With a rich professional journey spanning nine years and encompassing diverse roles in backend development across a few countries - the USA, Japan, Singapore, and Sweden - I have recently taken a significant step in my career by transitioning to the field of data science. This shift is grounded in a solid educational background, with a recent completion of a Master's degree in Data Science, building upon my earlier foundation in Engineering.
Throughout my career, I have consistently demonstrated my abilities in coding, problem-solving, and bringing forth technical innovations. These skills, coupled with my new academic credentials, have prepared me well for the challenges and opportunities in data science.
My enthusiasm for data analysis and predictive modeling is not just a professional interest but a passion. I have a keen interest in exploring how data can be used to forecast trends and inform decisions. My extensive experience in coding, especially in the context of backend development, gives me a unique edge in data science. It allows me to approach problems with a multifaceted perspective, blending technical proficiency with analytical insights.
Currently, I am eagerly looking for opportunities where I can apply my diverse skill set. I am particularly interested in roles that emphasize data-driven decision-making and the development of innovative solutions. My goal is to contribute meaningfully to projects that leverage data to make impactful decisions and drive progress.
Keywords: Natural Language Processing | Ontologies | Sinhala |
Conference Papers
Indeewari Wijesiri, Malaka Gallage, Buddhika Gunathilaka, Madhuranga Lakjeewa, Daya Wimalasuriya, Gihan Dias, Rohini Paranavithana, and Nisansa de Silva, "Building a WordNet for Sinhala", in Proceedings of the Seventh Global WordNet Conference, January. 2014, pp. 100--108.
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.
