I am a computational scientist with a background in civil and geotechnical engineering and extensive experience in developing numerical and statistical methods for subsurface imaging. My work integrates finite element modelling, PDE-constrained inversion, and Bayesian inference to solve complex problems in seepage flow, electrical resistivity, quasi-static electromagnetics, elastic wave propagation, and gravity modelling. Over the past decade, I have developed novel statistical inversion techniques based on Hamiltonian Monte Carlo and SOLA approaches, with an explicit focus on robust interface delineation and efficient uncertainty quantification in subsurface characterisation across geophysical, geotechnical, and agricultural applications.

My current research at the Research School of Earth Sciences, Australian National University, focuses on developing scalable computational frameworks for gravity inversion using data from airborne quantum gravity sensors. I am exploring new mathematical formulations and dimensionality-reduction strategies for large-scale PDE-constrained inverse problems, aimed at improving the robustness, efficiency, and interpretability of geophysical imaging. More broadly, my work seeks to bridge applied mathematics, high-performance computing, and engineering to advance the next generation of inversion algorithms for Earth and infrastructure systems.