Area of Expertise:
sparse simulation-based inference, multi-level modelling, robust statistics

Short bio: Paola Stolfi is a post-doc at the Institute of Applied Mathematics of the National Research Council of Italy. She graduated in Pure Mathematics and received her Ph.D. in Statistical Methods at Roma Tre University. She is an applied mathematician who, working on several research projects ranging from energy to computational biology, specialized in multi-level mathematical and statistical models, machine learning algorithms and on simulation-based statistical inference. She is currently involved in an international project related to antimicrobial resistance surveillance activities where she is developing a model to temporally predict the spread of resistance.


Sparse methods for high-dimensional data:* Short introduction to sparse methods* Lasso regression with applications to financial/energy data* Lasso logistic regression with applications to DNA sequences/diseases identification* Graphical Lasso and applications to gene expression and portfolio selection.