Rhys Shaw

Mentored undergraduates across five 4-week physics research projects, marking reports and conducting over 100 interviews, while earning direct student commendations for effective and fair technical guidance.

Co-lead of an open group for PhD students and academics focused on research software development, facilitating weekly discussions and delivering tutorials on topics including parallelisation, self-hosting, Git, and Docker.

Committee member for a student-led hackathon providing data science support to non-profits and charities, involved in event organisation, technical feasibility assessment, and on-site technical support.

Developed a full-stack internal LLM client using FastAPI, HTMX, and MongoDB, capable of querying multiple APIs while securely managing local data storage and network document integration.

Activities and societies: Given numerous talks and a seminar on my work both within the university and at external conferences.

PhD in Physics with thesis on machine learning and analysis techniques for extra-galactic astronomy. Research focuses on developing and applying machine learning to analyze large astronomical datasets, including building novel software for source detection and galaxy image analysis for the Euclid Space Telescope.

Completed the Fundamentals of Accelerated Computing with Modern CUDA C++ course.

MSc in Data Intensive Astrophysics with thesis on the effect of fractal substructure in early stellar cluster evolution. Utilized N-body simulations to model dynamical evolution of clusters with varying fractal substructure.

BSc in Physics with Astrophysics, thesis on exploring the bias in X-ray and SZ selected galaxy clusters using scaling relations. Analyzed scaling relations to understand selection effects in galaxy cluster surveys.