I am currently an Associate Professor at the Dept. of Computer Science, Karlstad University, Sweden.

Before this, 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.

To have a look at my educational background, click here. For an overview of the courses that I teach, click here. May be you are also interested in the books that keep me occupied, click here.

(updated CV to be uploaded soon) for the latest copy of my CV.

PUBLISHED

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

Preprints, under review

Ramaswamy, A. (2021). Deep Q-learning: to target or not to target

Redder, A., Ramaswamy, A. & Karl, H. (2021). Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms.

Afifi, H., Ramaswamy, A. & Karl, H. (2021). Multi-agent Reinforcement Learning for Autonomous Vehicles in Wireless Sensor Networks.

Redder, A., Ramaswamy, A. & Karl, H. (2021). Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference. arXiv:2105.04230