This repository serves as a centralised archive of my academic progression and technical projects during my undergraduate studies at Queen Mary University of London. I am currently a first-year student on the Industrial Experience track (scheduled for 2027-2028), specialising in Computer Vision and Deep Learning.
- Degree: BSc (Hons) Computer Science and Artificial Intelligence.
- University: Queen Mary University of London.
- Foundation: Earned 45 Distinctions in Access to Higher Education (Computing & Mathematics).
- Focus: Deep Learning Architecture, Medical Imaging, and Procedural Programming.
Each directory represents a specific module or academic milestone. To maintain academic integrity, source code is only uploaded once cleared for public sharing by the university.
| Module Code | Module Name | Primary Technologies |
|---|---|---|
ecs401u/ |
Procedural Programming | Java, Software Development. |
ecs404u/ |
Computer Systems and Networks | Systems Architecture, Networking. |
Note: My primary research project, Medical Image Classification: Pulmonary Disease Detection, is maintained in its own dedicated repository to track experimental iterations and performance optimisation.
- Languages: Java (Procedural & OO), Python (AI & Research Focus).
- AI/CV Stack: PyTorch, OpenCV, NumPy, Scikit-learn, Matplotlib.
- Tools: GitHub, Jupyter Notebooks, VS Code, Miniconda, JetBrains Toolbox.
- Collaboration: All work contained herein is my own unless otherwise cited, following QMUL’s academic integrity policies.
- Security: Professional practices for data governance and documentation are applied throughout, mirrored from my industry experience at NovoPart.
- Email: monica.duarte@monicaduarte.com
- Portfolio: monicaduarte.com
- LinkedIn: linkedin.com/in/monicaduarteai
- GitHub: github.com/monicaduarteai
This README serves as a professional record of my undergraduate technical development.