Andreas A. Malikopoulos

Andreas A. Malikopoulos
Born
Athens, Greece
CitizenshipUnited States
Alma materEllinogermaniki Agogi (Elementary and Middle School) and 2nd Lyceum Ymittou (High School)
National Technical University of Athens (MEng)
University of Michigan, Ann Arbor (M.S., Ph.D)
Known forControl theory, team theory with applications to emerging mobility systems
Scientific career
FieldsMechanical Engineering; Systems Engineering; Robotics
Thesis Real-time, self-learning identification and stochastic optimal control of advanced powertrain systems
Dennis N. Assanis; Panos Papalambros
Websitehttp://www.engineering.cornell.edu/people/andreas-malikopoulos/

Andreas A. Malikopoulos (Greek: Ανδρέας Μαλικόπουλος) is a Greek-American control theorist and engineer. He is a Professor at Cornell University, with affiliations in Applied Mathematics, Systems Engineering, Mechanical Engineering, Electrical and Computer Engineering, and Civil and Environmental Engineering. He is also the Director of the Information and Decision Science (IDS) Laboratory.[1]

Malikopoulos' research bridges control theory and learning to enable systems such as vehicles, robots, and large-scale infrastructure to operate autonomously while achieving near-optimal performance and safely adapting to dynamic environments.[2][3][4][5]

Early life and education

Malikopoulos was born in Athens, Greece, where he developed an early interest in mathematics and engineering.[6] He earned a Diploma in Mechanical Engineering from the National Technical University of Athens in 2000.[6]

He later pursued graduate studies at the University of Michigan, earning a Master of Science degree in 2004 and a Ph.D. in Mechanical Engineering in 2008.

At Michigan, he studied under Dennis N. Assanis and Panos Y. Papalambros. His doctoral research focused on real-time, self-learning stochastic optimal control of advanced powertrain systems.[7]

His dissertation introduced a learning-based control framework that transforms internal combustion engines into adaptive intelligent systems capable of learning driver behavior. These systems can optimize performance metrics such as fuel efficiency and emissions while identifying and adapting to individual driving styles.

This work resulted in a U.S. patent and was made available for licensing through the University of Michigan’s Technology Transfer Office.[8][9]

Career

Before joining Cornell, Malikopoulos held academic and research positions at the University of Delaware, Oak Ridge National Laboratory, and General Motors Research & Development.[10]

During this period, he contributed to the development and commercialization of advanced control technologies for energy-efficient and connected vehicle systems.[11]

Research

Malikopoulos' research lies at the intersection of control theory, learning, and decision-making, with applications in autonomous systems, robotics, and intelligent infrastructure. He has advanced the theoretical foundations of decentralized stochastic control and team decision theory, developing methods for coordination among agents operating under asymmetric or incomplete information.[12][6]

He has resolved long-standing open problems in team theory[12] and introduced mathematical frameworks that integrate reinforcement learning with decision-theoretic principles and multi-agent coordination.[13] He has also developed control strategies for connected and automated vehicles, enabling fuel-efficient and collision-free coordination in complex traffic scenarios such as intersections and merging zones.[14] These technologies have been experimentally validated at MCity under the ARPA-E NEXTCAR program, demonstrating improvements in energy efficiency of at least 25% in vehicles such as the Audi A3 e-tron. Some of these technologies have been commercialized by Robert Bosch GmbH.[15][16][17]

References

  1. ↑ "Andreas A. Malikopoulos". Cornell University. Retrieved 2025-12-29.
  2. ↑ "NEXTCAR Program". ARPA-E. United States: U.S. Department of Energy. Retrieved 2025-12-29.
  3. ↑ "Andreas Malikopoulos Inventions, Patents and Patent Applications". Justia Patents. United States: Justia. Retrieved 2025-12-29.
  4. ↑ "Andreas A. Malikopoulos – Google Scholar profile". Google Scholar.
  5. ↑ "Mini Smart-CIT drives design of safer automated transportation". Cornell Chronicle. Ithaca, New York, United States: Cornell University. Retrieved 2025-12-29.
  6. 1 2 3 Malikopoulos, Andreas A. (2024). "Andreas A. Malikopoulos [People in Control]". IEEE Control Systems Magazine. 44 (1): 15–19. doi:10.1109/MCS.2023.3329865.
  7. ↑ Malikopoulos, Andreas A. (2010). "Online Identification and Stochastic Control for Autonomous Internal Combustion Engines". Journal of Dynamic Systems, Measurement, and Control. 132 (2): 024504. doi:10.1115/1.4000819.
  8. ↑ "Method, control apparatus and powertrain system controller for real-time, self-learning control based on individual operating style". Justia Patents. United States: Justia. Retrieved 2025-12-29.
  9. ↑ "ORNL features profile of ME alumnus Andreas Malikopoulos". University of Michigan College of Engineering. Ann Arbor, Michigan, United States: University of Michigan. Retrieved 2025-12-29.
  10. ↑ "Method for real-time, self-learning identification of fuel injectors during engine operation". Justia Patents. United States: Justia. Retrieved 2025-12-29.
  11. ↑ "Innovation Partnerships". University of Michigan Innovation Partnerships. Ann Arbor, Michigan, United States: University of Michigan. Retrieved 2025-12-29.
  12. 1 2 Malikopoulos, Andreas A. (2023). "On Team Decision Problems With Nonclassical Information Structures". IEEE Transactions on Automatic Control. 68 (7): 3915–3930. arXiv:2101.10992. doi:10.1109/TAC.2022.3195126.
  13. ↑ Malikopoulos, Andreas A. (2023). "Separation of learning and control for cyber–physical systems". Automatica. 151 110912. doi:10.1016/j.automatica.2023.110912.
  14. ↑ Chremos, Ioannis V.; Malikopoulos, Andreas A. (2024). "Mechanism Design Theory in Control Engineering: A Tutorial and Overview of Applications in Communication, Power Grid, Transportation, and Security Systems". IEEE Control Systems Magazine. 44 (1): 20–45. doi:10.1109/MCS.2023.3329919.
  15. ↑ "The future of testing self-driving cars". UDaily. Newark, Delaware, United States: University of Delaware. Retrieved 2025-12-29.
  16. ↑ Malikopoulos, Andreas A.; Cassandras, Christos G.; Zhang, Yue J. (2018). "A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections". Automatica. 93: 244–256. arXiv:1602.03786. doi:10.1016/j.automatica.2018.03.056.
  17. ↑ Malikopoulos, Andreas A.; Beaver, Logan; Chremos, Ioannis V. (2021). "Optimal time trajectory and coordination for connected and automated vehicles". Automatica. 125 109469. arXiv:2003.12183. doi:10.1016/j.automatica.2020.109469. ISSN 0005-1098. Retrieved 2025-12-20.
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