Bio
I am a PhD student at the University of Bristol, generously funded by Compass (EPSRC Centre for Doctoral Training in Computational Statistics and Data Science). I am currently supervised by Dr. Song Liu and Professor Mark Beaumont.
I am a statistician at heart, interested in how we can best extract information from data. My research is in Bayesian learning and centres on simulation-based inference, which is a class of methods for fitting models using only simulations from them. I am also broadly interested in generative modelling, which fundamentally comes down to the same problem of learning a probability distribution from data.
Before my PhD, I completed an MSc in Statistics at the University of Warwick (Distinction) and a BSc in Mathematics and Economics at the London School of Economics (First Class Honours).
Selected publications
Khoo, S.*, Liu, S.* & Beaumont, M. (2026). Local Manifold Identification with Latent Linear Models and OT Flows. NeurIPS 2026. Poster
Khoo, S., Prangle, D., Liu, S. & Beaumont, M. (2026). Minimum Distance Summaries for Robust Neural Posterior Estimation. ICML 2026. Poster [arXiv]
Khoo, S., Wang, Y., Liu, S. & Beaumont, M. (2025). Direct Fisher Score Estimation for Likelihood Maximization. NeurIPS 2025. Spotlight, top 3% [arXiv]
Pacchiardi, L., Khoo, S. & Dutta, R. (2024). Generalized Bayesian Likelihood-Free Inference. Electronic Journal of Statistics, 18(2), 3628–3686. [Paper]