About Me

  •  Postdoctoral Fellow (Nov. 2020-Present) — Queensland University of Technology, Centre for Data Science
  •  ACEMS Research Fellow (Jan. 2019 -Oct. 2020) — UNSW Sydney
  •  ACEMS Research Fellow (Aug.2018 – Jan. 2019) [Short 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 

For more details, please see my recent CV.


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!).

Research Interests

My research, generally speaking, lies at the intersection of computational statistics and probabilistic machine learning. I am broadly interested in these fields, but specifically I am particularly interested in developing novel methodology and theory relating

  • Inference Algorithms  (e.g., Markov Chain Monte Carlo, Sequential Monte Carlo, and Variational Methods)
  • Kernelized Stein Discrepencies
  • Deep Generative Models (e.g., Normalizing Flows and Variational Autoencoders)
  • Variance Reduction and Unbiased Estimation  in Monte Carlo Simulation

Research Output


Villani, M., Quiroz, M., Kohn, R., and Salomone, R. (2021), Spectral Subsampling MCMC for Stationary Multivariate Time Series. arXiv:2104.02134

Hodgkinson, L., Salomone, R., and Roosta, F. (2021), The reproducing Stein kernel approach for post-hoc corrected sampling. arXiv: 2001.09266

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



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 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]


Selected Presentations