I'm newly graduated from University College Dublin where I achieved my MSc in AI for medicine and medical research. The master focuses on learning ways to utilise AI/ML to improve medicine and medical research, with a hevay emphasis on cancer. My thesis, "Machine Learning Based Biomarker Discovery for Pancreatic Ductal Adenocarcinoma Using Serum Metabolites", analysed serum metabolites found from performing untargeted metabolomics using LC/MS on serum from known pancreatic cancer patients. I built an ML pipeline in Python, from data loading and cleaning through model training and testing to final model results. An excel list of high impact metabolites were extracted and used for pathway analysis. This project highlights my proficiency in applying ML to real-world healthcare challenges, a field I'm deeply passionate about.
My background is a BSc in Computer Science from Swansea University where I built a strong foundation in logic, ML, and software development. In my final year of my bachelor I wrote my dissertation titled "Machine Learning Assessment of Novel Biomarkers for Aggressive Prostate Cancer," which earned a grade of 95% and won me the prize of "Best Dissertation in Computer Science 2024".
Through my years I have developed skills in Python, ML, R, and Git, and I'm continually expanding my knowledge in ML, as well as medicine, through both academic and personal projects. My goal is to use these skills to innovate within the healthcare industry, using ML to advance medical research and improve patient outcomes.
University College Dublin 2025-2026
First Class Honours MSc in AI for Medicine & Medical Research (GPA: 3.91)
Course Highlights:
A research internship in The Molecular Epidemiology of Cancer (MEC) Lab at the Conway institute during 7 months. Project Title: Machine Learning Based Biomarker Discovery for Pancreatic Ductal Adenocarcinoma Using Serum Metabolites.
ML, Medical data, Data exploration, Cancer research, Python, Libraries for ML such as Pandas and SciKit-Learn
A course in utilising AI for personalised medicine with a strong focus on cancer research.
Survival modelling, Cancer research, ML, AI, Multi-omics data, R, ODE models
A course in biological principles with focus on cell organisation, disease mechanisms and omics data. Performed RNASeq and gene expression analyses on data from CBioPortal.
Cell biology, omics data, R, gene expression analysis, RNASeq
Swansea University 2021 - 2024
First Class Honours BSc in Computer Science
Course Highlights:
Data Science, Medical Data, ML prediction, Excel, Prostate Cancer grading, Python + Libraries
Data structures, Algorithms, Complexities
MySQL, Relational databases, ACID properties
Big Data handling, ML algorithms, Pipelines, ETL, Python, SciKit, Feature selection, Analysis
Propositional, Normal Forms, Natural Deduction, Predicate, Gödel