MSc (UWE Bristol), BSc Eng. (Hons) (Moratuwa)
About Me: My expertise lies in the latest technology stack, consisting of React, TypeScript, MySQL, MonogDB, Azure DevOps, Micro Frontend, Node.js and Python. I specialize in creating and executing strong Continuous Integration and Continuous Deployment (CI/CD) pipelines with skills in Github Actions, Docker, and Azure DevOps. I offer total solutions for contemporary system needs with Azure DevOps. My method focuses on developing optimal user experiences for apps, particularly when handling intricate user interactions. In addition, I utilize contemporary AI tools to expedite decision-making processes and lessen obstacles.
I began my coding adventure in college by working on small projects, eventually progressing to full-stack projects. Motivated by my love for technology, I have adopted lifelong learning to enhance my abilities. This dedication has driven my career ahead in the constantly changing technology industry.
My personal mantra is: 'Program with a clear goal, inspire with enthusiasm: Creating the technology of the future, today.'
Some examples of programming languages include JavaScript, TypeScript, and PHP.
Programming languages & platforms: React.js, Angular, PHP, Figma, Zustand, Redux, Git, GitHub, Cypress, Jest, Node.js, jQuery, Express.js, GraphQL, Material-UI, Tailwind, OAuth2, JWT, Webpack, Module Federation, PostgreSQL, RESTful APIs, Jira
The Cloud covers CI/CD, Docker, Azure, AWS.
Approaches: Scrum, Agile, Micro Services. Frontend Microservices
Areas of interest: Micro Architecture, Cloud Computing, Containerisation, Web Performance
Keywords: Natural Language Processing | Machine Learning / Deep Learning |
Theses: BSc
Nadeeshaan Gunasinghe, Dulanga Sashika, Anushka De Silva, and Thilina Premasiri, "Warana: Recruitment Helper for Enterprises", University of Moratuwa, 2015
Conference Papers
U. L. D. N. Gunasinghe, W. A. M. De Silva, N. H. N. D. de Silva, A. S. Perera, W. A. D. Sashika, and W. D. T. P. Premasiri, "Sentence Similarity Measuring by Vector Space Model", in Advances in ICT for Emerging Regions (ICTer), 2014 International Conference on, December. 2014, pp. 185--189. doi: 10.1109/ICTER.2014.7083899
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.