Umberto Picchini

Associate Professor, Mathematical Sciences

I am interested in statistical inference for stochastic modelling, and especially Bayesian computational methods. For example, I am interested in MCMC, sequential Monte Carlo (particle filters) and especially “likelihood-free” methods, such as approximate Bayesian computation (ABC). I have special interest in stochastic modelling (e.g. stochastic differential equations) and applications in biomedicine.

​A more detailed personal webpage is at https://umbertopicchini.github.io/

You can follow me on Twitter at https://twitter.com/uPicchini

Master thesis projects: http://www.math.chalmers.se/Math/Grundutb/thesis/projects_list/

​I am PI for the CHAIR and VR funded project "Deep Learning and Likelihood-Free Bayesian Inference for Intractable Stochastic Models". See a description at https://www.chalmers.se/en/departments/math/research/research-groups/AIMS/Pages/ai-project-5.aspx

​• Ongoing cooperation with Kresten Lindorff-Larsen (University of Copenhagen), Julie Lyng Forman (University of Copenhagen) and PhD student Samuel Wiqvist (Lund University) on the VR funded project “Statistical inference and stochastic modelling of protein folding” (2013-5167). See a description at http://www.maths.lu.se/index.php?id=85411

• Ongoing cooperation with Jes Frellsen (DTU, Copenhagen) and Andrew Golightly (Uni. Newcastle, UK) on the CHAIR and VR funded project "Deep Learning and Likelihood-Free Bayesian Inference for Intractable Stochastic Models". See a description at https://www.chalmers.se/en/departments/math/research/research-groups/AIMS/Pages/ai-project-5.aspx

​• In 2016 have created Bayes Nordics, a list to distribute news on events related to Bayesian analysis in the European nordic countries. You can join and contribute at https://sites.google.com/site/bayesnordics/

Published: Thu 03 Sep 2020.