Rate My Professor Saif Jabari

Jabari's research specializes in the theoretical aspects of traffic flow, data analysis, modeling uncertainty, and emergent phenomena, with applications in traffic operations including traffic state estimation and prediction, distributed traffic control, and cybersecurity. His interests also include traffic flow theory, algorithms for urban network traffic management, applied probability, and statistical mechanics. He has earned the 2012 Milton Pikarsky Memorial Award for the best doctoral dissertation in science and technology from the Council of University Transportation Centers, the Best MS Thesis in Civil Engineering from the University of Minnesota in 2009, ITS Student of the Year in 2010 from the University of Minnesota ITS Institute, and an Award for Innovation and Outstanding Teaching from NYU Abu Dhabi Engineering. Key publications encompass "Nonlinear traffic prediction as a matrix completion problem with ensemble learning" (Transportation Science, 2022), "Stop and go: Exploring backdoor attacks in deep reinforcement learning-based traffic congestion control systems" (IEEE Transactions on Information Forensics and Security, 2021), "A real-time dispatching strategy for shared automated electric vehicles with performance guarantees" (Transportation Research Part E, 2021), "Sparse travel time estimation from streaming data" (Transportation Science, 2020), and "Traffic state estimation using stochastic Lagrangian dynamics" (Transportation Research Part B, 2018). In 2019, the Secretary General of the World Road Association praised his proposed traffic management plan adaptable to driverless vehicles.