Ceyhun Eksin

About

Ceyhun Eksin is an Associate Professor in the Department of Industrial & Systems Engineering at Texas A&M University, where he also holds the Corrie and Jim Furber '64 Faculty Fellowship. His educational background includes a Ph.D. in Electrical Systems & Engineering from the University of Pennsylvania, obtained in 2015. His research focuses on the analysis and design of networked multi-agent systems, with particular interest in distributed optimization, game theory, evolutionary game theory, networks, autonomous systems, energy systems, and epidemics. Eksin's work involves understanding complex interactions within these systems to improve their efficiency, robustness, and behavior, especially in contexts such as energy markets and disease dynamics.

Research topics

• Computer Science
• Physics
• Medicine
• Economics
• Psychology
• Econometrics
• Biology

Selected publications

• Awareness-driven behavior changes can shift the shape of epidemics away from peaks and toward plateaus, shoulders, and oscillations

  Proceedings of the National Academy of Sciences · 2020 · 208 citations

  The COVID-19 pandemic has caused more than 1,000,000 reported deaths globally, of which more than 200,000 have been reported in the United States as of October 1, 2020. Public health interventions have had significant impacts in reducing transmission and in averting even more deaths. Nonetheless, in many jurisdictions, the decline of cases and fatalities after apparent epidemic peaks has not been rapid. Instead, the asymmetric decline in cases appears, in most cases, to be consistent with platea…

  DOI

• Disease spread coupled with evolutionary social distancing dynamics can lead to growing oscillations

  2021 60th IEEE Conference on Decision and Control (CDC) · 2021 · 22 citations

  Senior authorCorresponding

  If the public’s adherence to social distance measures remained steady during an outbreak, the number of cases would have a single peak followed by a sharp decline according to standard epidemiological models. Nonetheless, during COVID-19 the initial rise and fall in the number of cases followed new waves of cases in many localities. In this paper, we explore a standard susceptible-exposed-infected-recovered (SEIR) epidemiological model coupled with an individual be-havior response model that mod…

  DOI

• SIS epidemics coupled with evolutionary social distancing dynamics

  2023-05-31 · 8 citations

  articleSenior author

  A major factor contributing to the difficulties in epidemic forecasting is the unpredictable nature of the population behavior that can either mitigate or exacerbate the spread of a disease. In this paper, we consider a game-theoretic framework for modeling the disease prevalence dependent response of the population behavior in a susceptible-infected-susceptible (SIS) epidemiological model. Our behavioral response model is based on replicator dynamics, where the individuals’ underlying payoffs d…

  PublisherDOI

• Approximate Submodularity of Maximizing Anticoordination in Network Games

  2022 IEEE 61st Conference on Decision and Control (CDC) · 2022-12-06 · 5 citations

  articleSenior author

  We consider decentralized learning dynamics for agents in an anti-coordination network game. In the anti-coordination network game, there is a preferred action in the absence of neighbors’ actions, and the utility an agent receives from the preferred action decreases as more of its neighbors select the preferred action, potentially causing the agent to select a less desirable action. The decentralized dynamics that is based on the iterated elimination of dominated strategies converge for the con…

  PublisherDOI

• Robust Social Welfare Maximization via Information Design in Linear-Quadratic-Gaussian Games

  IEEE Control Systems Letters · 2023-01-01 · 4 citations

  articleSenior author

  Information design involves a designer with the goal of influencing players’ actions in an incomplete information game through signals generated from a designed probability distribution so that its objective function is optimized. We consider a setting in which the designer has partial knowledge on players’ payoffs, and wants to maximize social welfare. We address the uncertainty about players’ preferences by formulating a robust information design problem against the worst-case payoffs. When th…

  PublisherDOI

Recent grants

• CAREER: Evolutionary Games in Dynamic and Networked Environments for Modeling and Controlling Large-Scale Multi-agent Systems

  NSF · $503k · 2023–2028

• CIF: Small: Communication-Aware Decentralized Game-Theoretic Learning Algorithms for Networked Systems with Uncertainty

  NSF · $361k · 2020–2024

• Modeling and Control of Ceovolutionary Network Formation with Applications to Finishing Processes for 3D Printed Components

  NSF · $430k · 2020–2024

Frequent coauthors

• Alejandro Ribeiro

  California University of Pennsylvania

  34 shared

• Joshua S. Weitz

  University of Maryland, College Park

  23 shared

• Keith Paarporn

  21 shared

• Armita Nourmohammad

  19 shared

• Sarper Aydın

  17 shared

• Ali Jadbabaie

  15 shared

• Pooya Molavi

  Northwestern University

  14 shared

• Furkan Sezer

  12 shared

Similar researchers at Texas A&M University

• Satish Bukkapatnamh-40
• Alaa Elwanyh-37
• Sergiy Butenkoh-35