About Me


I am an Assistant Professor in the Artificial Intelligence Institute of UofSC (AIISC) and the Department of Computer Science and Engineering at the University of South Carolina (UofSC). Before joining UofSC, I was a postdoctoral associate in the Department of Electrical and Systems Engineering at the Washington University in St. Louis (WashU).
My past affiliations are: 1. Applied Mathematics Lab @WashU; 2. Brain Dynamics and Control Research Group @WashU; and 3. Embedded Control Systems and Networking Laboratory @MST.
The focus of my research is on the areas of dynamical systems and networks, data science and learning theory, and computational neuroscience.

News

I am looking for highly-motivated students at all levels to work with me ( flyer). If you're interested in dynamical systems, data science, and/or computational neuroscience, please reach out! (vignar@sc.edu)

Research

Dynamical Systems and Networks - Modeling, Estimation, and Control

Data Science and Learning Theory - Reinforcement Learning for Dynamical Systems and Data Analysis

Computational Neuroscience - Dynamic Inference and Spike Pattern/Sequence Control

Teaching

Fall 2021 - Neural Networks and their Applications to Intelligent Systems - CSCE 790 005

Spring 2022 - Optimization - CSCE 590 001

Publications

Journal Articles

15. Liang Wang, Vignesh Narayanan, Yao-Chi Yu, Yikyung Park, and Jr-Shin Li, "A two-stage hierarchical clustering method for structured temporal sequence data," Knowledge and Information Systems, vol.63(7), pp.1627-1662, 2021. [pdf]
14. Wei Miao, Vignesh Narayanan, and Jr-Shin Li, "Parallel residual projection: a new paradigm for solving linear inverse problems," Scientific Reports, vol. 10(1), pp.1-10, 2020. [pdf]
13. Vignesh Narayanan, Hamidreza Modares, Sarangapani Jagannathan, and Frank L. Lewis, "Event-driven off-policy reinforcement learning for control of interconnected systems," IEEE Transactions on Cybernetics, in press. [pdf-download]
12. Avimanyu Sahoo and Vignesh Narayanan, "Differential-game for resource aware approximate optimal control of large-scale nonlinear systems with multiple players," Neural Networks, Elsevier, vol. 124, pp.95-108, 2020. [pdf-download]
11. Vignesh Narayanan, Hamidreza Modares, and Sarangapani Jagannathan, "Optimal event-triggered control of input-affine nonlinear interconnected systems using multi-player games,” International Journal of Robust and Nonlinear Control, vol. 31(3), pp.950-970, 2021. [pdf-download]
10. Wei-Cheng Jiang, Vignesh Narayanan, and Jr-Shin Li, "Model learning and knowledge sharing for cooperative multiagent systems in stochastic environment," IEEE Transactions on Cybernetics, in press. [pdf]
9. Vignesh Narayanan, Jr-Shin Li, and Shinung Ching, "Biophysically interpretable inference of single neuron dynamics," Journal of Computational Neuroscience, Springer, vol. 47(1), pp.61-76, 2019. [pdf]
8. Avimanyu Sahoo and Vignesh Narayanan, "Optimization of sampling intervals for tracking control of nonlinear systems: A game theoretic approach," Neural Networks, Elsevier, vol. 114, pp.78-90, 2019. [pdf-download]
7. Vignesh Narayanan, Avimanyu Sahoo, Sarangapani Jagannathan, and Koshy George, "Approximate optimal distributed control of nonlinear interconnected systems using event-triggered nonzero-sum games," IEEE Transactions on Neural Networks and Learning systems, vol. 30(5), pp.1512-1522, 2018. [pdf-download]
6. Avimanyu Sahoo, Vignesh Narayanan, and Sarangapani Jagannathan, "A min–max approach to event- and self-triggered sampling and regulation of linear systems," IEEE Transactions on Industrial Electronics, vol. 66(7), pp.5433-5440, 2018. [pdf-download]
5. Vignesh Narayanan, Sarangapani Jagannathan, and Ram Kumar, "Event-sampled output feedback control of robotic manipulators using neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 30(6), pp.1651-1658, 2017. [pdf-download]
4. Vignesh Narayanan and Sarangapani Jagannathan, "Event-triggered distributed control of nonlinear interconnected systems using online reinforcement learning with exploration," IEEE Transactions on Cybernetics, vol. 48(9), pp.2510-2519, 2017. [pdf-download]
3. Vignesh Narayanan and Sarangapani Jagannathan, "Event-triggered distributed approximate optimal state and output control of affine nonlinear interconnected systems," IEEE Transactions on Neural Networks and Learning Systems, vol. 29(7), pp.2846-2856, 2017. [pdf-download]
2. Nathan Szanto, Vignesh Narayanan, and Sarangapani Jagannathan, "Event-sampled direct adaptive neural network output- and state-feedback control of uncertain strict-feedback system," IEEE Transactions on Neural Networks and Learning Systems, vol. 29(5), pp.1850-1863, 2017. [pdf-download]
1. Vignesh Narayanan and Sarangapani Jagannathan, "Distributed adaptive optimal regulation of uncertain large-scale interconnected systems using hybrid Q-learning approach," IET Control Theory and Applications, vol. 10(12), pp. 1448-1457, 2016. [pdf-download]

Preprints

2. Vignesh Narayanan, Wei Zhang, and Jr-Shin Li, "Moment-based ensemble control". (preprint arXiv:2009.02646) [pdf]
1. Vignesh Narayanan, Wei Miao, and Jr-Shin Li, "Disentangling drift-and control-vector fields for interpretable inference of control-affine systems". (preprint arXiv:2004.10954) [pdf]

Conference Papers

24. Avimanyu Sahoo, Vignesh Narayanan, and Qiming Zhao, "Adaptive gain observers for distributed state estimation of linear systems," in IEEE American Control Conference (ACC), New Orleans, Louisiana, USA, 2021.
23. Avimanyu Sahoo, Vignesh Narayanan, and Qiming Zhao, "Finite-time adaptive optimal output feedback control of linear systems with intermittent feedback," in IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020.
22. Yao-Chi Yu, Vignesh Narayanan, Shinung Ching, and Jr-Shin Li, "Learning to control neurons using aggregated measurements," in IEEE American Control Conference (ACC), Denver, USA, pp.4028-4033, 2020.
21. Wei Zhang, Vignesh Narayanan, and Jr-Shin Li, "Robust population transfer for coupled spin ensembles," in IEEE Conference on Decision and Control (CDC), Nice, France, pp.419-424, 2019. [pdf]
20. Vignesh Narayanan, Jason Ritt, Jr-Shin Li, and Shinung Ching, "A learning framework for controlling spiking neural networks," in IEEE American Control Conference (ACC), Philadelphia, USA, pp.211-216 , 2019.
19. Vignesh Narayanan, Sarangapani Jagannathan, and Rohollah Moghadam, "Optimality in event-triggered adaptive control of uncertain linear dynamical systems," in AIAA Scitech Forum, San Diego, CA, USA, pp.2187, 2019.
18. Avimanyu Sahoo and Vignesh Narayanan, "Event-based near optimal sampling and tracking control of nonlinear systems," in IEEE Conference on Decision and Control (CDC), Miami Beach, FL, USA, pp.55-60, 2018.
17. Vignesh Narayanan, Avimanyu Sahoo, and Sarangapani Jagannathan, "Approximate optimal distributed control of nonlinear interconnected systems using nonzero-sum games," in IEEE Conference on Decision and Control (CDC), Miami Beach, FL, USA, pp.2872-2877, 2018.
16. Vignesh Narayanan, Avimanyu Sahoo, and Sarangapani Jagannathan, "Optimal adaptive distributed control of linear interconnected systems," in IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, pp.1441-1446, 2018.
15. Avimanyu Sahoo, Vignesh Narayanan, and Sarangapani Jagannathan, "Event-triggered control of N-player nonlinear systems using nonzero-sum games," in IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, pp.1447-1452, 2018.
14. Vignesh Narayanan, Avimanyu Sahoo, and Sarangapani Jagannathan, "Optimal event-triggered control of nonlinear systems: A min-max approach," in IEEE American Control Conference (ACC), Milwaukee, WI, USA, pp.3441–3446, 2018.
13. Avimanyu Sahoo, Vignesh Narayanan, and Sarangapani Jagannathan, "Optimal event-triggered control of uncertain linear networked control systems: A co-design approach," in IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, 2017.
12. Avimanyu Sahoo, Vignesh Narayanan, and Sarangapani Jagannathan, "Optimal sampling and regulation of uncertain interconnected linear continuous time systems," in IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, 2017.
11. Haci Mehmet Guzey, Vignesh Narayanan, Sarangapani Jagannathan, Travis Dierks, and Levent Acar, "Distributed consensus-based event-triggered approximate control of non-holonomic mobile robot formations," in IEEE American Control Conference (ACC), Seattle, WA, USA, pp.3194-3199, 2017. [pdf]
10. Nathan Szanto, Vignesh Narayanan, and Sarangapani Jagannathan, "Event-sampled control of quadrotor unmanned aerial vehicle," in IEEE American Control Conference (ACC), Seattle, WA, USA, pp.2956-2961, 2017.
9. Vignesh Narayanan and Sarangapani Jagannathan, "Online reinforcement with exploration for distributed control," in International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, pp.4022-4027, 2017.
8. Vignesh Narayanan and Sarangapani Jagannathan, "Approximate optimal distributed control of uncertain nonlinear interconnected systems with event-sampled feedback," in IEEE Conference on Decision and Control (CDC), Las Vegas, NV, pp. 5827-5832, 2016.
7. Nathan Szanto, Vignesh Narayanan, and Sarangapani Jagannathan, "Event-sampled direct adaptive NN state-feedback control of uncertain strict-feedback system," in IEEE Conference on Decision and Control (CDC), Las Vegas, NV, pp. 3395-3400, 2016.
6. Vignesh Narayanan and Sarangapani Jagannathan, "Event-sampled adaptive neural network control of robot manipulators," in International joint conference on neural networks (IJCNN), Vancouver, BC, pp. 4941-4946, 2016.
5. Vignesh Narayanan and Sarangapani Jagannathan, "Distributed event-sampled approximate optimal control of interconnected affine nonlinear continuous-time systems," in IEEE American Control Conference (ACC), Boston, MA, pp.3044-3049, 2016.
4. Vignesh Narayanan and Sarangapani Jagannathan, "Distributed adaptive optimal regulation of uncertain large-scale linear networked control systems using Q-learning," in IEEE Symposium Series on Computational Intelligence, pp.587-592, 2015.
3. Vignesh Narayanan, Avimanyu Sahoo, and Sarangapani Jagannathan, "Optimal regulation of uncertain linear discrete-time systems using event-sampled Q-learning and adaptive dynamic programming,” in 17th Yale Workshop on Adaptive and Learning Systems, 2015.
2. Vignesh Narayanan, Akhilesh Swarup, and Pulak Halder, "Design of autopilots for tactical aerospace vehicles: a comparative study," in IFAC Proceedings, vol.47(1), pp. 271-278, 2014. [pdf]
1. Siddhardha Kedarisetty, Vignesh Narayanan, and Pulak Halder, "Autopilot design for flexible tactical aerospace vehicle using parameter plane technique," in IFAC Proceedings, vol. 47(1), pp. 211-218, 2014. [pdf]

Contributed Book Chapters

1. Vignesh Narayanan, Haci Mehmet Guzey, and Sarangapani Jagannathan, "Event sampled adaptive control of robot manipulators and mobile robot formations," Adaptive Control for Robotic Manipulators, Editors: Dan Zhang, Bin Wei, CRC Press/Taylor & Francis Group, pp.124-158, 2016. [abstract]
2. Rohollah Moghadam, Sarangapani Jagannathan, Vignesh Narayanan, and Krishnan Raghavan, "Optimal adaptive control of partially uncertain linear continuous-time systems with state delay,” Handbook of Reinforcement Learning and Control, Editors: Kyriakos G. Vamvoudakis, Yan Wan, Frank Lewis, and Derya Cansever, Springer International Publishing, pp.243-272, 2021. [Details]

Contributed Magazine Articles

1. Avimanyu Sahoo, Vignesh Narayanan, Jagannathan Sarangapani, "Resource aware learning-based optimal control of cyber-physical systems,” in TC-CPS Newsletter, vol. 6(1), pp.24-34, March-2021. [pdf]

Posters and Abstracts

2. Vignesh Narayanan, Jr-Shin Li, and Shinung Ching, "Biophysically interpretable, model-free identification of neuronal dynamics," in Computational and Systems Neuroscience (Cosyne), Denver, CO, 2018.
1. Jr-Shin Li, Wei Miao, and Vignesh Narayanan, "A scalable computational framework for solving large-scale linear regression problems," in The Second Joint SIAM/CAIMS Annual Meeting (AN20), (scheduled in) Toronto, Canada, July 2020 (Accepted contributed lecture).