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The broad goal of my research is to enable reliable and efficient learning and decision-making in large-scale distributed systems, while contending with modern challenges related to computation, communication, and adversarial robustness. To meet this goal, my research draws on ideas and tools from Control and Optimization Theory, Statistical Signal Processing, Machine Learning, and Network Science. While my work is theoretically grounded, the theory that I develop is motivated by a variety of application domains: multi-robot systems, wireless sensor networks, federated learning, edge-computing, estimation and control in smart cities and power-grids, and learning in social networks.
The broad goal of my research is to enable reliable and efficient learning and decision-making in large-scale distributed systems, while contending with modern challenges related to computation, communication, and adversarial robustness. To meet this goal, my research draws on ideas and tools from Control and Optimization Theory, Statistical Signal Processing, Machine Learning, and Network Science. While my work is theoretically grounded, the theory that I develop is motivated by a variety of application domains: multi-robot systems, wireless sensor networks, federated learning, edge-computing, estimation and control in smart cities and power-grids, and learning in social networks.
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IEEE Transactions on Systems, Man, and Cybernetics: Systemspp.1-13, (2024)
CoRR (2024)
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Proceedings of the AAAI Symposium Seriesno. 1 (2024): 308-308
IEEE Transactions on Systems, Man, and Cybernetics: Systemsno. 99 (2024): 1-13
CoRR (2024)
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CoRR (2024)
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arXiv (Cornell University) (2023)
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