Nasser Lashgarian Azad

Nasser Lashgarian Azad is an Associate Professor in the Department of Systems Design Engineering and is also a member of the University of Waterloo Centre for Automotive Research (WatCAR), as well as the Waterloo Artificial Intelligence Institute.

Before joining the Systems Design Engineering Department, he was a postdoctoral fellow at the University of California, Berkeley. His primary research interests lie in: (i) intelligent controls and automation with applications to automotive systems as well as autonomous systems like automated vehicles and drones, and (ii) innovative applications of AI methods to solve complex modeling, optimization, control, and automation problems.

He has over 70 publications in high-caliber, peer-reviewed journals such as IEEE Transactions on Intelligent Transportation Systems, Artificial Intelligence Review, Applied Soft Computing, IEEE Transactions on Vehicular Technology, Neurocomputing, Engineering Applications of Artificial Intelligence, IEEE Access, Soft Computing, Mechatronics, Vehicle System Dynamics, Engineering Optimization, Journal of Experimental & Theoretical Artificial Intelligence, and ASME Journal of Dynamic Systems, Measurement, and Control. He received an Early Researcher Award in 2015 from the Ontario Ministry of Research and Innovation.

• Artificial Intelligence
• Automation
• Autonomous Vehicles
• Connected Vehicles
• Control Design
• Dynamics
• Electric and Hybrid Vehicles
• Energy Management Systems
• Motion Prediction
• Navigation Systems
• Optimization
• Secure Vehicle Control
• Space Domain Awareness
• Unmanned Aerial Vehicles

• 2007, Doctorate, Mechanical Engineering, University of Waterloo
• 1997, Master of Applied Science, Mechanical Engineering, Amirkabir University of Technology (Tehran Polytechnic)
• 1994, Bachelor of Applied Science, Mechanical Engineering, Sharif University of Technology

• BME 411 - Optimization and Numerical Methods
  • Taught in 2018, 2019, 2020
• SYDE 252 - Linear Systems and Signals
  • Taught in 2017
• SYDE 352 - Introduction to Control Systems
  • Taught in 2017, 2018, 2019, 2020, 2021
• SYDE 411 - Optimization and Numerical Methods
  • Taught in 2018, 2019, 2020
• SYDE 632 - Optimization Methods
  • Taught in 2020
• SYDE 655 - Optimal Control
  • Taught in 2017, 2018, 2019, 2021
• SYDE 750 - Topics in Systems Modelling
  • Taught in 2017, 2018, 2020

• Y. Lin, J. McPhee, and N. L. Azad, Comparison of deep reinforcement learning and model predictive control for adaptive cruise control, IEEE Transactions on Intelligent Vehicles, 6(2), 2021, 221 - 231
• M. H. Basiri, M. Pirani, N. L. Azad, and S. Fischmeister, Security of vehicle platooning: a game-theoretic approach, IEEE Access, 7, 2019, 185565 - 185579
• S. Tajeddin, S. Ekhtiari, M. Faieghi, and N. L. Azad, Ecological adaptive cruise control with optimal lane selection in connected vehicle environments, IEEE Transactions on Intelligent Transportation Systems, 21(11), 2020, 4538 - 4549
• S. Ekhtiari, M. R. Faieghi, and N. L. Azad, Sensitivity analysis of a real-time trip planning assisted energy management system for connected plug-in hybrid electric vehicles, IEEE Transactions on Vehicular Technology, 68(8), 2019, 7340 - 7352
• B. Sakhdari and N. L. Azad, A distributed reference governor approach to ecological cooperative adaptive cruise control, IEEE Transactions on Intelligent Transportation Systems, 19(5), 2018, 1496 - 1507
• P. Golchoubian and N. L. Azad, Real-time nonlinear model predictive control of a battery-supercapacitor hybrid energy storage system in electric vehicles, IEEE Transactions on Vehicular Technology, 66(11), 2017, 9678 - 9688
• M. Vajedi and N. L. Azad, Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control, IEEE Intelligent Transportation Systems Transactions, 17(1), 2016, 113 - 122
• A. Mozaffari, M. Vajedi , and N. L. Azad, A robust safety-oriented autonomous cruise control scheme for electric vehicles based on model predictive control and online sequential extreme learning machine with a hyper-level fault tolerance-based supervisor, Neurocomputing, 151, 2015, 845 - 856

• Currently considering applications from graduate students. A completed online application is required for admission; start the application process now.
• Has Approved Doctoral Dissertation Supervisor (ADDS) status