks against vulnerabilities.

Dr. Sahay’s teaching spans undergraduate and graduate courses in machine learning, data science, and programming languages (C/C++/Python), with a passion for tailoring curricula to student needs. His publications reflect expertise in federated learning, adversarial defense mechanisms, and signal processing, with recent contributions to AI-assisted education and robust wireless systems.

Scientific Awards:

• Purdue Engineering Dean’s Teaching Fellow (2021)

Teaching and Research Interests:

• Machine Learning Pedagogy
• Adversarial Robustness in ML Systems
• Federated Learning Applications
• Social Learning Network Analysis
• 6G Wireless Communication Security