Birmingham, England, United Kingdom
I’m a Data Scientist and Software Engineer, and a graduate with an MSc (Distinction) in Data Science from the University of Salford, where I’ve developed intelligent, explainable AI systems to address real-world healthcare challenges. With a First-Class Honours in Computer Science and a background in full-stack development, I bring a powerful blend of analytical thinking and practical engineering skills. My recent work includes building lightweight deep learning models (EfficientNetB0-B3) for diabetic retinopathy severity grading, applying Grad-CAM++ and LIME for explainability, and deploying an interactive Streamlit prototype for real-time image screening in low-resource settings. Beyond healthcare AI, I’ve delivered projects in NLP, clustering, predictive analytics, and big data processing using tools like Apache Spark and Databricks. I'm passionate about applying emerging technologies, including generative AI, natural language processing (NLP), Large Language Models (LLMs), and computer vision, to solve real-world problems and improve decision-making at scale.
Worked with a team of other developers to: -Design responsive components of the system using HTML, CSS, Angular and Tailwind, which contributed to the successful early launch of a new product feature that increased user engagement by over 30%. -Took part in creating a responsive front-end interface for all breakpoints from mobile to desktop, of subsidiary start-up website: Nested Loop Technologies.
Conducted applied machine learning and computer vision research to support early-stage exploration of safety-oriented monitoring systems. Designed and implemented an end-to-end human action recognition pipeline using video data, with a focus on activities relevant to fall detection. Applied pose estimation (OpenPose) to extract skeletal keypoints and constructed temporal pose sequences for spatio-temporal modelling. Developed and evaluated 3D Convolutional Neural Network (3D CNN) models to capture motion patterns across multiple action classes, achieving 76.4% classification accuracy. Analysed system limitations including motion ambiguity, occlusion, and noisy pose data, and documented findings to inform future system feasibility and model improvements. Documented experiments and contributed to knowledge sharing and reproducibility within the team.
Analysed large-scale user interaction datasets to identify behavioural patterns and business optimisation opportunities. Built classification and clustering models to segment users and predict return probability, enabling targeted interventions. Translated model outputs into actionable product and marketing strategies, improving data-driven decision-making. Contributed to retention initiatives that increased returning user activity by 15% within six months. Communicated complex analytical findings to non-technical stakeholders, driving adoption of data solutions.
Developed production-ready frontend features using Angular, HTML, and Tailwind CSS. Integrated frontend components with backend APIs. Used Git and GitHub for version control, branch management, pull requests, and collaborative code reviews. Supported feature rollouts that contributed to a 10% increase in user engagement.
- Designed Fliers, Certificates, Complementary Cards, Office Door tags for staff and students etc. - Worked in collaboration with the departments to design student handbooks for Accreditation and student use. - Collaborated with the convocation committee to design event tags, graduation award plaques and certificates and other creatives. - Made contributions on how to create advertisement content as part if the marketing team during our weekly standup meetings. - Designed environemental and hygiene warning stickers for the landscape and envirnmental unit.
During my time as a graphic design intern, I learnt how to use CorelDraw, Adobe photoshop, Adobe Illustrator to create design fliers, complementary cards, door tags, certificates and so on.