Deep Learning for Predictive Analysis
of Cardiovascular Diseases
Combining Deep learning with Explainable AI (XAI) for the prediction of Cardiovascular Disease. Implemented using PyTorch
About Me
I recently completed an MSc in Artificial Intelligence at Northumbria University, where I developed a strong foundation in machine learning, deep learning, statistical modelling, and time series analysis. My primary technical work is in Python, leveraging libraries such as PyTorch, TensorFlow, scikit-learn, pandas, and NumPy; however, I am comfortable working across multiple programming languages and tools commonly used in machine learning engineering and data science environments, including SQL and modern data engineering frameworks. I focus on building clean, reproducible, and well-structured modelling workflows that translate effectively from experimentation to real-world application.
My interests centre on applying machine learning to complex, real-world problems — particularly those involving structured data, forecasting, optimisation, and decision-making under uncertainty. While I have a strong interest in financial applications and quantitative research, I am equally motivated by opportunities across industry where rigorous modelling, data-driven experimentation, and scalable ML systems can create measurable impact.
I bring strong analytical and statistical thinking, a research-oriented mindset, and a practical awareness of how models perform in real environments. I am a structured problem-solver who values clarity, reproducibility, and technical precision. I communicate complex ideas clearly, learn quickly in fast-moving teams, and approach challenges with a competitive, high-performance mindset focused on continuous improvement and delivering meaningful results.
