Hello, I'm
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I'm a Machine Learning PhD student at Imperial College London,
working on AI-based personalised tools for healthcare.
My research focuses on developing computational methods
to support the monitoring, prediction, and management of chronic diseases,
with a particular interest in atopic dermatitis.
I am interested in deep learning, computer vision, forecasting models,
and interpretable AI. I aim to build AI models that are accessible
to patients and healthcare professionals.
Alongside my research, I enjoy software development and mentoring students
in mathematics, programming, and science.
I am always happy to connect about AI for healthcare, machine learning,
digital health, and collaborative research opportunities.
Explore My
Dates: 2024 – 2028 (expected)
Developing AI-based personalised tools for atopic dermatitis, with a focus on image severity assessment, severity forecasting, and patient-specific modelling.
My research combines computer vision, predictive machine learning and causal modelling to support personalised and scalable healthcare tools.
2024 – Present
Dates: Oct 2020 – Jun 2024
Grade: First Class Honours
Prize: Best Second Year Group Project
Completed a Master's project in the Tanaka Group on AI-enhanced computer vision for eczema severity prediction.
Developed deep learning models for image-based severity assessment, including prediction and segmentation models.
2020 – 2024
Dates: Oct 2013 – Jun 2020
Distinction: Mention Très Bien
Overall grade: 19/20
Dates: Jun 2023 – Oct 2024
Worked on EczemaNet, a deep learning pipeline for eczema image severity assessment, improving model robustness and performance through skin segmentation, data augmentation, and modular deep learning workflows built with PyTorch Lightning and Hydra.
2023 – 2024
Dates: Jun 2022 – Sept 2022
Worked on the development and evaluation of algorithmic approaches to improve cancer mutation detection accuracy in complex genomic regions, including segmental duplications, using the Illumina DRAGEN Bio-IT Platform.
2022
Dates: Jun 2018 – Jul 2018
Worked with the investment banking IT team on the technical aspects of back-office data workflows, supporting the review of client-facing information and contributing to the planning of an internal application for client services.
2018Browse My Recent
Fine-tune a personal LLM on private chat data to create a deployable “digital twin” that mirrors your personality.
Reinforcement learning in PyTorch, including DQN control and MDP-based maze solving.
A set of machine learning and deep learning projects covering classification, segmentation, and generative models.
A collection of small interactive games built in Python.
Neural network models for high-dimensional visual classification
Explore My
JID Innovations, 6(5), 100513
Review of computational modelling approaches for atopic dermatitis and their potential for personalised and translational research.
Read paper
Presented XemaPred, a machine learning framework for real-time forecasting of eczema severity, with applications to personalised disease monitoring and scalable healthcare prediction.
Poster presentation on real-time eczema severity forecasting using sequential machine learning methods.
Poster available on request
Led a workshop introducing computational approaches in immunology and healthcare, with a focus on how AI and machine learning can support personalised prediction, disease modelling, and treatment strategy development.
Workshop session on computational immunology, AI models, healthcare data, and personalised prediction.
View workshop code
Presented an AI-based framework to estimate patient-specific treatment and environmental effects on eczema severity, supporting more personalised treatment decisions.
Poster presentation on personalised modelling of treatment and environmental effects on eczema severity.
Poster available on requestMore research outputs coming soon...
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