Rate My Professor Usman Khan

Usman A. Khan is an Adjunct Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, where he holds a secondary appointment in Computer Science and directs the Signal Processing and RoboTic Networks (SPARTN) laboratory. He earned a B.S. degree in Electrical and Computer Engineering from the University of Engineering and Technology Lahore, Pakistan, in 2002, an M.S. degree from the University of Wisconsin-Madison in 2004, and a Ph.D. degree from Carnegie Mellon University in 2009. Following his Ph.D., Khan conducted postdoctoral research in the GRASP laboratory at the University of Pennsylvania and served as a Visiting Professor at KTH Royal Institute of Technology in Sweden. His research interests include signal processing, optimization and control, stochastic dynamical systems, machine learning, and artificial intelligence, with applications to data and network science, autonomous multi-agent systems, Internet of Mobile Things, fleets of driverless vehicles, and smart-and-connected cities. Khan has authored more than 120 articles in peer-reviewed journals and conference proceedings and holds multiple patents.

Khan's contributions have earned him the National Science Foundation CAREER Award in 2014 and elevation to IEEE Fellow in 2025 for outstanding work in optimization, control, and stochastic dynamical systems with applications in AI and machine learning. Additional recognitions include the 2022 EURASIP Best Paper Award for a publication in the EURASIP Journal on Advances in Signal Processing, Best Student Paper Awards at the 2014 IEEE International Conference on Networking, Sensing and Control, and the IEEE Asilomar Conferences in 2014 and 2016. A senior member of IEEE, he serves on the editorial boards of IEEE Transactions on Signal Processing (since 2021), IEEE Transactions on Signal and Information Processing over Networks (since 2019), and IEEE Open Journal of Signal Processing (since 2019), and was Chief Editor for the Proceedings of the IEEE special issue on Optimization for Data-driven Learning and Control (November 2020). Key publications encompass 'Distributing the Kalman Filter for Large-Scale Systems' (2008), 'DEXTRA: A Fast Algorithm for Optimization Over Directed Graphs' (IEEE Transactions on Automatic Control, 2017), 'Linear Convergence in Optimization over Directed Graphs with Row-Stochastic Matrices' (IEEE Transactions on Automatic Control, 2018), and 'Distributed Heavy-Ball: A Generalization and Acceleration of First-Order Methods with Gradient Tracking' (2019).