About

Andreas Malikopoulos is a professor in the School of Civil & Environmental Engineering at Cornell University and the Director of the Information and Decision Science Lab. His research interests are grounded at the intersection of learning and control to enable systems—such as vehicles, robots, or large-scale infrastructures—to operate autonomously and achieve near-optimal performance while safely adapting to and interacting with dynamic environments. His work integrates decision-theoretic foundations with learning-based methods to endow engineered systems with the capability to reason, learn, and act in real time. His principal domain of application has been emerging mobility systems, including connected and automated vehicles, shared autonomous mobility, and smart-city infrastructures. Prior to his current appointments, he served as the Terri Connor Kelly and John Kelly Career Development Professor in the Department of Mechanical Engineering at the University of Delaware and was the founding director of the Sociotechnical Systems Center. His extensive background includes fellowships at Oak Ridge National Laboratory and research roles at General Motors. Dr. Malikopoulos holds a Diploma from the National Technical University of Athens and M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor. He has received numerous awards, including the IEEE Intelligent Transportation Systems Young Researcher Award and the University of Delaware’s College of Engineering Outstanding Junior Faculty Award. He is an Associate Editor of Automatica and IEEE Transactions on Automatic Control, a Senior Editor of IEEE Transactions on Intelligent Transportation Systems, a Senior Member of IEEE, a Fellow of ASME, and a member of the Board of Governors and Distinguished Lecturer of the IEEE Intelligent Transportation Systems Society.

Research topics

• Computer science
• Mathematical optimization
• Transport engineering
• Automotive engineering
• Engineering

Selected publications

• Teaching Cars to Drive: Spotlight on Connected and Automated Vehicles

  ArXiv.org · 2025-07-01

  preprintOpen accessSenior author

  In recent decades, society has witnessed significant advancements in emerging mobility systems. These systems refer to transportation solutions that incorporate digital technologies, automation, connectivity, and sustainability to create safer, more efficient, and user-centered mobility. Examples include connected and automated vehicles (CAVs), shared mobility services (car-pooling), electric vehicles, and mobility-as-a-service platforms. These innovations have the potential to greatly impact areas such as safety, pollution, comfort, travel time, and fairness. In this article, we explore the current landscape of CAVs. We discuss their role in daily life and their future potential, while also addressing the challenges they may introduce. Following, we also examine the practical difficulties in research associated with CAVs especially simulating and testing CAV-related algorithms in real-world settings. We present existing solutions that aim to overcome these limitations. Finally, we provide an accessible introduction to modeling CAVs using basic kinematic principles and offer an open-source tutorial to help interested students begin exploring the field.

  PublisherOA PDFDOI

• On the Robustness of Derivative-free Methods for Linear Quadratic Regulator

  ArXiv.org · 2025-06-14

  preprintOpen accessSenior author

  Policy optimization has drawn increasing attention in reinforcement learning, particularly in the context of derivative-free methods for linear quadratic regulator (LQR) problems with unknown dynamics. This paper focuses on characterizing the robustness of derivative-free methods for solving an infinite-horizon LQR problem. To be specific, we estimate policy gradients by cost values, and study the effect of perturbations on the estimations, where the perturbations may arise from function approximations, measurement noises, etc. We show that under sufficiently small perturbations, the derivative-free methods converge to any pre-specified neighborhood of the optimal policy. Furthermore, we establish explicit bounds on the perturbations, and provide the sample complexity for the perturbed derivative-free methods.

  PublisherOA PDFDOI

• Learning-Based Robust Bayesian Persuasion with Conformal Prediction Guarantees

  ArXiv.org · 2025-11-09

  preprintOpen accessSenior author

  Classical Bayesian persuasion assumes that senders fully understand how receivers form beliefs and make decisions--an assumption that rarely holds when receivers possess private information or exhibit non-Bayesian behavior. In this paper, we develop a learning-based framework that integrates neural networks with conformal prediction to achieve robust persuasion under uncertainty about receiver belief formation. The proposed neural architecture learns end-to-end mappings from receiver observations and sender signals to action predictions, eliminating the need to identify belief mechanisms explicitly. Conformal prediction constructs finite-sample valid prediction sets with provable marginal coverage, enabling principled, distribution-free robust optimization. We establish exact coverage guarantees for the data-generating policy and derive bounds on coverage degradation under policy shifts. Furthermore, we provide neural network approximation and estimation error bounds, with sample complexity $O(d \log(|\mathcal{U}||\mathcal{Y}||\mathcal{S}|)/\varepsilon^2)$, where $d$ denotes the effective network dimension, and finite-sample lower bounds on the sender's expected utility. Numerical experiments on smart-grid energy management illustrate the framework's robustness.

  PublisherOA PDFDOI

• Route Recommendations for Traffic Management Under Learned Partial Driver Compliance

  ArXiv.org · 2025-04-03

  preprintOpen accessSenior author

  In this paper, we aim to mitigate congestion in traffic management systems by guiding travelers along system-optimal (SO) routes. However, we recognize that most theoretical approaches assume perfect driver compliance, which often does not reflect reality, as drivers tend to deviate from recommendations to fulfill their personal objectives. Therefore, we propose a route recommendation framework that explicitly learns partial driver compliance and optimizes traffic flow under realistic adherence. We first compute an SO edge flow through flow optimization techniques. Next, we train a compliance model based on historical driver decisions to capture individual responses to our recommendations. Finally, we formulate a stochastic optimization problem that minimizes the gap between the target SO flow and the realized flow under conditions of imperfect adherence. Our simulations conducted on a grid network reveal that our approach significantly reduces travel time compared to baseline strategies, demonstrating the practical advantage of incorporating learned compliance into traffic management.

  PublisherOA PDFDOI

• A Communication-Efficient Decentralized Actor-Critic Algorithm

  ArXiv.org · 2025-10-22

  preprintOpen accessSenior author

  In this paper, we study the problem of reinforcement learning in multi-agent systems where communication among agents is limited. We develop a decentralized actor-critic learning framework in which each agent performs several local updates of its policy and value function, where the latter is approximated by a multi-layer neural network, before exchanging information with its neighbors. This local training strategy substantially reduces the communication burden while maintaining coordination across the network. We establish finite-time convergence analysis for the algorithm under Markov-sampling. Specifically, to attain the $\varepsilon$-accurate stationary point, the sample complexity is of order $\mathcal{O}(\varepsilon^{-3})$ and the communication complexity is of order $\mathcal{O}(\varepsilon^{-1}τ^{-1})$, where tau denotes the number of local training steps. We also show how the final error bound depends on the neural network's approximation quality. Numerical experiments in a cooperative control setting illustrate and validate the theoretical findings.

  PublisherOA PDFDOI

• Handling Pedestrian Uncertainty in Coordinating Autonomous Vehicles at Signal-Free Intersections

  ArXiv.org · 2025-05-09

  preprintOpen accessSenior author

  In this paper, we provide a theoretical framework for the coordination of connected and automated vehicles (CAVs) at signal-free intersections, accounting for the unexpected presence of pedestrians. First, we introduce a general vehicle-to-infrastructure communication protocol and a low-level controller that determines the optimal unconstrained trajectories for CAVs, in terms of fuel efficiency and travel time, to cross the intersection without considering pedestrians. If such an unconstrained trajectory is unattainable, we introduce sufficient certificates for each CAV to cross the intersection while respecting the associated constraints. Next, we consider the case where an unexpected pedestrian enters the road. When the CAV's sensors detect a pedestrian, an emergency mode is activated, which imposes certificates related to an unsafe set in the pedestrian's proximity area. Simultaneously, a re-planning mechanism is implemented for all CAVs to accommodate the trajectories of vehicles operating in emergency mode. Finally, we validate the efficacy of our approach through simulations conducted in MATLAB and RoadRunner softwares, which facilitate the integration of sensor tools and the realization of real-time implementation.

  PublisherOA PDFDOI

• VisioPath: Vision-Language Enhanced Model Predictive Control for Safe Autonomous Navigation in Mixed Traffic

  ArXiv.org · 2025-07-08

  preprintOpen accessSenior author

  In this paper, we introduce VisioPath, a novel framework combining vision-language models (VLMs) with model predictive control (MPC) to enable safe autonomous driving in dynamic traffic environments. The proposed approach leverages a bird's-eye view video processing pipeline and zero-shot VLM capabilities to obtain structured information about surrounding vehicles, including their positions, dimensions, and velocities. Using this rich perception output, we construct elliptical collision-avoidance potential fields around other traffic participants, which are seamlessly integrated into a finite-horizon optimal control problem for trajectory planning. The resulting trajectory optimization is solved via differential dynamic programming with an adaptive regularization scheme and is embedded in an event-triggered MPC loop. To ensure collision-free motion, a safety verification layer is incorporated in the framework that provides an assessment of potential unsafe trajectories. Extensive simulations in Simulation of Urban Mobility (SUMO) demonstrate that VisioPath outperforms conventional MPC baselines across multiple metrics. By combining modern AI-driven perception with the rigorous foundation of optimal control, VisioPath represents a significant step forward in safe trajectory planning for complex traffic systems.

  PublisherOA PDFDOI

• Stable and Fair Benefit Allocation in Mixed-Energy Truck Platooning: A Coalitional Game Approach

  ArXiv.org · 2025-07-22

  preprintOpen accessSenior author

  This paper addresses the benefit allocation in a mixed-energy truck platoon composed of fuel-powered and electric trucks. The interactions among trucks during platoon formation are modeled as a coalitional game with transferable utility. We first design a stable payoff allocation scheme that accounts for truck heterogeneity in energy savings and platoon roles (leader or follower), establishing core-stability conditions to ensure that no subset of trucks has an incentive to deviate for greater benefit. To enhance payoff fairness, we then propose a closed-form, Shapley value-based allocation approach that is computationally efficient and independent of the platoon size. Sufficient conditions under which the allocation is both fair and core-stable are provided. In scenarios where the Shapley value falls outside the core, we develop an alternative allocation based on the stable payoff that minimizes the mean relative deviation from the Shapley value while preserving core stability. This deviation is further proved to be upper-bounded by $1$, showing a favorable trade-off between stability and fairness. Finally, extensive numerical studies validate the theoretical results and demonstrate the effectiveness of the proposed framework in facilitating stable, equitable, and sustainable cooperation in mixed-energy truck platooning.

  PublisherOA PDFDOI

• Distributed Mixed-Integer Quadratic Programming for Mixed-Traffic Intersection Control

  ArXiv.org · 2025-04-06

  preprintOpen accessSenior author

  In this paper, we present a distributed algorithm utilizing the proximal alternating direction method of multipliers (ADMM) in conjunction with sequential constraint tightening to address mixed-integer quadratic programming (MIQP) problems associated with traffic light systems and connected automated vehicles (CAVs) in mixed-traffic intersections. We formulate a comprehensive MIQP model aimed at optimizing the coordination of traffic light systems and CAVs, thereby fully capitalizing on the advantages of CAV integration under conditions of high penetration rates. To effectively approximate the intricate multi-agent MIQP challenges, we develop a distributed algorithm that employs proximal ADMM for solving the convex relaxation of the MIQP while systematically tightening the constraint coefficients to uphold integrality requirements. The performance of our control framework and the efficacy of the distributed algorithm are rigorously validated through a series of simulations conducted across varying penetration rates and traffic volumes.

  PublisherOA PDFDOI

• Robust Accelerated Dynamics for Subnetwork Bilinear Zero-Sum Games with Distributed Restarting

  ArXiv.org · 2025-04-23

  preprintOpen accessSenior author

  In this paper, we investigate distributed Nash equilibrium seeking for a class of two-subnetwork zero-sum games characterized by bilinear coupling. We present a distributed primal-dual accelerated mirror-descent algorithm with convergence guarantees. However, we demonstrate that this time-varying algorithm is not robust, as it fails to converge under arbitrarily small disturbances. To address this limitation, we introduce a distributed accelerated algorithm that incorporates a coordinated restarting mechanism. We model this new algorithm as a hybrid dynamical system and establish its structural robustness.

  PublisherOA PDFDOI

Recent grants

• NRI: Addressing Safe Interaction Between Autonomous and Human-Driven Vehicles

  NSF · $476k · 2022–2023

• Collaborative Research: CPS: Medium: An Online Learning Framework for Socially Emerging Mixed Mobility

  NSF · $380k · 2022–2023

Frequent coauthors

• Thomas Siegert

  University of West Florida

  125 shared

• Kevin Lisankie

  Institute of Electrical and Electronics Engineers

  125 shared

• Peter Tuohy

  University of Memphis

  121 shared

• Neelam Khinvasara

  Institute of Electrical and Electronics Engineers

  121 shared

• Karen Hawkins

  Xi'an Jiaotong University

  109 shared

• Jeffrey Cichocki

  Institute of Electrical and Electronics Engineers

  109 shared

• Dawn Melley

  109 shared

• Stephen Welby

  Institute of Electrical and Electronics Engineers

  97 shared

Education

• PhD, Mechanical Engineering

  University of Michigan

  2008

• MS, Mechanical Engineering

  University of Michigan

  2004

• Diploma, Mechanical Engineering

  National Technical University of Athens

  2000

Awards & honors

• 2010 Alvin M. Weinberg Fellowship
• 2019 IEEE Intelligent Transportation Systems Young Researche…
• 2020 University of Delaware’s College of Engineering Outstan…
• Selected by the National Academy of Engineering to participa…
• Organized a session on transportation at the 2016 European-A…