Mehrdad Mahdavi

Mehrdad Mahdavi is an Associate Professor in the field of Computer Science and Engineering. He has secured multiple National Science Foundation (NSF) grants, including EFRI BRAID: Neuroscience Inspired Visual Analytics, CAREER: Foundations of Collaborative Machine Learning, and CNS Core: Medium: When Next Generation Wireless Networks Meet Machine Learning. His research spans diverse areas of machine learning and optimization.

His work focuses on Machine Learning, Optimization Algorithms, Quantum Computing, and Graph Neural Networks. He investigates memory-efficient training methods, generalization in unsupervised learning, quantum sampling for complex distributions, and distributed algorithms for collaborative learning. His research also intersects with healthcare applications, such as AI-driven lung ultrasound analysis for diseases like COVID-19.

Recent publications highlight trends in Continual Learning, Temporal Graph Learning, Quantum Algorithms, and Federated Learning. His studies address theoretical frameworks for generalization, optimization challenges in non-convex and non-logconcave problems, and scalable solutions for graph-based machine learning tasks. He has contributed to energy consumption modeling, quantum sampling, and distributed risk minimization.

As a Principal Investigator (PI) and Co-PI, he has led NSF-funded projects on collaborative machine learning, neuroscience-inspired visual analytics, and AI-enabled materials discovery. These grants underscore his focus on foundational research with applications in wireless networks, quantum computing, and interdisciplinary domains.