We develop rigorous mathematical foundations and scalable algorithms at the intersection of optimization, stochastic systems, game theory, and data-driven control — with applications to networked infrastructure, epidemic dynamics, and multi-agent systems.
Our goal is to understand and shape decision-making in complex, uncertain, and interconnected systems — bridging the gap between mathematical theory and real-world engineering challenges through principled data-driven methods.
— Mission of the ODDESSY Lab
We design optimization and control algorithms that are robust to uncertainty without requiring full distributional knowledge. Our work on distributionally robust and data-driven methods provides rigorous performance guarantees using only observed data, with applications in power systems, robotics, and multi-robot navigation.
We analyse strategic interactions among rational and boundedly-rational agents on networks. Our research quantifies how decentralised decision-making affects security investments, resource sharing, and systemic resilience in interdependent infrastructure.
We combine network science, dynamical systems, and game theory to model the spread of infectious diseases and opinions. We study how individual protective behaviour evolves under uncertainty and design optimal non-pharmaceutical interventions using closed-loop feedback.
We investigate how cognitive biases and prospect-theoretic preferences shape human interaction with shared engineered systems. We design dynamic incentive mechanisms — taxes, subsidies, and information signals — to align individual and societal objectives.
We study how agents learn and adapt strategies in non-stationary, adversarial, and cooperative environments. Our work develops convergent online learning algorithms and population game dynamics with provable stability and regret guarantees.
We apply distributionally robust optimisation and game-theoretic tools to energy systems, enabling reliable and economically efficient dispatch, demand response, and risk-aware control under the uncertainty brought by renewable generation.
Department of Electrical Engineering, IIT Kharagpur · Ph.D. Purdue University (2017) · Postdoc ETH Zürich (2018) · Young Associate, Indian National Academy of Engineering (2023)