Cosmology · Scientific Computing · Problem Solving
I work on problems in cosmology and computational science, with a focus on modelling complex systems, quantifying uncertainty, and extracting meaning from confusing data.
My interests lie in the intersection of cosmology · bayesian statistics · artificial intelligence · machine learning
- Cosmology modelling
- Statistics driven analysis
- Data + inference pipelines
Languages
Familiar
— Bayesian power spectrum estimation with modelling of systematic effects in delay-fringe rate space Gibbs-sampling power spectrum estimator for the EoR (Epoch of Reionization) signal in radio interferometric visibilities. Jointly samples the EoR signal, foreground amplitudes, multiplicative antenna gain systematics, and the EoR delay power spectrum — all within a single self-consistent Bayesian framework.arXiv:2604.26885
— 21 cm – Galaxy Cross-Correlation: Uncertainty Budget Forecasting the detectability of the 21 cm × galaxy cross-power spectrum during the Epoch of Reionization, following the framework of La Plante et al. (2023).
— From ANN to BNN: Inferring reionization parameters using uncertainty-aware emulators of 21-cm summaries (co-author) Replaces point-value ANN emulators with Bayesian neural networks that produce full posterior uncertainty on their predictions, so that emulator uncertainty propagates correctly through the downstream Bayesian inference pipeline. arXiv:2508.13261
Open to working on
- scientific problem solving
- data-intensive modelling
- interdisciplinary research
"Be clearly aware of the stars and infinity on high. Then life seems almost enchanted after all." -Vincent van Gogh