Dr.
Nuwan Gunasekara

Nuwan Gunasekara is a postdoctoral researcher at Halmstad University, Sweden, and an AI researcher specialising in data stream learning and continual learning. His work focuses on adaptive neural networks and ensemble methods for evolving, non-stationary data, with particular emphasis on concept drift adaptation and mitigating catastrophic forgetting in real-time learning systems.

Nuwan’s research spans adaptive neural architectures, gradient-boosted models, and online hyperparameter optimisation, with publications in leading venues such as IJCAI, Machine Learning, and Data Mining and Knowledge Discovery. He is an active contributor and maintainer of open-source data stream learning frameworks, including MOA and CapyMOA, and regularly engages in tutorials, workshops, and program committees within the machine learning community.

With a background that bridges academia and industry—including a decade as a senior software engineer—Nuwan values interdisciplinary collaboration and aims to translate cutting-edge research into robust, reproducible, and impactful AI systems for real-world applications.