I am a Computer Science and Engineering undergraduate at the University of Moratuwa with a strong technical foundation in software engineering and a keen interest in full-stack development, mobile application development, and machine learning. Experienced in designing, developing, and maintaining scalable applications with exposure to both web and mobile platforms. I actively explore and adapt to new technologies and frameworks, enabling me to work effectively in fast-paced, evolving environments. My academic and project experiences have strengthened my problem-solving abilities, analytical thinking, and understanding of end-to-end system development. Alongside my technical work, I have gained leadership and collaboration experience through my role as a CSE department representative and active involvement in student clubs and societies such as IEEE, MoraSpirit, and the Classical Music Society at the University of Moratuwa. I have contributed to organizing and leading events, which strengthened my communication, coordination, teamwork, and leadership skills while working with diverse multidisciplinary teams. Driven by continuous learning, innovation, and creativity, I aim to apply my technical expertise to build efficient, reliable, and impactful software solutions.
Keywords: Machine Learning / Deep Learning |
Conference Papers
Mineth Weerasinghe, Himindu Kularathne, Methmini Madhushika, Danuka Lakshan, Nisansa de Silva, Adeesha Wijayasiri, and Srinath Perera, "From Monolith to Microservices: A Comparative Evaluation of Decomposition Frameworks", in Recent Trends and Challenges in Information Systems and Technologies, 2026, pp. 309--320. doi: 10.1007/978-3-032-32032-2_29
Preprints
Mineth Weerasinghe, Himindu Kularathne, Methmini Madhushika, Danuka Lakshan, Nisansa de Silva, Adeesha Wijayasiri, and Srinath Perera, "TripleBound: Triplet-Guided Heterogeneous Graph Learning for Microservice Decomposition", arXiv preprint arXiv:2609.11212, 2026
ActivePI: Sandareka Wickramanayake
We propose to advance automated program repair by leveraging natural language processing techniques, iterative repair strategies, and explainable AI frameworks to enhance accuracy, robustness, and interpretability of repair systems.
