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Dr Massimiliano Tamborrino

I am an Associate Professor at the Department of Statistics at the University of Warwick since December 2022 (and Assistant professor before, December 2019 - November 2022). Before moving to Warwick, I was a University Research Assistant at the Institute for Stochastics, JKU Linz, Austria (2014-2019) and Postdoc at the Department of Mathematical Sciences, University of Copenhagen, where I obtained my PhD in Probability Theory and Statistics under the supervision of Professor Susanne Ditlevsen in 2013.

Preprints and Publications

Preprints and publications are available here. For my latest publications, see also Google ScholarLink opens in a new window and ResearchGateLink opens in a new window

Research Interests

My interest is in the study of stochastic processes (mostly diffusions) and point processes from a modelling, numerical, probabilistic and statistical point of view. In particular, I am interested in the interface between numerics and statistics when considering simulation-based methods applied to problems arising mainly, but not exclusively, in neuroscience, physiology, cognitive psychology and biology.

  • Statistical inference for (fully/partially observed) stochastic processes.
  • Approximate Bayesian Computation (ABC) method.
  • Interface between stochastic numerics and (computational) statistics.
  • (Probabilistics) parallel-in-time (PinT) numerical schemes.
  • Stochastic modelling in neuroscience.
  • Mathematical and computational neuroscience
  • Hitting times (also known as first passage times).
  • Statistical inference for point processes.
  • Dependence measures between point processes.

Supervision

Students interested in working in one of the above topics are encouraged to contact me. A list of possible PhD projects and ongoing/supervised PhD projects, BSc and MSc dissertations is available here.Link opens in a new window

2023-2024 Teaching

Term 2: ST923: Graduate Topics in Computational Stochastics and Machine Learning (in particular, Simulation and inference for Stochastic Processes)