Shana Moothedath

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Education

• Postdoctoral Research Scholar (2018-2021) University of Washington, Seattle
• Ph.D (2018) Indian Institute of Technology Bombay

Research Interests Distributed/federated learning, Reinforcement learning, Bandit learning, Control and security of cyber-physical systems, Analysis and control of networked systems, Game theory, Control and optimization

Publications

• Moothedath, N. Vaswani, Fully Decentralized and Federated Low-Rank Compressive Sensing, In American Control Conference (ACC), 2022.
• Moothedath, D. Sahabandu, J. Allen, A. Clark, L. Bushnell, W. Lee, and R. Poovendran. A Game Theoretic Approach for Dynamic Information Flow Tracking to Detect Multi-Stage Advanced Persistent Threats. IEEE Transactions on Automatic Control, vol. 65, no. 12, pp: 5248 – 5263, 2020,
• Sahabandu, S. Moothedath, J. Allen, L. Bushnell, W. Lee, and R. Poovendran, Quickest Detection of Advanced Persistent Threats: A Semi-Markov Game Approach. In International Conference on Cyber-Physical Systems (ICCPS), Sydney, Australia, April 2020.
• Misra, S. Moothedath, H. Hosseini, J. Allen, L. Bushnell, W. Lee, and R. Poovendran. Learning Equilibria in Stochastic Information Flow Tracking Games with Partial Knowledge. In IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.
• Sahabandu, S. Moothedath, J. Allen, A. Clark, L. Bushnell, W. Lee, and R. Poovendran. Dynamic Information Flow Tracking Games for Simultaneous Detection of Multiple Attackers. In IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.
• Moothedath, P. Chaporkar, and M. N. Belur. Approximating Constrained Minimum Cost Input-Output Selection for Generic Arbitrary Pole Placement in Structured Systems. Automatica, vol. 107, pp: 200-210, 2019.
• Moothedath, P. Chaporkar, and M. N. Belur. A Flow-Network Based Polynomial-Time Approximation Algorithm for the Minimum Constrained Input Structural Controllability Problem. IEEE Transactions on Automatic Control, vol. 63, no. 9, pp: 3151- 3158, 2018
• Sahabandu, S. Moothedath, J. Allen, L. Bushnell, W. Lee, and R. Poovendran. A Multi-Agent Reinforcement Learning Approach for Dynamic Information Flow Tracking Games for Advanced Persistent Threats. In IEEE Transactions on Automatic Control, 2024.
• Moothedath, D. Sahabandu, J. Allen, L. Bushnell, W. Lee, and R. Poovendran. Stochastic Dynamic Information Flow Tracking Game using Supervised Learning for Detecting Advanced Persistent Threats, Automatica, 2024.
• Moothedath, D. Sahabandu, J. Allen, A. Clark, L. Bushnell, W. Lee, and R. Poovendran. Dynamic Information Flow Tracking for Detection of Advanced Persistent Threats: A Stochastic Game Approach. In IEEE Transactions on Automatic Control, 2024.

Primary Strategic Research Area

Secure Cyberspace & Autonomy

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