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.


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