marcomol

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Academic interests

  • High-dimensional statistical models
  • Bayesian Inference

Background

I obtained my PhD in Statistics at UCL (UK) in 2020, with specialisation in Bayesian graphical models with applications to metabolomics data.

Between January 2020 and July 2023 I worked as a Machine Learning Engineer in a CRO company in London, where I gained experience in advanced machine learning modelling and software development.

Publications

  • Marco Molinari, Magne Thoresen. A computationally efficient approach to false discovery rate control and power maximisation via randomisation and mirror statistic. Statistical Methods in Medical Research. 2025;0(0). doi:10.1177/09622802251329768
  • Marco Molinari, Andrea Cremaschi, Maria De Iorio, Nishi Chaturvedi, Alun Hughes & Therese Tillin (2022) Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases, Journal of Applied Statistics, DOI: 10.1080/02664763.2022.2116746
  • Marco Molinari, Andrea Cremaschi, Maria De Iorio, Nishi Chaturvedi, Alun D. Hughes, Therese Tillin, Bayesian Nonparametric Modelling of Multiple Graphs with an Application to Ethnic Metabolic Differences, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 71, Issue 5, November 2022, Pages 1181–1204, https://doi.org/10.1111/rssc.12570
  • Molinari, Marco, de Iorio, Maria, Chaturvedi, Nishi, Hughes, Alun and Tillin, Therese. "Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study" The International Journal of Biostatistics, vol. 17, no. 1, 2021, pp. 153-164. https://doi.org/10.1515/ijb-2019-0108
Published Nov. 20, 2023 2:12 PM - Last modified Feb. 17, 2026 4:53 PM

Research groups