The `*' represents that the authors have contributed equally.
Preprints
Various aspects of approximate Newton-type optimization algorithms for distributed learning, with Subhrakanti Dey.
Design of optimal detectors to detect adversarial attacks in control systems, with Siddhartha Ganguly.
Adversarial defense to linear systems with sensor/actuator placement, with Carlyn Medona, Abir De, and Debasish Chatterjee.
Detection of adversarial attacks in Markov Decision Processes, with Debasish Chatterjee.
Continuous-time robust model predictive control, with Siddhartha Ganguly and Debasish Chatterjee.
Tunable Interval Predictor Models with the Conditional Value-at-Risk, with Federico A. Ramponi.
Journal
S. Das and S. Dey. CONET-GIANT+: A communication-efficient Newton-type distributed optimization algorithm with linear convergence, submitted to JSAC.
S. Das, S. Dey, and L. Schenato. NETWORK-GIANT: An approximate Hessian-based fully distributed optimization algorithm and its convergence analysis, submitted.
S. Das, C. Medona, P. Patil, A. De, and D. Chatterjee. PRE-TADV: Adversarial Defense to Linear Time-Invariant Systems, submitted to ACM Transactions on Cyber-Physical Systems.
S. Ganguly, A. Aravind, S. Das, M. Nagahara, and D. Chatterjee. Sparse robust optimal control in continuous-time: a computationally viable approach, submitted to IEEE Transactions on Automatic Control.
S. Ganguly*, S. Das*, M. Anjali, and D. Chatterjee. Continuous-time robust predictive control Part I: exact algorithmic solutions, submitted to SICON.
A. Gupta, S. Das, and D. Chatterjee. SwiftNav: A probabilistic global optimizer derived from the Walker slice sampling, submitted to Annals of Operations Research.
S. Das, A. Ghosh, and D. Chatterjee. An algorithm to detect sensor attacks in cyber-physical systems, submitted to Automatica.
S. Das, P. Dey, and D. Chatterjee. Almost sure detection of the presence of malicious components in cyber-physical systems, Automatica, 167, 2024.
S. Das*, A. Aravind*, A. Cherukuri, and D. Chatterjee. Near-optimal solutions of convex semi-infinite programs via targeted sampling, Annals of Operation Research, 2022.
S. Ganguly*, S. Das*, D. Chatterjee, and R. Banavar. A discrete-time Pontryagin maximum principle under rate constraints, Asian Journal of Control, 2024.
S. Das, Luca Schenato, and S. Dey. HbNet-GIANT: An accelerated heavy-ball Newton-type distributed algorithm, submitted to ECC 2025.
S. Das and S. Dey. CoNet-GIANT: A compressed Newton-type fully distributed optimization algorithm, submitted to IEEE International Conference on Communication, 2026.
S. Das*, S. Ganguly*, A. Aravind*, and D. Chatterjee. Data-driven distributionally robust MPC via semi-infinite semidefinite programming: an application to finance, 26th International Symposium on Mathematical Theory of Networks and Systems (MTNS), 2024.
S. Das, A. Ghosh, and D. Chatterjee. Detection of false data injection attacks in cyber-physical systems, International Symposium on Information Theory (ISIT), 2024.
S. Das*, S. Ganguly*, M. Anjali, and D. Chatterjee. Towards continuous-time constrained MPC, IEEE Conference on Decision & Control, Singapore, 2023.
S. Ganguly*, S. Das*, D. Chatterjee, and R. Banavar. Rate constraint discrete-time maximum principle, 7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control, Berlin, 2021.
S. Das and S. Sen. Effect of history on the transient response of a closed-loop system with a fractional PI controller, Indicon, 2018.
S. Das, A. Aravind, D. Chatterjee, and A. Cherukuri . A global optimization approach to exact solutions of convex semi-infinite programs, Patent no. 512296, Application no. 202121061723.