Faculty · Department of Electrical Engineering

Rohit Parasnis

Rohit Parasnis

Contact

Email:
rohit@ee.iitb.ac.in

Address:
Department of Electrical Engineering Indian Institute of Technology Bombay, Powai, Mumbai - 400 076 India

Visit Website →

Research Interests

Networked dynamical systems Game Theory Optimization Matrix analysis with applications to incentive design for cyber-physical human systems Distributed learning in multi-agent systems Social learning Sustainability in natural and engineered systems

Qualifications

  • • Postdoc, MIT Schwarzman College of Computing, 2023–2026
  • • Postdoc, Purdue University, 2022–2023
  • • Ph.D. in Electrical Engineering, University of California San Diego, 2017–2022
  • • B. Tech in Electrical Engineering & M. Tech in Applied Mechanics, IIT Madras

Work Experience

  • • Postdoc, MIT Schwarzman College of Computing, 2023–2026
  • • Postdoc, Purdue University, 2022–2023

Publications

Journal Papers ▼
  • R. Parasnis and S. Amin, “Incentive Design for Sustainable Behavior in Coupled-Activity Economic Networks,” 2025, SSRN Working Paper No. 5359432, 2025.
  • B. Velasevic*, R. Parasnis*, C. Brinton, and N. Azizan, “A Comparative Analysis of Distributed Linear Solvers under Data Heterogeneity,” to appear, SIAM Journal on Mathematics of Data Science. arXiv preprint arXiv:2304.10640.
  • R. Parasnis, M. Franceschetti, and B. Touri, “A Perron-Frobenius Theorem for Strongly Aperiodic Stochastic Chains,” IEEE Transactions on Automatic Control, vol. 70, no. 7, pp. 4286–4301, July 2025.
  • R. Parasnis, M. Franceschetti, and B. Touri, “On Using the Age-Structured SIR Model for Epidemic Spreading over Time-Varying Random Networks,” SIAM Journal on Control and Optimization, vol. 63, no. 4, pp. 2472–2496, 2025.
  • R. Parasnis, S. Hosseinalipour, Y. W. Chu, M. Chiang, and C. G. Brinton, “Energy-Efficient Connectivity-Aware Learning Over Time-Varying D2D Networks,” IEEE Journal of Selected Topics in Signal Processing, special issue: “Toward Explainable, Reliable, and Sustainable Machine Learning,” vol. 18, no. 2, pp. 242–258, March 2024.
  • R. Parasnis, A. Verma, M. Franceschetti, and B. Touri, “A Random Adaptation Perspective on Distributed Averaging,” IEEE Control Systems Letters, vol. 7, pp. 241–246, 2023.
  • R. Parasnis, M. Franceschetti, and B. Touri, “On the Convergence Properties of Social Hegselmann-Krause Dynamics,” IEEE Transactions on Automatic Control, vol. 67, no. 2, pp. 589–604, 2022.
  • R. Parasnis, M. Franceschetti, and B. Touri, “Non-Bayesian Social Learning on Random Digraphs with Aperiodically Varying Network Connectivity,” IEEE Transactions on Control of Network Systems, vol. 9, no. 3, pp. 1202–1214, 2022. One of 13 articles published in the special issue “Dynamics and Behaviors in Social Networks.”
  • A. Piaseczny, E. Ruzomberka, R. Parasnis, and C. G. Brinton, “Adversarial Node Placement in Decentralized Federated Learning: Maximum Spanning-Centrality Strategy and Performance Analysis,” IEEE Internet of Things Journal, 2024.
Conference Papers / Book Chapters ▼
  • R. Parasnis, R. Khorramfar, S. Amin, “Oblique Projections for Flow Reconstruction and Misreporting Detection in Networks”, to appear, IEEE Conference on Decision and Control (CDC), 2026.
  • R. Parasnis and S. Amin, “Optimal Interventions in Coupled-Activity Network Games: Application to Sustainable Forestry,” 63rd IEEE Conference on Decision and Control (CDC), 2024.
  • B. Velasevic*, R. Parasnis*, C. Brinton, and N. Azizan, “On the Effects of Data Heterogeneity on the Convergence Rates of Distributed Linear System Solvers,” 62nd IEEE Conference on Decision and Control (CDC), 2023. Awarded the 2023 MIT Outstanding UROP Student Award.
  • R. Parasnis, S. Hosseinalipour, Y. W. Chu, M. Chiang, and C. G. Brinton, “Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks,” Proceedings of the Twenty-Fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc ’23), ACM, New York, NY, pp. 31–40, 2023. Acceptance rate: 22%.
  • R. Parasnis, A. Sakhale, R. Kato, M. Franceschetti, and B. Touri, “A Case for the Age-Structured SIR Dynamics for Modelling COVID-19,” 2021 60th IEEE Conference on Decision and Control (CDC), pp. 5508–5513, 2021.
  • R. Parasnis, M. Franceschetti, and B. Touri, “Uniform Strong Connectivity Is Not Necessary for Non-Bayesian Social Learning on Time-Varying Directed Graphs,” 2020 59th IEEE Conference on Decision and Control (CDC), pp. 4933–4940, 2020.
  • R. Parasnis, M. Franceschetti, and B. Touri, “On Graphs with Bounded and Unbounded Convergence Times in Social Hegselmann-Krause Dynamics,” 2019 IEEE 58th Conference on Decision and Control (CDC), pp. 6431–6436, 2019.
  • R. Parasnis, M. Franceschetti, and B. Touri, “Hegselmann-Krause Dynamics with Limited Connectivity,” 2018 IEEE Conference on Decision and Control (CDC), pp. 5364–5369, 2018.
  • R. Parasnis, A. Pawar, and M. Manivannan, “Multiscale Entropy and Poincaré Plot-Based Analysis of Heart Rate Variability and Pulse Rate Variability of ICU Patients,” 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 290–295, 2015.
  • S. Zehtabi, D. Han, R. Parasnis, S. Hosseinalipour, and C. Brinton, “Decentralized Sporadic Federated Learning: A Unified Methodology with Generalized Convergence Guarantees,” International Conference on Learning Representations (ICLR), 2025. Spotlight. arXiv preprint arXiv:2402.0344.
  • A. Piaseczny, E. Ruzomberka, R. Parasnis, and C. Brinton, “The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks,” 2024 IEEE International Conference on Communications (ICC), pp. 1679–1684, June 2024.
Ph.D. Thesis ▼
  • R. Parasnis, “Mathematical Tools and Convergence Results for Dynamics over Networks,” Ph.D. thesis, University of California San Diego, 2022.