PhD Candidate | Astrophysics, Statistics & Data Science
I am a Ph.D. student working in astrophysics at the School of Physics & Astronomy at Monash University, Australia. I'm working on a state-of-the-art method for accelerated Gaussian processes to model spectrospatial data, allowing for accurate recovery of astrophysical quantities of interest. Broadly, my work combines data analysis methods, machine learning, and astrophysics.
I have worked on projects in astrophysics spanning orders of magnitude in wavelength, including radio, optical and x-ray. During my PhD, my research has focused on modelling nebular line emission in the interstellar medium to improve our understanding of the mechanisms driving energy and angular momentum transport through the Milky Way, as well as protoplanetary disc kinematics as a tool for detecting newly-formed planets. For these, I used observations from the Local Volume Mapper and the ALMA observatory. Both projects are part of larger, collaborative efforts—an essential aspect of modern astronomy—and so I am a member of the Sloan Digital Sky Survey V and the exoALMA collaboration.
- Programming: Python (JAX, NumPy, SciPy, Matplotlib, Scikit-learn), Julia, Fortran
- Machine Learning & Statistics: Gaussian processes, Bayesian inference, probabilistic programming (NumPyro, Stan, PyMC, Turing), linear models, non-parametrics, high-dimensional non-linear and hierarchical models
- Computational Methods: Accelerated and high perfomance computing, computational linear algebra, matrix-free methods, Fast Fourier Transforms, auto-differentiation, optimisation