Dr Matthew Sutton

ARC DECRA Research Fellow

Mathematics

m.sutton2@uq.edu.au

Priestley Building (67), Room 618

Researcher biography

Matt Sutton is a statistician and Bayesian computation researcher specialising in advanced Monte Carlo methods for complex and high-dimensional models. Currently a lecturer in mathematics and statistics at the University of Queensland, he develops new approaches to scalable Bayesian inference through piecewise deterministic Markov processes (PDMPs) and related non-reversible algorithms.

He holds an ARC DECRA fellowship on Scalable Bayesian inference for secure and reliable decision making and is a Chief Investigator on the ARC Discovery Project Fixing the holes in Bayesian model comparison. His research focuses on enhancing the efficiency and reliability of simulation-based inference by leveraging continuous-time dynamics, gradient-driven sampling, and robust model comparison methods for modern Bayesian computation.

Publications

Book Chapter (1)

Journal Articles (11)

Conference Papers (3)

Book Chapter

Sutton, Matthew (2020). Bayesian variable selection. Case studies in applied Bayesian data science: CIRM Jean-Morlet Chair, Fall 2018. (pp. 121-135) edited by Kerrie L. Mengersen, Pierre Pudlo and Christian P. Robert. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-42553-1_5

Journal Articles

Corenflos, Adrien, Sutton, Matthew and Chopin, Nicolas (2025). Debiasing piecewise deterministic Markov process samplers using couplings. Scandinavian Journal of Statistics, 52 (4), 1932-1974. doi: 10.1111/sjos.70015

Davies, L., Ley-Cooper, A. Y., Sutton, M. and Drovandi, C. (2023). Bayesian detectability of induced polarization in airborne electromagnetic data. Geophysical Journal International, 235 (3), 2499-2523. doi: 10.1093/gji/ggad073

Sutton, Matthew and Fearnhead, Paul (2023). Concave-convex PDMP-based sampling. Journal of Computational and Graphical Statistics, 32 (4), 1425-1435. doi: 10.1080/10618600.2023.2203735

Chevallier, Augustin, Fearnhead, Paul and Sutton, Matthew (2022). Reversible jump PDMP samplers for variable selection. Journal of the American Statistical Association, 118 (544), 2915-2927. doi: 10.1080/01621459.2022.2099402

Sutton, Matthew, Sugier, Pierre-Emmanuel, Truong, Therese and Liquet, Benoit (2022). Leveraging pleiotropic association using sparse group variable selection in genomics data. BMC Medical Research Methodology, 22 (1) 9. doi: 10.1186/s12874-021-01491-8

Lafaye de Micheaux, Pierre, Liquet, Benoît and Sutton, Matthew (2019). PLS for Big Data: A unified parallel algorithm for regularised group PLS. Statistics Surveys, 13, 119-149. doi: 10.1214/19-ss125

Sutton, Matthew, Mengersen, Kerrie and Liquet, Benoit (2018). [HDDA] sparse subspace constrained partial least squares. Journal of Statistical Computation and Simulation, 89 (6), 1005-1019. doi: 10.1080/00949655.2018.1555830

Sutton, Matthew, Thiébaut, Rodolphe and Liquet, Benoît (2018). Sparse partial least squares with group and subgroup structure. Statistics in Medicine, 37 (23), 3338-3356. doi: 10.1002/sim.7821

Liquet, Benoit, Mengersen, Kerrie L., Pettitt, Anthony N. and Sutton, Matthew (2017). Bayesian variable selection regression of multivariate responses for group data. Bayesian Analysis, 12 (4), 1039-1067. doi: 10.1214/17-ba1081

Billington, Elizabeth J., Khodkar, Abdollah, Petrusma, Dylan and Sutton, Matthew (2014). Lambda-fold theta graphs: Metamorphosis into 6-cycles. AKCE International Journal of Graphs and Combinatorics, 11 (1), 81-94.

Sutton, Matthew William (2014). On the metamorphosis of a G-design into a (G - e) -design. Discrete Mathematics, 318 (1), 71-77. doi: 10.1016/j.disc.2013.11.014

Conference Papers

Srinivasan, Narayan, Sutton, Matthew, Drovandi, Christopher and South, Leah F. (2025). The polynomial Stein discrepancy for assessing moment convergence. International Conference on Machine Learning (ICML 2025), Vancouver, Canada, 13–19 July 2025. San Diego, CA, United States: ICML.

Davies, Laurence, Salomone, Robert, Sutton, Matthew and Drovandi, Christopher (2023). Transport Reversible Jump Proposals. 26th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 25 - 27 April 2023. United States: ML Research Press.

Sutton, Matthew, Salomone, Robert, Chevallier, Augustin and Fearnhead, Paul (2022). Continuously-Tempered PDMP samplers. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA United States, 28 November - 9 December 2022.