Ph.D. ​University of Pennsylvania, 2015M.S. ​University of Pennsylvania, 2012B.S. ​PEC University of Technology, India, 2009

"Making things go, where they need to go - autonomously !"

Madhur Behl, Associate Professor


Research interests include: Robotics, Artificial Intelligence, Autonomous Systems, Cyber-Physical Systems, Smart Cities, and Internet of Things


Dr. Madhur Behl is an associate professor in the departments of Computer Science, and Systems and Information Engineering, and a member of the Cyber-Physical Systems Link Lab at the University of Virginia.  He conducts research at the confluence of Machine Learning, Predictive Control, and Artificial Intelligence with applications in Cyber-Physical Systems, Autonomous Systems, Robotics, and Smart Cities.

Examples include: fully autonomous racing at the limits of control (Agile Autonomy), safety of autonomous vehicles (Safe Autonomy), data predictive control for flooding in coastal cities, and AI for building energy optimization.

He received his Ph.D. (2015) and M.S. (2012), in Electrical and Systems Engineering, both from the University of Pennsylvania. Dr. Behl is the winner of the American Control Conference (ACC 2017) Best Energy Systems Paper Award, TECHCON Best Paper Award (2015), and the best demo award at BuildSys, 2012. In 2016 he won the Department of Energy’s Allegheny Cleantech university prize. In 2011, he won the World Embedded Software Contest held in Seoul by the Korean Ministry of Knowledge Economy. He is also the recipient of the Richard K. Dentel Memorial Prize awarded by the University of Pennsylvania for research and excellence in urban transportation. He is the co-founder, organizer, and the race director of the F1/10 International Autonomous Racing Competitions. He is the recipient of the NSF CAREER award (2021).

He is an associate editor for the SAE Journal on Connected and Autonomous Vehcile. He also serves on the on the Academic Advisory Council of the Partners for Automated Vehicle Education (PAVE) campaign, to help promote public understanding about autonomous vehicles and their potential benefits.


  • Best Paper Award: Journal of Water - Open Access 2021
  • Best Paper Award: International Conference on Intelligent Robotics and Systems (IROS): Workshop on Perception, Learning, and Control for Autonomous Agile Vehicles. 2020
  • Best Systems Design Award - For ”Autonomous Electric Vehicle Charging System", Systems and Information Engineering Design Symposium (SIEDS). 2019
  • Best Research Poster Award - 5th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys) 2018
  • Best Energy Systems Paper Award - American Control Conference (ACC) 2017
  • Winner of the 2106 DoE EERE’s Allegheny Region Cleantech University Prize, Carnegie Mellon University, Pittsburgh, USA. 2016
  • Best Paper Award , for ”Sometimes, Money Does Grow on Trees: Data-Driven Demand Response with DR-Advisor, Internet of Things Session at the Semiconductor Research Corporation’s (SRC) TECHCON, Austin, USA. 2015
  • Best Demo Award at BuildSys, 4th ACM Workshop On Embedded Systems For Energy-Efficiency In Buildings, Toronto, Canada. 2012
  • Richard K. Dentel Memorial Prize in Urban Transportation, University of Pennsylvania, Philadelphia, USA. 2011
  • Winner of the World Embedded Software Contest, Korean Ministry of Knowledge Economy and Electronics and Telecommunications Research Institute (ETRI), Seoul, South Korea. 2010

Research Interests

  • Robotics
  • Artificial Intelligence
  • Cyber-Physical Systems
  • Autonomous Systems
  • Smart Buildings/Cities
  • Internet of Things

In the News

Selected Publications

  • Varundev SureshBabu and Madhur Behl, " - an Open-Source ROS Based F1/10 Autonomous Racing Simulator" 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)
  • Trent Weiss and Madhur Behl "DeepRacing: Parameterized Trajectories for Autonomous Racing" ABS 2020 arXiv preprint arXiv:2005.05178
  • Trent Weiss, Madhur Behl, "DeepRacing: a framework for autonomous racing" ABS 2020 IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE), Pages 1163-1168
  • Alexander B Chen, Madhur Behl, Jonathan L Goodall, "Trust me, my neighbors say it's raining outside: ensuring data trustworthiness for crowdsourced weather stations" ABS 2018 Proceedings of the 5th Conference on Systems for Built Environments, Pages 25-28 [Best Presentation Award]
  • Madhur Behl, Achin Jain, and Rahul Mangharam “Data-Driven Modeling, Control and Tools for Cyber- Physical Energy Systems” ABS ACM/IEEE Conference on Cyber-Physical Systems, April 2016

Courses Taught

  • F1/10 Autonomous Racing - Perception, Planning, and Control for Autonomous Vehicles Spring '18,'19,'20
  • Principles of Modeling for Cyber-Physical Systems Fall '17,'18,'19,'20

Featured Grants & Projects

  • NSF CAREER: Safe and Agile Autonomous Cyber-Physical Systems

    Autonomous Cyber-Physical Systems (CPS), such as self-driving cars, and drones, powered by deep learning and AI based perception, planning, and control algorithms, are forming the basis for significant pieces of our nation’s critical infrastructure, and present direct, and urgent safety-critical challenges. A major limitation with current approaches towards deploying autonomous CPS is in ensuring that the system operates safely, and reliably in situations that do not happen very often under normal operating conditions and are therefore difficult to gather data on. For instance, a self-driving car trained to follow the ‘rules of the road’ will perform well most of the time, but it is the unusual conditions, the edge cases, which pose the hardest safety challenges. This project brings forward an innovative idea – can increasing the agility of an autonomous vehicle improve its safety? This notion is somewhat controversial since agility (like that of race cars) is more frequently associated with decreased safety margins. Motivated by these challenges underlying real-world testing and safety for autonomous vehicles, the goal of this project is to develop the foundations for autonomous cyber-physical systems along two dimensions: agility, safety, and their interplay. The project is centered on (1) increasing agility for AVs by developing new methods for agile motion planning, so they can maneuver at the limits of their handling and control when it matters most to escape potentially unsafe conditions, (2) automated reasoning about uncertain dynamic situations that may occur during autonomous CPS operation, and (3) developing novel methods for automatically generating testing and edge-case scenarios at design time, to explore scenarios under which the autonomous CPS would fail. The proposed methods will be evaluated on scaled autonomous vehicles testbeds, on photorealistic and high-fidelity simulation platforms, and on full scale AV prototypes. The project will also consider not just safety of an unoccupied AV – but one in which passengers may be present. This CAREER project includes designing exciting new courses, and initiatives centered around autonomous racing to engage with research and mentoring for K-12, undergraduate, and graduate students. The project aims to ensure that students cultivate a holistic view of cyber-physical systems and autonomous systems by drawing stronger connections between theory, applications, and hands-on platform development. The project will help enhance the capabilities of autonomous cyber-physical systems and facilitate with their safe deployment.

    Read More
  • NSF CRISP Type 2: dMIST: Data-driven Management for Interdependent Stormwater and Transportation Systems

    The overarching objective of this Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) research project is to create a novel decision support system denoted dMIST (Data-driven Management for Interdependent Stormwater and Transportation Systems) to improve management of interdependent transportation and stormwater infrastructure systems. dMIST is designed specifically to address the critical problem of recurrent flooding caused by sea level rise and more frequent intense storms. The City of Norfolk, Virginia, a national leader in addressing the sea level rise challenge, will collaborate with the research team and serve as the project testbed. With sea level rise and more frequent intense storms, streets in many cities now flood multiple times per year. Flooding of roadways has cascading impacts to other infrastructure systems that depend on the road network including emergency services. Solving the problem of flooded roadways requires new tools capable of analyzing stormwater, transportation, and other infrastructure as interdependent systems. dMIST will be a recommendation system for assisting municipal decision makers and stakeholders in day-to-day operations to mitigate the short-term impacts of road flooding occurrences. It will also offer decision makers novel ways of testing "what if" scenarios that stretch across interdependent infrastructure systems in order to guide how large investments are used to adapt infrastructure systems to a more resilient future

    Read More
  • Jefferson Trust: Cavalier Autonomous Racing

    Awarded Amount: $50,000

    The Cavalier Autonomous Racing Club, under the supervision of UVA faculty, will build, develop, program and race an autonomous electric go-kart. Club activity will culminate in a demonstration at the Indianapolis Motor Speedway, home of the Indy 500 race.

    Read More