Dr Matteo Giordano

Postdoctoral Researcher

About Me

  • 2017 - 2021: Ph.D. in Mathematical Statistics, University of Cambridge
  • ​2015 - 2017: M.Sc. in Mathematics, University of Torino, Italy
  • 2015 - 2017: Master in Statistics and Applied Mathematics, Collegio Carlo Alberto, Torino, Italy
  • 2012 - 2015: B.Sc. in Mathematics for Finance and Insurance, University of Torino, Italy

Research Interests

My main research interests range across the theory and methodology of Bayesian procedures in complex high- and infinite-dimensional statistical models. My current research areas are:

  • frequentist analysis of Bayesian nonparametric procedures
  • statistical inverse problems
  • inference for diffusion processes
  • inference for counting processes
  • inference on manifolds

Publications

Giordano, M. (2022) “Besov priors in density estimation: optimal posterior contraction rates and adaptation.”
Giordano, M., Ray, K. and Schmidt-Hieber, J. (2022) “On the inability of Gaussian process regression to optimally learn compositional functions.”
Giordano, M. and Ray, K. (2020) “Nonparametric Bayesian inference for reversible multi-dimensional diffusions.”
Giordano, M. and Nickl, R. (2019) “Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem.”
Giordano, M., Blasi, P. and Ruggiero, M. (2019) “A reversible allelic partition process and Pitman sampling formula.”
Giordano, M. and Kekkonen, H. (2018) “Bernstein-von Mises theorems and uncertainty quantification for linear inverse problems.”

Contact Details

Email: matteo.giordano [at] stats.ox.ac.uk

Office: G.03

Pronouns: He/Him

Contact me about the  Bayesian Reading Group.