ML/AI Research Engineering (Applied ML and Maths)
Researching and building mathematically grounded machine learning systems for complex, real-world problems
Computer Science & Mathematics undergraduate at the University of Manchester, building a career in ML/AI research and engineering.
I co-authored a research paper on nonlinear time-series forecasting for air pollution using chaos theory and statistical modelling, presented at the CHAOS 2025 International Conference at Greece. This work deepened my expertise in dynamical systems, probability theory, and data-driven analysis.
At Manchester Stinger Motorsports, I develop Vision-informed control algorithms for an autonomous Formula Student race car — improving trajectory tracking accuracy by 80% and reducing response latency by 18ms through data-driven optimisation and telemetry analysis.
During my internship at BASARSOFT, I engineered large-scale data validation pipelines for 21 million geospatial records, improving data integrity across 3 systems by 40%. I also completed an intensive ML, AI & Data Science summer programme at Sabanci University.
My technical foundation includes Python, PyTorch, TensorFlow, and Scikit-learn, alongside strong mathematical skills in probability, optimisation, and statistical modelling. I'm pursuing research-oriented modules and have a growing interest in quantum algorithms.
I'm always interested in hearing about new projects and opportunities. Whether you have a question or just want to say hi, feel free to reach out!