Research & Projects

This project was part of my final-year undergraduate research at Queen Mary, University of London, and was conducted between September 2023 and May 2024. I worked independently under the supervision of [Supervisor’s Name, if applicable], with a focus on transfer learning in Natural Language Processing (NLP) between English and Tamil. My interest in multilingual NLP and the linguistic complexities of low-resource languages like Tamil inspired me to explore how transfer learning models could bridge language-specific challenges. The project leveraged real-world data—over 42,000 YouTube comments in both languages—to investigate model accuracy, bias, and performance limitations in cross-lingual sentiment and text classification tasks.

Objectives

The primary aim of this project was to evaluate the effectiveness of transfer learning when applied between a high-resource language (English) and a low-resource language (Tamil). The initial challenge was the significant disparity in available NLP tools, datasets, and annotated corpora for Tamil compared to English. 

Key objectives included:

Applying and fine-tuning pre-trained language models to perform sentiment analysis and classification in both English and Tamil.

Identifying and addressing challenges unique to Tamil, such as morphological richness, complex script, and limited annotated data.

Comparing model performance across both languages using key evaluation metrics (precision, recall, F1-score).

Improving performance in Tamil tasks through dataset manipulation, data augmentation, and multi-dataset training using Python-based NLP frameworks.

Essential considerations included handling noisy user-generated text, ensuring fairness in cross-lingual evaluation, and maintaining reproducibility of experiments.

Results

The project revealed notable performance differences between English and Tamil tasks. While transfer learning showed promising results in English, the models struggled with Tamil due to its lower resource support and syntactic complexity. Specifically:

Tamil models consistently underperformed in comparison, with lower precision and F1 scores.

Model performance was improved marginally through targeted data cleaning, bilingual training strategies, and custom preprocessing scripts.

The best-performing model architecture achieved reasonable accuracy on both tasks but highlighted the ongoing need for better Tamil-specific resources and tokenization techniques.

Unexpectedly, I observed that even small variations in script representation (e.g., Unicode normalization inconsistencies) significantly impacted model outcomes in Tamil. This underscored the importance of linguistic sensitivity in multilingual NLP.

Through this project, I gained hands-on experience in working with real-world multilingual datasets, fine-tuning transformer models, and performing error analysis. The findings not only contributed to my technical growth but also sparked an ongoing interest in low-resource NLP, which I hope to explore further in future academic or industry research.

This project was completed as part of a group Software engineering module at Queen Mary, University of London, between January and April 2023. Working collaboratively in a team of seven students, we designed and prototyped a functional cryptocurrency wallet mobile application for Android. The project was developed using Android Studio with Kotlin for the front end and an SQL database for managing secure data on the back end.

Our group aimed to explore the emerging fintech space while tackling the technical and UX challenges of digital currency management. The project gained significant recognition and was awarded Runner-Up for Best Cryptocurrency Application, highlighting both our technical execution and innovative approach. I contributed extensively to both the application’s development and its presentation, with a focus on front-end architecture and integration.

Objectives

The primary objectives of the project were to:

Develop a working prototype of a mobile cryptocurrency wallet that allows users to simulate key functions such as viewing balances, sending and receiving digital currency, and tracking transaction history.

Enhance security and data integrity by designing a reliable back-end architecture using SQL, while maintaining a smooth and responsive front-end interface.

Understand and apply key mobile development practices, including Android-specific UI/UX design, activity lifecycle management, and local data storage.

Collaborate effectively as a team, using version control tools (e.g., Git), task boards, and peer reviews to ensure alignment and progress throughout the development cycle.

Essential considerations included usability, performance, clarity in transaction flows, and addressing security concerns inherent in handling financial data—even within a simulated environment

Results

The final prototype successfully met all core functional requirements:

Users could log in, view their balance, initiate simulated transfers, and see a history of transactions through an intuitive interface.

The application integrated multiple UI components and state management strategies within Kotlin to deliver a user-friendly experience.

The SQL-based back end stored user data securely, and basic encryption measures were implemented for demonstration purposes.

The project was recognized in a university-wide showcase, where it earned Runner-Up status for its innovation, design, and attention to real-world fintech challenges.

Throughout the process, we encountered and addressed challenges such as managing group workflows, implementing secure authentication, and balancing feature complexity with time constraints. Personally, I deepened my skills in mobile development, UI design, and cross-functional teamwork—experience I plan to build on in future fintech or mobile-focused projects.

This project provided a strong foundation for understanding the intersection of mobile development and blockchain-inspired applications, and has inspired future exploration in digital finance and secure app development.

This project was developed between January and April 2023 as part of a collaborative software development module at Queen Mary, University of London. Our team of four set out to create a weather application specifically designed for tourists, integrating real-time weather forecasting with location-based tourist hotspot recommendations. I worked primarily on the front-end development using React, along with HTML and CSS, contributing to the user interface, responsiveness, and API integration.

The project was inspired by the practical needs of travelers who want not only weather updates but also suggestions for places to visit nearby, all in one intuitive interface. It served as a strong opportunity to apply web development skills while working with external APIs and geolocation technologies.

Objectives

The main goals of the project were to:

Create a web-based application that provides real-time weather forecasts tailored to the user’s current location.

Recommend nearby tourist attractions based on geographic data, offering added value for users who may be unfamiliar with the area.

Implement geolocation and external APIs, combining weather data and points of interest in a cohesive, user-friendly platform.

Design and develop the front end using React, ensuring responsiveness, usability, and performance across devices.

Key considerations throughout the project included API reliability, UI clarity, geolocation permissions, and the seamless integration of multiple data sources.

Results

The final application successfully achieved its intended purpose:

Users were able to detect their location, view the current and upcoming weather forecast, and receive a curated list of nearby tourist destinations.

The app utilized APIs for both weather data retrieval (e.g., OpenWeatherMap) and location-based recommendations, integrating them into a clean, functional React front end.

Our team delivered a fully responsive web app with a modern interface and smooth navigation, suitable for use on both desktop and mobile browsers.

One of the key lessons from the project was handling real-time data from multiple APIs and ensuring synchronization across components in React. We also gained practical experience with state management, error handling, and group coordination using Git and project planning tools.

The project was well received within our course and helped me solidify my skills in React-based front-end engineering, API integration, and geo-aware application design. It laid the groundwork for further work in travel tech, user-focused web apps, and data-driven interface design.