Astrophysics/Machine Learning PhD Researcher

I’m a PhD student at the University of Surrey.
I develop machine learning surrogate models to predict the dynamical evolution of astrophysical systems, with a focus on supermassive binary black holes in galactic mergers. My work combines graph neural networks, neural ODEs, and symbolic regression to build interpretable, scalable models for spatiotemporal systems.

Technical Skills

• Programming Languages: Python, SQL
• Machine Learning Frameworks: PyTorch, PyTorch Lightning, PyTorch Geometric, scikit-learn, Pyro, Hugging Face Transformers
• Scientific Computing & Data Handling: pandas, NumPy, SciPy, h5py, Jupyter
• Workflow & Experimentation: Git, Bash, Weights & Biases

Education

• PhD Astrophysics, University of Surrey (Sept 2023 - Current)
• MSc. Computer Science, Swansea University (2021 - 2022)
• MSci. Physics with Particle Physics and Cosmology, University of Birmingham (2016 - 2020)

Work Experience

• Freelance Data Analyst, TELUS (Aug 2022 - Aug 2022)
• Research Intern, Particle Physics group, University of Birmingham (Summer 18’ and 19’)

Current Projects

Machine Learning for Predicting the Time Evolution of Supermassive Black Hole Binaries_ (Ongoing)

Workshops

• International workshop on diffusions in machine learning: foundations, generative models, and optimisation, participant