Machine Learning / Data Science
Data Science Master's student with a Computer Science background, focused on machine learning, computer vision and data analysis. I build and evaluate models, explore and visualise data, and care about understanding why systems behave the way they do.
I’m currently progressing through a Master's in Data Science, building on my Computer Science background at Lancaster University and a strong interest in applied machine learning.
My work centres on computer vision, machine learning, data preprocessing, visualisation and model evaluation. I enjoy the full workflow: exploring and structuring data, building features, implementing models and analysing results.
I tend to approach technical problems experimentally — forming a hypothesis, measuring behaviour, and refining the solution from evidence rather than intuition alone.
Data Science / Risk Analytics
Designed labelled and numerical datasets for company-risk monitoring, combining temporal event counts, sentiment indicators, operational signals and market features for classification and risk scoring.
Deep Learning / Computer Vision
Implemented deep-learning and computer-vision workflows with PyTorch, including convolutional neural networks, training pipelines, optimisation and evaluation of model behaviour.
AI / Data Analysis
Explored AI-assisted financial chart analysis and signal discovery, focusing on how data-driven methods can augment visual market analysis.
Core tools
Machine learning
Deep learning
Data & evaluation
AI concepts
Working style
Education
In progress
Developing further expertise in machine learning, statistical analysis, data visualisation and applied data science workflows.
Lancaster University · 2026
Focus areas include deep learning, specialised neural architectures, data preprocessing, statistical analysis, scalable software and performance engineering.
Languages
Open to graduate and early-career opportunities in machine learning, data science and applied AI.
andr_cyp[@]proton.me - without "[]" github