- 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, see my recent CV.
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
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. (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 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 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]