2024
12. J. Lin, S. Moothedath, and N. Vaswani, Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits, ICML, 2024.
11. J. Lin and S. Moothedath, Multi-Task Learning for Stochastic Bandits with Stage-Wise Constraints, Submitted.
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10. A. A. Abbasi, S. Moothedath, N. Vaswani, Fast Federated Low Rank Matrix Completion, Submitted.
9. S. T. Jose and S. Moothedath, Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis, Submitted. [Preprint] [Bibtex]
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8. J Lin and S. Moothedath, Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints, Submitted. [Preprint] [Bibtex]
7. G. Joseph, S. Moothedath, and J. Lin. Minimal Input Structural Modifications for Strongly Structural Controllability, Submitted. [Preprint] [Bibtex]
6. D. Sahabandu, S. Moothedath, J. Allen, L. Bushnell, W. Lee, and R. Poovendran. A Reinforcement Learning Approach for Dynamic Information Flow Tracking Games for Detecting Advanced Persistent Threats, Submitted.
5. S. Moothedath and N. Vaswani. Fast, Communication-Efficient, and Provable Decentralized Low Rank Matrix Recovery. Submitted. [Preprint] [Bibtex]
4. S. 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, vol. 159, 2024. [Preprint] [Bibtex]
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3. S. Moothedath and N. Vaswani, Decentralized Low Rank Matrix Recovery from Column-wise Projections by Alternating GD and Minimization. To appear at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024.
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2. Jiabin Lin, Karuna Anna Sajeevan, Bibek Acharya, Shana Moothedath, Ratul Chowdhury. Distributed Stochastic Contextual Bandits for Protein Drug Interaction, To appear in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024
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1. J. Lin and S. Moothedath, Federated Learning for Heterogeneous Bandits with Unobserved Contexts, To appear in International Symposium on Information Theory (ISIT), 2024. [Preprint] [Bibtex]
2023
7. D. Sahabandu, S. Moothedath, J. Allen, L. Bushnell, W. Lee, and R. Poovendran. RL-ARNE: A Reinforcement Learning Algorithm for
Computing Average Reward Nash Equilibrium of Nonzero-Sum Stochastic Games. To appear in IEEE Transactions on Automatic Control, 2023. [Preprint] [Bibtex]
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6. S. 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. To appear in IEEE Transactions on Automatic Control, 2023. [Preprint] [Bibtex
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5. A. A. Abbasi, S. Moothedath, N. Vaswani, Fast Federated Low Rank Matrix Completion, Fifty-Ninth Annual Allerton Conference on Communication, Control, and Computing, 2023.
4. J. Lin and S. Moothedath, Distributed Stochastic Bandit Learning with Delayed Context Observation, European Control Conference (ECC), 2023.
3. J. Lin and S. Moothedath, Distributed Stochastic Bandits with Hidden Contexts, European Control Conference (ECC), 2023.
2. S. Moothedath, N. Vaswani, Comparing Decentralized Gradient Descent Approaches and Guarantees, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.
1. J. Lin and S. Moothedath, Feature Selection in Distributed Stochastic Linear Bandits, American Control Conference (ACC), 2023.
2022
4. S. Moothedath, N. Vaswani, Dec-AltProjGD: Fully-Decentralized Alternating Projected Gradient Descent for Low Rank Column-wise Compressive Sensing, Conference on Decision and Control (CDC), 2022.
3. J. Lin, Xian Yeow Lee, Talukder Jubery, Shana Moothedath, Soumik Sarkar, and Baskar Ganapathysubramanian, Stochastic Conservative Contextual Linear Bandits, Conference on Decision and Control (CDC), 2022.
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2. S. Moothedath, N. Vaswani, Fully Decentralized and Federated Low Rank Compressive Sensing, American Control Conference (ACC), 2022.
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1. S. Moothedath, X. Y. Lee, T. Jubery, B. Ganapathysubramanian, S. Sarkar, A Conservative Stochastic Contextual Bandit Based
Framework for Farming Recommender Systems, AAAI Workshop on AIAFS, 2022.
2021
1. R. Gundeti, S. Moothedath, and P. Chaporkar. Feedback Robustness in Structured Closed-loop System. European Journal of Control, vol. 57, pp: 95-108, 2021. [Pdf] [Bibtex]
2020
4. S. 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. [Pdf] [Bibtex]
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3. K. Yashashwi, S. Moothedath, and P. Chaporkar. Minimum Cost Input Selection for Strong Structural Controllability. Under review. [Preprint] [Bibtex]
2. A. Joshi, S. Moothedath, and P. Chaporkar. Minimum Cost Feedback Selection in Structured Systems: Hardness and Approximation Algorithm. IEEE Transactions on Automatic Control. IEEE Transactions on Automatic Control, vol. 65, no. 12, pp: 5517 - 5524, 2020. [Pdf] [Bibtex]
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1​. D. Sahabandu, S. Moothedath, J. Allen, L. Bushnell, W. Lee, and R. Poovendran, Quickest Detection of Advanced Persistent Threats: A Semi-Markov Game Approach, International Conference on Cyber-Physical Systems (ICCPS), Sydney, Australia, April, 2020.
2019
11. S. Moothedath, P. Chaporkar, and M. N. Belur. Optimal Network Topology Design in Composite Systems for Structural Controllability. IEEE Transactions on Control of Network Systems, vol. 7, no. 3, pp: 1164 - 1175, 2019. [Pdf] [Bibtex]
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10. S. 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. [Pdf] [Bibtex]
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9. S. Moothedath, P. Chaporkar, and M. N. Belur. Optimal Selection of Essential Interconnections for Structural Controllability in Heterogeneous Subsystems. Automatica, vol. 103, pp: 424-434, 2019. [Pdf] [Bibtex]
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8. S. Moothedath, P. Chaporkar, and M. N. Belur. Sparsest Feedback Selection for Structurally Cyclic Systems with Dedicated Actuators and Sensors in Polynomial Time. IEEE Transactions on Automatic Control, vol. 64, no. 9, pp: 3956-3963, 2019. [Pdf] [Bibtex]
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7. D. Sahabandu, S. Moothedath, J. Allen, L. Bushnell, W. Lee, and R. Poovendran, Stochastic Dynamic Information Flow Tracking Game with Reinforcement Learning, Conference on Decision and Game Theory for Security (GameSec), Stockholm, Sweden, October, 2019.
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6. D. 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, IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.
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5. S. 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, IEEE Conference on Decision and Control (CDC), Nice, France, December, 2019.
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4. D. Shabandu, S. Moothedath, J. Allen, A. Clark, L. Bushnell, W. Lee, and R. Poovendran. A Game Theoretic Approach for Dynamic Information Flow Tracking with Conditional Branching, American Control Conference (ACC), pp: 2289-2296, Philadelphia, USA, July, 2019.
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3. S. Moothedath, P. Chaporkar, and A. Joshi. Optimal Network Topol- ogy Design in Composite Systems with Constrained Neighbours for Structural Controllability, American Control Conference (ACC), pp: 2078-2083, Philadelphia, USA, July, 2019.
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2. K. Yashashwi, S. Moothedath, and P. Chaporkar. Minimizing Inputs for Strong Structural Controllability, American Control Conference (ACC), pp: 2048-2053, Philadelphia, USA, July, 2019.
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1. S. Moothedath, K. Yashashwi, and P. Chaporkar. Target Controllability for Structured Systems, European Control Conference (ECC), pp: 3484-3489, Naples, Italy, June, 2019.
2018
4. S. Moothedath, P. Chaporkar, and M. N. Belur. Minimum Cost Feedback Selection for Arbitrary Pole Placement in Structured Systems. IEEE Transactions on Automatic Control, vol. 63, no. 11, pp: 3881-3888, 2018. [Pdf] [Bibtex]
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3. S. 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. [Pdf] [Bibtex]
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2. S. Moothedath, D. Shabandu, A. Clark, S. Lee, W. Lee, and R. Poovendran. Multi-stage Dynamic Information Flow Tracking Game, Conference on Decision and Game Theory for Security (GameSec), pp: 80-101, Seattle, USA, 2018.
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1. S. Moothedath, P. Chaporkar, and M. N. Belur. A Randomized Algorithm for the Minimum Cost Constrained Input Selection for State Space Structural Controllability, European Control Conference (ECC), pp: 483-488, Limassol, Cyprus, 2018.
2016
3. S. Moothedath, P. Chaporkar, and M. N. Belur. A Maximum Likelihood Based Offline Estimation of Student Capabilities and Question Difficulties. Perspectives in Education, vol. 34, no. 4, pp: 99-115, 2016. [Pdf] [Bibtex]
2. S. Kumar, S. Moothedath, P. Chaporkar, and M. N. Belur. An MCMC based Course to Teaching Assistant Allocation, International Conference on Network, Communication and Computing (ICNCC), pp: 131-134, Kyoto, Japan, 2016.
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1. S. Moothedath, P. Chaporkar, and M. N. Belur. A Maximum Likelihood Based Offline Estimation of Student Capabilities and Question Difficulties, International Association for Educational Assessment Conference (IAEA), Cape Town, South Africa, 2016.
2014
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1. S. Moothedath, R. Gangadharan and R. L. Kumari. On board trajectory Optimization of a Launch Vehicle with Splash Down Constraint, IEEE International Conference on Magnetics, Machines & Drives, pp:1-6, Kerala, India, 2014.
Posters
[P-6] Shana Moothedath, Dinuka Sahabandu, Joey Allen, Andrew Clark, Linda Bushnell, Wenke Lee, Radha Poovendran. Multi-Stage Dynamic Information Flow Tracking Game for Advanced Persistent Threats. Invited Poster in Workshop on Analysis and Control of Complex Networks: State of the Art and Research Directions, American Control Conference, Philadelphia, USA, July 2019 and ONR-MURI Mid-Review Meeting, Washington DC, USA, April 2019.
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[P-5] Dinuka Sahabandu, Shana Moothedath, Joey Allen, Andrew Clark, Linda Bushnell, Wenke Lee, Radha Poovendran. A Game Theoretic Approach for Resource Efficient Dynamic Information Flow Tracking. In Poster Session, ONR-MURI Mid-Review Meeting, Washington DC, USA, April 2019.
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[P-4] Shruti Misra, Shana Moothedath, Hossein Hosseini, Joey Allen, Linda Bushnell, Wenke Lee, Radha Poovendran. Learning Equilibria in Stochastic Information Flow Tracking Games with Partial Knowledge. In Poster Session, ONR-MURI Mid-Review Meeting, Washington DC, USA, April 2019.
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[P-3] Shana Moothedath, Dinuka Sahabandu, Joey Allen, Andrew Clark, Linda Bushnell, Wenke Lee, Radha Poovendran. Multi-Stage Dynamic Information Flow Tracking Game for Advanced Persistent Threats. In iREDEFINE Workshop, ECEDHA Conference, Arizona, USA, March 2019.
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[P-2] Shana Moothedath, Dinuka Sahabandu, Joey Allen, Andrew Clark, Linda Bushnell, Wenke Lee, Radha Poovendran. Multi-Stage Dynamic Information Flow Tracking Game. In Meet the Faculty Candidate Poster Session, IEEE Conference on Decision and Control, Florida, USA, December 2018 and in Poster Session, Western USA ECE Departments Heads Association (WECEDHA), Seattle, USA, November, 2018.
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[P-1] Shana Moothedath, Saurabh Kumar, Prasanna Chaporkar, and Madhu N. Belur. Solving Allocation Problems using Markov Chain Monte Carlo Technique. In Poster Session, Indian Control Conference, Hyderabad, India, January 2016.
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PhD Thesis
Shana Moothedath, “Optimizing Structured Linear Time Invariant Systems: Complexity and Algorithms”, PhD Thesis, Dept. of Electrical and Electronics Engineering, Indian Institute of Technology Bombay, 2018.