Epidemics on Networks: Modeling, Estimation and Control

Our focus is on rigorously understanding the dynamics of spreading processes on networks. We borrow tools from network science, dynamical systems, optimization, game theory, and signal processing.
Key Questions
Representative Publications
Learning to mitigate epidemic risks: A dynamic population game approach, Dynamic Games and Applications, 2023. (with U. Maitra, E. Elokda, S. Bolognani) [Link]
Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical Networks, SIAM J. Control Optim., 2022. (with T. Sneh, K. Gupta) [Link]
A Closed-Loop Framework for Inference, Prediction and Control of SIR Epidemics on Networks, IEEE Trans. Network Sci. Eng., 2021. (with P. Paré, J. Godbole) [Link]
Game-Theoretic Vaccination Against Networked SIS Epidemics, IEEE Trans. Control Netw. Syst., 2019. (with S. Sundaram) [Link]
SIS/SIR ModelsPopulation GamesBayesian PersuasionNetwork Estimation
Collaborators: Philip Paré & Shreyas Sundaram (Purdue), Saverio Bolognani (ETH Zurich), Vaibhav Srivastava (MSU). Funding: Joint DST-NSF Indo-US Research Grant.

Stochastic (Data-Driven) Optimization and Control

We develop techniques that do not impose assumptions on the probability distribution of uncertainty, but rely directly on available data to compute optimal solutions with provable guarantees.
Representative Publications
Data-Driven Risk-sensitive MPC for Safe Navigation in Multi-Robot Systems, IEEE ICRA, 2023. (with A. Navsalkar) [Link]
Wasserstein Distributionally Robust Look-Ahead Economic Dispatch, IEEE Trans. Power Syst., 2021. (with B. K. Poolla, S. Bolognani, D. Callaway, A. Cherukuri) [Link]
Consistency of Distributionally Robust Optimization under Wasserstein Ambiguity Sets, IEEE Control Syst. Lett., 2021. (with A. Cherukuri) [Link]
Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets, ACC, 2019. (with A. Cherukuri, J. Lygeros) [Preprint]
Distributionally Robust Opt.Wasserstein AmbiguityMPCRisk MeasuresPower Systems
Collaborators: Ashish Cherukuri (Groningen), B. K. Poolla (NREL), S. Bolognani & J. Lygeros (ETH Zurich). Funding: ISIRD Grant, IIT Kharagpur.

Game Theory for Security of Network Systems

We study how decentralized decision-making, behavioral biases, and network structure interact to shape the security and resilience of large-scale infrastructure.
Key Questions
Representative Publications
Equilibrium Strategies for Multiple Interdictors on a Common Network, Eur. J. Oper. Res., 2021. (with H. Sreekumaran et al.) [Link]
Behavioral and Game-Theoretic Security Investments in Interdependent Systems, IEEE Trans. Control Netw. Syst., 2020. (with M. Abdallah et al.) [Link]
Interdependent Security Games under Behavioral Probability Weighting, IEEE Trans. Control Netw. Syst., 2018. (with S. Sundaram) [Link]
Nash EquilibriumAttack GraphsProspect TheoryNetwork Resilience

Equilibrium and Incentives under Behavioral Biases in Multi-Agent Systems

We investigate how psychological biases and prospect-theoretic preferences shape human behavior in shared infrastructure, and design dynamic incentives to align individual and societal objectives.
Key Questions
Representative Publications
Controlling Human Utilization of Failure-Prone Shared Resources via Taxes, IEEE Trans. Autom. Control, 2021. (with S. Sundaram) [Link]
Fragility of the Commons under Prospect-Theoretic Risk Attitudes, Games and Economic Behavior, 2016. (with S. Garg, S. Sundaram) [Link]
Dynamic Mechanism Design for Human-in-the-Loop Control of Building Energy, ACC, 2019. (with M. Schuette, A. Eichler, J. Lygeros) [Link]
Mechanism DesignProspect TheoryHuman-in-the-LoopSmart Grid