CV

Education

Programming Experience:
- Machine learning and differentiable programming
- Numerical methods and simulation techniques (Agent based models, monte-carlo methods)
- Graph optimisation
- Image processing
- GPU and MPI parallelisation
Software and Tools:
- Python (numpy, scipy, matplotlib, tensorflow, JAX, pandas)
- Bash
- ImageJ
- C, C++, MATLAB, Cuda
Mathematics:
- Partial differential equations and dynamical systems
- Statistical mechanics and probability theory
- Mathematical modelling

Research:
- Modelling spatio-temporal pattern formation with Neural Cellular Automata (see Projects page).
- Predicting hospital ICU stay times with patient demographics and disease co-occurrences via graph clustering algorithms.
- Validating the performance of wind turbines after modifications, given noisy/unreliable direct measurement.
- Ongoing work exploring how to fit reaction-diffusion-advection equations to biological data.
Other experience:
- I run a weekly reading group for scientific applications of machine learning, which involves organising/giving talks, and discussing papers as a group.
- Attended a wide range of conferences (see Conferences page), sometimes presenting my work, which has led to ongoing collaborations.
- Experience presenting technical work in a clear and understandable manner.
- Experience working in a team and independently, balancing the time commitments of several concurrent projects.

Summer 2019 - Research assistant
- Assisted a PhD student with research in frustrated anti-ferromagnets
- Developed C++ code to simulate the classical heisenberg model
2017 - 2020 - Maths and Physics tutor
- Part time tutoring of high school level maths and physics
2021 - ongoing - Teaching assistant
- Teaching assistant for undergraduate scientific programming courses, giving me experience in debugging other people’s code and explaining technical concepts to those who don’t understand them.