This is the personal page of Umberto Picchini, an Associate Professor in Mathematical Statistics at the Department of Mathematical Sciences at Chalmers University of Technology and University of Gothenburg, Sweden. Here is my official university profile. I am also a faculty member of the Chalmers AI Research Centre.
Let’s get in touch to talk about statistical inference (especially Bayesian), likelihood-free methods for models with intractable likelihoods, and Monte Carlo statistical methods such as MCMC and sequential Monte Carlo, and the application of statistical inference in applied problems.
Interested in Bayesian methods? Check out this page!
News
- Sept 2024: accepted in Bayesian Analysis, Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations.
- June 2024: accepted in Transactions on Machine Learning Research, Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings.
- May 2024: accepted in Bayesian Analysis, Guided sequential ABC schemes for intractable Bayesian models.
- March 2024: New paper: Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings.
- Oct 2023: New paper: Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations.
- July 2023: Accepted in Statistics in Medicine, Statistical modeling of diabetic neuropathy: Exploring the dynamics of nerve mortality.
- June 2023: NORDSTAT 2023 was a success! 300 participants, 180 talks and 30 posters. A great pleasure to have been (very much) involved with its organization! Here is a summary article.
- Feb 2023: New paper: JANA: jointly amortized neural approximation of complex Bayesian models.
- Feb 2023: New paper: Mathematical modeling of nerve mortality caused by diabetic neuropathy.