About Me

  • Research Fellow (11/20-Present) — Queensland University of Technology (QUT), Centre for Data Science
  •  ACEMS Research Fellow (01/19 -10/ 20) — UNSW Sydney
  •  ACEMS Research Fellow (08/18 – 01 /19) [Short Contract] — The University of Queensland
  •  PhD Candidate in Statistics (2015-2018) — The University of Queensland.
      • Thesis: Advances in Monte Carlo Methodology |  Advisor: Professor Dirk Kroese

Research Interests

My research primarily surrounds fundamental aspects of methodology in Probabilistic Machine Learning and related fields.

I am also interested in the application and development of extensions of such advanced methods and techniques with the goal of solving complicated and interesting scientific problems and/or developing advanced artificial intelligence systems!

Some topics I have worked/published/am working on: Monte Carlo Methods, Deep Generative Models, Bayesian Statistics, Variational Inference, Representation Learning, Federated Learning & Privacy Enhancing Technologies, Time Series Analysis, Kernel Methods, Variance Reduction, Likelihood-Free Models, Particle Filters, and Rare-Event Simulation.

To find out a little bit more about some of my interests, feel free to have a look at the course content for my AMSI Winter School 2021 course on Deep Probabalistic Models. 

Research Output


Davies, L., Salomone, R., Sutton, S., and Drovandi, C. (2023), Transport Reversible Jump Proposals. 26th International Conference on Artificial Intelligence and Statistics (AISTATS).  Accepted. [Preprint]

Bon, J.J., Bretherton, A., Buchhorn, K., Cramb, S., Drovandi, C., Hassan, C., Jenner, A., Mayfield, H.J., McGree, J.M., Mengersen, K., Price, A., Salomone, R. (corresponding author), Santos-Fernández, E., Vercelloni, E., and Wang, X. (2023), Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statisticsPhilosophical Transactions of the Royal Society A: Mathematical, Physical, and Engineering Sciences.  Accepted. [Preprint]

Villani, M., Quiroz, M., Kohn, R., and Salomone, R. (2022), Spectral Subsampling MCMC for Stationary Multivariate Time Series with an Application to Vector ARTFIMA Processes. Econometrics and Statistics. [Read Online]

Sutton, M. , Salomone, R., Chevallier, A., and Fearnhead, P. (2022), Continuously-Tempered PDMP Samplers.  Neural Information Processing Systems (NeuRIPS), 2022.   [Preprint]

Hodgkinson, L., Salomone,R., and Roosta, F. (2021),  Implicit Langevin Algorithms for Sampling From Log-concave Densities, Journal of Machine Learning Research (JMLR) 22: 1-30. [Read Online]

Salomone R., Quiroz, M., Kohn, R., Villani, M., and Tran, M.N. (2020), Spectral Subsampling MCMC for  Stationary Time Series,  Proceedings of the International Conference on Machine Learning (ICML) 2020.  [Read Online]

Botev, Z.I., Salomone, R., Mackinlay, D. (2019), Fast and accurate computation of the distribution of sums of dependent log-normals,  Annals of Operations Research 280 (1), 19-46.  [Read Online]

Laub, P.J., Salomone, R., Botev, Z.I. (2019), Monte Carlo estimation of the density of the sum of dependent random variablesMathematics and Computers in Simulation 161, 23-31.

Salomone, R., Vaisman, R., and Kroese, D.P. (2016). Estimating the Number of Vertices in Convex Polytopes. Proceedings of the Annual International Conference on Operations Research and Statistics, ORS 2016.  [Read Online]


Hassan, C., Salomone, R., and Mengersen, K., Federated Variational Inference Methods for Structured Latent Variable Models. [Preprint Available Soon]

Salomone, R., Yu, X., Nott, D., and Kohn, R., Structured Variational Approximations with Skew Normal Decomposable Graphical Models.  [pdf]

Hodgkinson, L., Salomone, R., and Roosta, F., The reproducing Stein kernel approach for post-hoc corrected sampling. arXiv: 2001.09266 (Under Revision)

Salomone, R., South, L.F., Drovandi, C.C., and Kroese, D.P. , Unbiased and Consistent Nested Sampling via Sequential Monte Carlo. arXiv:1805.03924 (Under Revision)

Wang, X., Jenner, A.L., Salomone, R., Drovandi, C. , Calibration of a Voronoi cell-based model for tumour growth using approximate Bayesian computation. [bioarXiv] (Under Revision)

Selected Presentations

If you enjoyed this talk, be sure to check out my course materials for my AMSI Winter School 2021 course on Deep Probabalistic Models (the above talk is a simplified version of the first quarter!).