| Summary
As a researcher and physicist, I tackle data analysis problems in gravitational wave astronomy using statistical and machine learning techniques.
I’ve worked with LIGO and LISA on problems such as detecting unexpected noise sources, quantifying statistical biases, and ML-based approaches to noise removal.
My contributions will help to facilitate proposed tests of general relativity and astrophysics, ensuring they can be performed with minimal compromise.
| Education
PhD in Physics
The University of Auckland, New Zealand
Thesis: Bayesian Tools and Machine Learning Methods for Gravitational Wave Data Analysis of Extreme Mass-ratio Inspirals
The University of Auckland, New Zealand
Thesis: Bayesian Tools and Machine Learning Methods for Gravitational Wave Data Analysis of Extreme Mass-ratio Inspirals
Integated Masters in Physics
Cardiff University, United Kingdom
Thesis: Real-time Classification of Transient Gravitational-Wave Noise Using Convolutional Neural Networks
Cardiff University, United Kingdom
Thesis: Real-time Classification of Transient Gravitational-Wave Noise Using Convolutional Neural Networks
| Experience
Graduate Teaching Assistant (2023 - 2025)
The University of Auckland, New Zealand
Tutored and/or marked courses: Probability and its Applications (STATS 125), Data Analysis (STATS 201/208), Statistical Theory (STATS 210).
Demonstrator (2021 - 2022)
Cardiff University, United Kingdom
Tutored course: Computational Physics (PX3143).
Cardiff University, United Kingdom
Tutored course: Computational Physics (PX3143).