Computer Science Engineering graduate with hands-on experience in full-stack software development and applied machine learning research. Skilled in building end-to-end web applications, RESTful APIs, and NLP / predictive…
Computer Science Engineering graduate with hands-on experience in full-stack software development and applied machine learning research. Skilled in building end-to-end web applications, RESTful APIs, and NLP / predictive-modeling solutions, with a track record of translating research findings into production-ready software and clear technical documentation.
<p>Designed and developed a Python Tkinter–based desktop application to streamline internal research workflows, reducing manual data-handling effort and improving day-to-day operational efficiency for the research team.<br>
Engineered and rigorously tested RESTful APIs using Postman, defining request/response contracts and validation checks that ensured reliable, low-latency client-server communication across the application stack.<br>
Collaborated within a cross-functional team to build and ship a full-stack web application using Spring Boot, React.js, and MySQL, contributing to backend service logic, frontend UI components, and relational schema design; deployed the application on Netlify for stakeholder access.<br>
Partnered with the data science team to integrate trained machine learning models into production-facing software, handling model input/output pipelines and verifying prediction accuracy post-integration.<br>
Participated in code reviews and iterative debugging cycles, adhering to version control best practices with Git/GitHub to maintain code quality and project traceability.<br>
Coordinated with senior engineers to translate functional requirements into technical tasks, contributing to sprint planning discussions and helping prioritize feature delivery.<br>
Documented application architecture, API endpoints, and setup procedures to support onboarding and knowledge transfer within the development team.</p>
<p>Conducted in-depth research into modern Natural Language Processing (NLP) techniques and fine-tuned an adapter-based mDeBERTa transformer model for movie review sentiment classification, improving parameter efficiency relative to full model fine-tuning while maintaining strong predictive performance.<br>
Designed and built a TabNet-based deep learning model to assess stroke risk from structured, tabular patient health data, applying attention-based feature selection to improve interpretability of clinical risk factors.<br>
Led end-to-end data preprocessing and exploratory data analysis (EDA) on clinical and textual datasets, including cleaning, feature engineering, and handling of class imbalance to strengthen downstream model reliability.<br>
Performed systematic evaluation of machine learning and deep learning models using metrics such as accuracy, F1-score, and AUC, benchmarking results against baseline approaches to guide model selection.<br>
Authored detailed research documentation and technical reports summarizing methodology, experiments, and findings, and actively collaborated with senior researchers on study design, hypothesis formulation, and result interpretation.<br>
Presented research progress and findings in regular team reviews, contributing to the direction of ongoing studies and helping shape future experimental design.<br>
Maintained organized experiment logs and version-controlled research code, enabling reproducibility of results and smoother handoff of findings to the broader team.</p>
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