A seasoned Management Accountant with a demonstrated track record of success in diverse commercial domains, I bring a comprehensive skill set encompassing the entire spectrum of management accounting functions and financial customer service practices. My expertise lies in strengthening business systems with a systematic and methodical approach, particularly in areas such as forecasting, budgeting, variance analysis, cost allocation, and strategic planning. This approach proves invaluable in critical decision-making scenarios, where my analytical acumen drives informed and effective strategies.
Driven by an unwavering ambition to excel in Cost Accounting and Data Science, my background in Accounting and Statistics, complemented by proficiency in BI tools, emphasizes my commitment to continuous professional growth. As a results-oriented professional, I approach challenges with unwavering enthusiasm, decisiveness, and a steadfast focus on achieving goals. My objective is to make substantive contributions to organizational success by leveraging a dynamic blend of financial expertise and data-driven insights.
Keywords: Natural Language Processing | Sinhala | Machine Learning / Deep Learning |
Journal Papers
W M Yomal De Mel and Nisansa de Silva, "Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study", ICTer, vol. 18, no. 2, pp. 121-130, 2025. doi: 10.4038/icter.v18i2.7299
Conference Papers
Yomal De Mel and Nisansa de Silva, "GeeSanBhava: Sentiment Tagged Sinhala Music Video Comment Data Set", in International Conference on Computational Collective Intelligence, 2025, pp. 157--171. doi: 10.1007/978-3-032-10209-6_11
Yomal De Mel, Kasun Wickramasinghe, Nisansa de Silva, and Surangika Ranathunga, "Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches", in Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, 2025, pp. 166--173.
PI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
PI: Nisansa de Silva
We propose to build and analyze Sinhala language resources, datasets, and models to strengthen NLP research for this low-resource language, while connecting it to English and Tamil through bilingual lexicons, embeddings, and cross-lingual methods.
PI: Nisansa de Silva
We propose to advance social media text analysis by combining linguistic study, preprocessing, and deep learning models to better capture sentiment, structure, and meaning in informal, large-scale user-generated text.
