Personal profile
Biography
Madhushi is a Research Associate at the BT Ireland Innovation Centre (BTIIC), School of Computing, Ulster University, where she investigates the use of artificial intelligence to advance software engineering practices. Her research focuses on bug classification and localisation in source code, as well as identifying the point of introduction, enabling earlier intervention and improving software quality.
She holds an M.Sc. in Computer Science with Artificial Intelligence from the University of York and a B.Sc. (Hons) in Computer Science from the University of Sri Jayewardenepura, Sri Lanka. During her undergraduate studies, she received a Gold Award at the Asia-Pacific ICT Alliance (APICTA) Awards 2019 in Vietnam and a Silver Award at the National ICT Awards 2019 in Sri Lanka for her work on machine learning-based non-invasive medical technology.
Her industry experience provides a strong practical foundation for her research. At Zepz (formerly WorldRemit), she led backend development for customer-facing mobile applications, focusing on secure and reliable system design. At Axiata Digital Labs, she contributed to scalable systems for large telecom operators. Consultancy and teaching roles have further strengthened her expertise in software quality, debugging, and large-scale system development.
Her broader interests lie at the intersection of machine learning, software engineering, and data-driven systems. She is particularly interested in explainable AI, edge computing, and data analytics, with a focus on developing practical, trustworthy solutions. She approaches research with a commitment to continuous learning and adaptability, recognising that impactful systems evolve alongside both technological advances and human needs.
Education/Academic qualification
Master, Cross-Sectional Analysis of Housing Market Valuation Influenced by Economic Indicators in Northern Ireland with Machine Learning, University of York
Jun 2022 → Jun 2024
Award Date: 9 Jan 2025
Bachelor, Komposer V2: A Hybrid Approach to Intelligent Musical Composition Based on Generative Adversarial Networks with a Variational Autoencoder, University of Sri Jayewardenepura
Jan 2016 → 31 Dec 2019
Award Date: 2 Nov 2020
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being
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SDG 9 Industry, Innovation, and Infrastructure
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Komposer V2: A Hybrid Approach to Intelligent Musical Composition Based on Generative Adversarial Networks with a Variational Autoencoder
Welikala, M. D. & Fernando, T. G. I., 31 Oct 2020, Advances in Intelligent Systems and Computing. Vol. 1. p. 413 425 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review
2 Link opens in a new tab Citations (Scopus) -
Identifying Racist Social Media Comments in Sinhala Language Using Text Analytics Models with Machine Learning
Dias, D. S., Welikala, M. D. & Dias, N. G. J., Sept 2018, 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEEResearch output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review