Arunselvan Ramaswamy

Reinforcement Learning, Multi-agent Learning, Explainable Learning, Statistical Learning Theory, Stochastic Systems and Optimization.

Introduction to ML (Fall 2022)

Project in ML (Spring 2022, Spring 2023)

I am interested in research at the intersection of Computer Science and Mathematics. These days, I work on problems from the fields of intelligent decision making and massively large-scale multi-agent learning. I work on both the theoretical and practical aspects of the problems I tackle.

I regularly publish in conferences and journals connected to the following: Reinforcement learning, stochastic optimization and learning, data-driven control, multi-agent learning, Industry 4.0. and learning over networked systems.

Before joining KaU, I was a Lecturer at the Department of Computer Science, Paderborn University, Germany and a Junior Research Head (Data Science and Artificial Intelligence) at the Heinz Nixdorf Institute, Paderborn University. I also served as the stand-in Professor for the Chair of Intelligent Systems and Machine Learning for SoSe21 and WiSe21/22.

Redder, A., Ramaswamy, A. & Karl, H. (2022). Age of Information Process under Strongly Mixing Communication — Moment Bound, Mixing Rate and Strong Law. 58th Allerton Conference on Communication, Control, and Computing

Redder, A., Ramaswamy, A. & Karl, H. (2022). Practical Network Conditions for the Convergence of Distributed Optimization. IFAC Conference on Networked Systems (Necsys’22)

Redder, A., Ramaswamy, A. & Karl, H. (2022). Multi-agent policy gradient algorithms for cyber-physical systems with lossy communication. 14th International Conference on Agents and Artificial Intelligence

Gupta, P., Ramaswamy, A., Drees, J., Priesterjahn, C., Jager, T. & Hüllermeier, E. (2022). Automated Information Leakage Detection: Application to Side-Channel Detection in Cryptographic Protocols. 14th International Conference on Agents and Artificial Intelligence

Ramaswamy, A., & Bhatnagar, S. (2021). Analyzing approximate value iteration algorithms. Mathematics of Operations Research

Ramaswamy, A. & Hüllermeier, E. (2021). Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. IEEE Transactions on Artificial Intelligence

Ramaswamy, A., Redder, A. & Quevedo D.E. (2021). Optimization over time-varying networks with unbounded delays. IEEE Transactions on Automatic Control

Afifi, H., Ramaswamy, A., & Karl, H. (2021). Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks. IEEE International Conference on Communications (ICC)

Afifi, H., Ramaswamy, A., & Karl, H. (2021). A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks. IEEE Consumer Communications & Networking Conference

Drees, J.P., Gupta, P., Konze, A., Hüllermeier, E., Jager, T., Priesterjahn, C., Ramaswamy, A. & Somorovsky, J. (2021). Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOT! 14th ACM Workshop on Artificial Intelligence and Security

Ramaswamy, A. (2020). DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme. 28th Euromicro Int. Conf. on Parallel, Distributed, and Network-Based Processing

Heid, S., Ramaswamy, A., & Hüllermeier, E. (2020). Constrained Multi-Agent Optimization with Unbounded Information Delay. Proc. 30. Workshop Computational Intelligence, Berlin

Ramaswamy, A., Bhatnagar, S., & Quevedo, D. E. (2020). Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning. IEEE Transactions on Automatic Control

Leong, A. S., Ramaswamy, A., Quevedo, D. E., Karl, H., & Shi, L. (2019). Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems. Automatica

Redder, A., Ramaswamy, A. & Quevedo, D. E. (2019). Deep reinforcement learning for scheduling in large-scale networked control systems. NecSys, ICCOPT (Poster)

Ramaswamy, A., & Bhatnagar, S. (2019). Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning. IEEE Transactions on Automatic Control

Koenig, J., Malberg, S., Martens, M., Niehaus, S., Krohn-Grimberghe, A., & Ramaswamy, A. (2019). Multi-Stage Reinforcement Learning For Object Detection. Computer Vision Conference

Demirel, B., Ramaswamy, A., Quevedo, D. E., & Karl, H. (2018). DeepCAS: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters, 2(4), 737-742

Ramaswamy, A., & Bhatnagar, S. (2018). Analysis of gradient descent methods with nondiminishing bounded errors. IEEE Transactions on Automatic Control, 63(5), 1465-1471

Ramaswamy, A., & Bhatnagar, S. (2017). A generalization of the Borkar-Meyn theorem for stochastic recursive inclusions. Mathematics of Operations Research, 42(3), 648-661

Ramaswamy, A., & Bhatnagar, S. (2016). Stochastic recursive inclusion in two timescales with an application to the lagrangian dual problem. Stochastics, 88(8), 1173-1187

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., & Ramaswamy, A. (2014). Rainbow connection number of graph power and graph products. Graphs and Combinatorics, 30(6), 1363-1382

Basavaraju, M., Chandran, L. S., Rajendraprasad, D., & Ramaswamy, A. (2014). Rainbow connection number and radius. Graphs and Combinatorics, 30(2), 275-285