- 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.
- (Online Course in July) I will be teaching the second half of the course “Neural Networks and Related Models” at the (fully online!) AMSI Winter School in July 2021. Here, my portion is the “related models” which will include interesting probabalistic and generative models such as Variational Autoencoders, Normalizing Flows, and Generative Adversarial Networks.
- (Paper Accepted) 2nd June, 2021. The (minor) revision of work Implicit Langevin Algorithms for Sampling From Log-concave Densities has been accepted for publication in the Journal of Machine Learning Research (JMLR) and will appear in the near future.
- (New Paper) 5th April, 2021. Preprint “Spectral Subsampling MCMC for Stationary Multivariate Time Series” is now available. Here, we show how to accelerate Bayesian inference with MCMC on “big data” time series in the multivariate case. This extends upon our ICML paper which focused on the univariate case.
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
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. (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
Hodgkinson, L., Salomone,R., and Roosta, F. (2021), Implicit Langevin Algorithms for Sampling From Log-concave Densities, Journal of Machine Learning Research (JMLR). Accepted for Publication. arXiv:1903.12322
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]