About
Arunselvan Ramaswamy is a Senior Lecturer in Data Science, AI, and Machine Learning Research at Karlstad University. His research focuses on the intersection of Computer Science and Mathematics, with primary interests in reinforcement learning, multi-agent systems, stochastic optimization, and control theory. He has held prior roles as Lecturer at Paderborn University and Junior Research Head at the Heinz Nixdorf Institute, Germany.
His work spans theoretical foundations of learning algorithms and practical applications in cyber-physical systems, wireless sensor networks, and Industry 4.0. Key contributions include distributed optimization frameworks, reinforcement learning for autonomous systems, and stability analysis of stochastic approximation methods. He has published widely in top venues such as IEEE Transactions on Automatic Control, NeurIPS, and IFAC conferences.
Ramaswamy has contributed to interdisciplinary projects including automated side-channel detection in cryptographic protocols and deep reinforcement learning for microgrid control. His teaching includes courses on machine learning fundamentals and project-based ML applications at Karlstad University.
Research highlights include developing the DeepCAS algorithm for control-aware scheduling, analyzing convergence properties of distributed optimization under communication constraints, and exploring gradient-based methods with unbounded delays. Current work emphasizes scalable multi-agent learning and explainable AI systems.
