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About

Damminda Alahakoon is a prominent researcher whose work spans artificial intelligence, unsupervised machine learning, autonomous robotics, and AI-driven healthcare applications. Based at the intersection of intelligent systems and real-world problem solving, Alahakoon has made significant contributions to self-organizing neural architectures, sensor fusion, and the application of AI in complex domains ranging from rehabilitation medicine to sustainable energy infrastructure. Among his most impactful contributions is pioneering work in bio-inspired and unsupervised machine learning frameworks for autonomous robots, including multisensory fusion and distributed Growing Self-Organizing Maps for skill transfer learning — research that advances how robots perceive and adapt to dynamic environments. His interdisciplinary reach is evident in widely cited scoping reviews on AI in adult stroke rehabilitation (47 citations) and digital twin integration for sustainable power grids (32 citations), reflecting a commitment to translating AI research into meaningful societal outcomes. Earlier work on automated sewer pipe defect detection further demonstrates his applied focus. With publications spanning over a decade and accumulating citations across diverse fields, Alahakoon has established himself as a versatile and impactful figure in applied AI research, bridging foundational machine learning innovation with pressing global challenges in health, robotics, and sustainability.