About Me

  •  ACEMS Research Fellow (Jan. 2019 – Present) — UNSW Sydney
  •  ACEMS Research Fellow (Aug. 2018 – Jan. 2019) [Short-Term 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

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

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)


2/2020 – A significantly revised version of our work on extending subsampling methods to stationary time series is now available.

1/2020 – Our paper on Reproducing Stein kernels is now available on arXiv.

1/2020 – Our paper on Implicit Langevin algorithms has been accepted to the Journal of Machine Learning Research, with minor revision.

1/2020I am serving on the program committee / reviewing for the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI2020). 

Selected Presentations

A Tutorial on Reproducing Stein Kernels

Slides and Jupyter Notebook for my three-hour workshop on Automatic Differentiation.

Research Output


Hodgkinson, L., Salomone, R., and Roosta, F., (2020), 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


Salomone R., Quiroz, M., Kohn, R., Villani, M., and Tran, M.N., (2019), Spectral Subsampling MCMC for  Stationary Time Series.  Accepted at ICML 2020.  arXiv:1910.13627 

Hodgkinson, L., Salomone,R., and Roosta, F. (2019),  Implicit Langevin Algorithms for Sampling From Log-concave Densities. Accepted at the Journal of Machine Learning Research (JMLR) with Minor Revision. arxiv:1903.12322 

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]