Biography:Andreas A. Malikopoulos

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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
ThesisReal-time, self-learning identification and stochastic optimal control of advanced powertrain systems
Doctoral advisorDennis 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 in the School of Civil and Environmental Engineering at Cornell University and the director of the Information and Decision Science Laboratory.[1]

Malikopoulos' research[2] bridges control theory and learning to enable systems—whether vehicles,[3] robots,[4] or large-scale infrastructures—to operate autonomously and achieve near-optimal performance while safely adapting to and interacting with dynamic environments.[5]

Early life and education

Andreas A. 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. He later pursued graduate studies at the University of Michigan, Ann Arbor, earning an M.S. in 2004 and a Ph.D. in 2008, both in Mechanical Engineering.[6]

At the University of Michigan, Malikopoulos studied under Dennis N. Assanis and Panos Papalambros, focusing his doctoral research on real-time, self-learning stochastic optimal control of advanced powertrain systems.[7] His dissertation introduced a learning-based control framework that turns internal combustion engines into autonomous intelligent systems[8] that can progressively perceive the driver's driving style and eventually learn to operate in a manner that optimizes specified performance criteria, e.g., fuel economy, emissions, with respect to the driver's driving style. This research ultimately led to a U.S. patent,[9] and was later made available for licensing through the University of Michigan's Technology Transfer Office.[10]

Before joining Cornell, he held academic and research positions at the University of Delaware, Oak Ridge National Laboratory,[11] and General Motors Global Research & Development,[12] contributing to advances in energy-efficient and connected-vehicle technologies.[10]

Research

Malikopoulos works at the intersection of control theory, learning, and decision making, with applications to autonomous systems and robotics, and intelligent infrastructure.[6] He worked on open problems in team theory,[13] and mathematical frameworks integrating reinforcement learning with decision-theoretic principles[14][15] and multi-agent coordination to design[16] autonomous systems that can reason, learn, and act in real time. His work introduced decentralized optimal control algorithms for connected and automated vehicles,[17][18] enabling fuel-efficient and collision-free coordination at intersections, merging zones, and other complex traffic environments.[19]

References

  1. ↑ "Andreas A. Malikopoulos". Cornell University. https://www.engineering.cornell.edu/people/andreas-malikopoulos/. 
  2. ↑ "Andreas A. Malikopoulos – Google Scholar profile". https://scholar.google.com/citations?user=ScKI3psAAAAJ&hl. 
  3. ↑ "NEXTCAR Program". United States: U.S. Department of Energy. https://arpa-e.energy.gov/programs-and-initiatives/view-all-programs/nextcar. 
  4. ↑ "Mini Smart-CIT drives design of safer automated transportation". Ithaca, New York, United States: Cornell University. https://news.cornell.edu/stories/2024/11/mini-smart-cit-drives-design-safer-automated-transportation. 
  5. ↑ "Andreas Malikopoulos Inventions, Patents and Patent Applications". United States: Justia. https://patents.justia.com/inventor/andreas-malikopoulos. 
  6. ↑ 6.0 6.1 6.2 Malikopoulos, Andreas A. (2024). "Andreas A. Malikopoulos [People in Control"]. IEEE Control Systems Magazine 44 (1): 15–19. doi:10.1109/MCS.2023.3329865. https://ieeexplore.ieee.org/abstract/document/10384580. 
  7. ↑ Malikopoulos, Andreas A. Real-time, self-learning identification and stochastic optimal control of advanced powertrain systems (Ph.D. thesis). University of Michigan.
  8. ↑ 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. https://asmedigitalcollection.asme.org/dynamicsystems/article/132/2/024504/470145/Online-Identification-and-Stochastic-Control-for. 
  9. ↑ "Method, control apparatus and powertrain system controller for real-time, self-learning control based on individual operating style". United States: Justia. https://patents.justia.com/patent/20090306866. 
  10. ↑ 10.0 10.1 "Innovation Partnerships". Ann Arbor, Michigan, United States: University of Michigan. https://me.engin.umich.edu/news-events/news/ornl-features-profile-me-alumnus-andreas-malikopoulos/. 
  11. ↑ "ORNL features profile of ME alumnus Andreas Malikopoulos". Ann Arbor, Michigan, United States: University of Michigan. https://me.engin.umich.edu/news-events/news/ornl-features-profile-me-alumnus-andreas-malikopoulos/. 
  12. ↑ "Method for real-time, self-learning identification of fuel injectors during engine operation". United States: Justia. https://patents.justia.com/patent/8676476. 
  13. ↑ Malikopoulos, Andreas A. (2023). "On Team Decision Problems With Nonclassical Information Structures". IEEE Transactions on Automatic Control 68 (7): 3915–3930. doi:10.1109/TAC.2022.3195126. https://ieeexplore.ieee.org/abstract/document/9844798. 
  14. ↑ Malikopoulos, Andreas A. (2023). "Separation of learning and control for cyber–physical systems". Automatica 151: 110912. doi:10.1016/j.automatica.2023.110912. https://www.sciencedirect.com/science/article/pii/S0005109823000626. 
  15. ↑ Malikopoulos, Andreas A. (2024). "Combining learning and control in linear systems". European Journal of Control 80: 101043. doi:10.1016/j.ejcon.2024.101043. https://www.sciencedirect.com/science/article/pii/S0947358024001031. 
  16. ↑ 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. https://doi.org/10.1109/MCS.2023.3329919. 
  17. ↑ 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. doi:10.1016/j.automatica.2018.03.056. https://www.sciencedirect.com/science/article/pii/S0005109818301511. 
  18. ↑ Malikopoulos, Andreas A.; Beaver, Logan; Chremos, Ioannis V. (2021). "Optimal time trajectory and coordination for connected and automated vehicles". Automatica 125: 109469. doi:10.1016/j.automatica.2020.109469. ISSN 0005-1098. https://www.sciencedirect.com/science/article/pii/S0005109820306671. Retrieved 2025-12-20. 
  19. ↑ "The future of testing self-driving cars". Newark, Delaware, United States: University of Delaware. https://www.udel.edu/udaily/2023/january/self-driving-cars-automated-vehicles-andreas-malikopoulos-algorithms-transportation-equity/. 



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