About Me

  •  ACEMS Research Fellow (Jan. 2019 – Present) — The University of New South Wales
  •  ACEMS Research Fellow (Aug. 2018 – Jan. 2019) [6 Month Contract] — The University of Queensland
  •  PhD Candidate in Statistics (2015-2018) — The University of Queensland.
    • Advisor: Professor Dirk Kroese.
    • Thesis: Advances in Monte Carlo Methodology.

Research Interests

My research, generally speaking, lies at the intersection of computational statistics and probabilistic machine learning. Some topics I am interested include:

  • Inference Algorithms for Bayesian Statistics / Machine Learning
    • Stochastic Variational Inference (Normalizing flows, structured approximations, hybrid approaches with MCMC/SMC)
    • Sequential Monte Carlo (Particle Filters, SMC Samplers,  Sequentially Interacting MCMC)
    • Scalable Markov Chain Monte Carlo (for high dimensional parameter spaces and/or big data)
  • Theory and Applications of Kernelized Stein Discrepencies in Machine Learning
  • Variance Reduction and Unbiased Estimation  in Monte Carlo Simulation
  • Bayesian Deep Learning and Non-parametrics
  • Deep Generative Models (e.g., Variational Autoencoders and Neural Processes)

– At UNSW, I run a fortnightly computational statistics and machine learning reading group. Sign up and come along if you are interested these topics!


31/10/2019 – A preprint of our new paper on extending MCMC methods for large data to complicated time series models is now online.

29/10/2019 – Slides and Jupyter Notebook for my three-hour workshop at this years ACEMS retreat later this month on Automatic Differentiation are now available here!

29/3/2019 – A preprint of our (with Liam Hodgkinson and Fred Roosta) new work on Implicit Langevin algorithms is now online.

-/2/2019 – I am on the program committee / reviewing for the 28th International Joint Conference for Artificial Intelligence (IJCAI2019).

Research Output


Salomone R., Quiroz, M., Kohn, R., Villani, M., and Tran, M.N., (2019), Spectral Subsampling MCMC for Stationary Time Series.  arXiv:1910.13627 

Hodgkinson, L., Salomone, R., and Roosta, F. (2019),  Implicit Langevin Algorithms for Sampling From Log-concave Densities. arxiv:1903.12322 

Salomone, R., South, L.F., Drovandi, C.C., and Kroese, D.P. (2018), Unbiased and Consistent Nested Sampling via Sequential Monte Carlo. arXiv:1805.03924


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, 1-28. [Read Online]

Laub, P.J., Salomone, R., Botev, Z.I. (2019), Monte Carlo estimation of the density of the sum of dependent random variables, Mathematics 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]