Intelligent Transportation Systems Society gives engineering professor Young Researcher Award
Andreas Malikopoulos, the Terri Connor Kelly and John Kelly Career Development Associate Professor in the University of Delaware’s Department of Mechanical Engineering, has received the 2019 IEEE Intelligent Transportation Systems Young Researcher Award.
IEEE stands for the Institute of Electrical and Electronics Engineers, but the group goes by the initials. The award recognizes early career contributions and leadership in research and/or application in intelligent transportation systems and related fields. Click here to view a video about Malikopoulos’ accomplishments.
Malikopoulos received a diploma from the National Technical University of Athens, Greece, and his master’s and doctoral degrees from the University of Michigan, Ann Arbor, all in mechanical engineering. His research interests span several fields, including analysis, optimization, and control of cyber-physical systems; decentralized stochastic systems, stochastic scheduling, and resource allocation. The emphasis is on applications related to connected and automated vehicles (CAVs), smart cities, and sociotechnical systems.
His contributions in this area started early on, while he was still at graduate school, when he addressed the problem related to the discrepancy between true fuel economy of a vehicle and the one posted on the window sticker. In his dissertation, Malikopoulos developed the theoretical framework and control algorithms that could transform the vehicle’s engine into an autonomous intelligent system capable of learning its optimal operation in real time while the driver is driving the vehicle. Through this approach, the engine progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specific performance criteria, such as fuel economy and emissions. Malikopoulos’ dissertation research was a paradigm shift and changed the perception of how internal combustion engines are optimized today.
Moving to General Motors Research and Development, as a senior researcher, and then later to Oak Ridge National Laboratory (ORNL) as an Alvin M. Weinberg Fellow, he continued making seminal contributions in the area of self-learning powertrain control. His work on driver feedback systems eventually led to a technology that was licensed in industry.
As the deputy director of the Urban Dynamics Institute at Oak Ridge, Malikopoulos developed several initiatives with the goal to investigate how scalable data and informatics could be used to enhance understanding of the environmental implications of connected and automated vehicles and improve transportation sustainability and accessibility.
Upon joining the Department of Mechanical Engineering at UD, Malikopoulos established the Information and Decision Science Lab with the vision to advance the analysis, optimization, and control of state-of-the-art cyber-physical networks. His research at UD focuses on applications related to emerging mobility systems and sociotechnical systems. He has made seminal contributions on the technological dimension of mobility systems by developing control algorithms for optimal coordination of CAVs and identifying potential research paths with connected autonomous systems. To help the research community visualize the implications of energy-efficient mobility systems, he created a unique testbed, UD’s Scaled Smart City, which includes 35 robotic cars and 10 drones that can replicate real-world traffic scenarios in a small and controlled environment. This testbed can help prove concepts beyond the simulation level and understand the implications of errors/delays in V2V and V2I communication as well as their impact on energy usage.
Malikopoulos has demonstrated leadership toward enhancing our understanding of the potential benefits of emerging mobility systems. He has organized several international workshops in which he brought together leaders from government, industry, and academia to discuss what innovations are needed and identify the road for energy-efficient mobility.
