Navid's research interests span a wide spectrum of topics in machine learning, optimization, control, and networks. His work has focused on various problems at the intersection of these areas, including deep learning theory, nonconvex optimization, distributed algorithms for machine learning, networked markets, and rumor spread in social networks. The Role of Optimizers in Generalization of Deep Learning
Stochastic Optimization Algorithms in Estimation
Optimization and Pricing in Markets
Distributed Optimization for Learning and Beyond
Spreading Processes in Complex Networks
