- Programming Languages: Python, Java, SQL
- Data Containers: pandas, h5py
- ML frameworks: scikit-learn, PyTorch, pytorch-lightning, Pyro
- 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)
- Freelance Data Analyst, TELUS (Aug 2022 - Aug 2022)
- Research Intern, Particle Physics group, University of Birmingham (Summer 18' and 19')
Bayesian Neural Networks for Predicting the Time Evolution of Supermassive Black Hole Binaries (Ongoing)
- International workshop on diffusions in machine learning: foundations, generative models, and optimisation, participant
My astrophysical interests include: N-body simulations, Supermassive black holes and stochastic gravitational wave background. In tandem, I'm very much interested in SciML, and how it can be applied to astrophysics to build interpretable surrogate models from simulations. The current techniques that I'm particularly fond of are: Bayesian Deep Learning, Physics-informed Neural Networks, Neural ODEs and Variational inference.
- I've also enjoy experimenting with foundation models on HuggingFace. Recently experimented with RAG using Cohere API + LlamaIndex + LangChain.
- I'm currently trying to pick up Julia so that I can use their SciML ecosystem.
- bash + iTerm2 + Oh-My-Bash
- VS Code + essential Python extensions for structured Python projects
- Jupyter notebooks for experimenting in Python/Julia
- Cookiecutter for generating project templates
- I tend to follow the PEP 8 Style Guide and the Zen of Python