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 (20152018) — The University of Queensland.

 Advisor: Professor Dirk Kroese  Thesis: Advances in Monte Carlo Methodology

For more details, 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 more specifically am interested in novel methodological methods 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
Pipeline
Hodgkinson, L., Salomone, R., and Roosta, F. (2020), The reproducing Stein kernel approach for posthoc 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
Publications
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]
Hodgkinson, L., Salomone,R., and Roosta, F. (2020), Implicit Langevin Algorithms for Sampling From Logconcave 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 lognormals, Annals of Operations Research 280 (1), 1946. [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, 2331.
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
 Monte Carlo Secrets Revealed
 Spectral Subsampling for Stationary Time Series (ICML 2020)
 A Tutorial on Reproducing Stein Kernels
 Slides and Jupyter Notebook for my threehour workshop on Automatic Differentiation.