Summer School Primer on Data Science 2019 - Uncertainty quantification and applications

Aim
Primer on Data Science is a serie of summer schools organized by the curriculum Mathematics and Statistics for Life and Social Sciences of the Laurea Magistrale in Mathematics (Department of Mathematics, University of Trento), to the aim of introducing third year bachelor students and bachelor graduates to the topics of this curriculum. Every year the school will have a different topic.

The 2019 edition will focus on a gentle introduction to some aspects of Uncertainty quantification and applications.

Where
All the activities are in room A102 of Polo Scientifico e Tecnologico “Fabio Ferrari”, Povo 1, see http://datascience.maths.unitn.it/events/pds2019/

Admission
Students
The school is open to 30 participants, no fees are required, but registration is mandatory. Everybody is welcome to apply, however, admission will be based on the following criteria in order of importance

Bachelor graduates and third year bachelor students in Mathematics
Transcript of Records and grades
Students from University of Trento
Lectures will be delivered in English.

Professionals
PDS2019 is also open to professionals and companies: for further information (fee, registration form etc) please contact Francesca Stanca.

Participation in the school includes
Access to the material: notes, slides, videos of the courses
Coffee breaks
Access to the university canteen (lunch time)

Teachers

• Lorenzo Tamellini (CNR-IMATI, Pavia, Italy)

Lorenzo Tamellini is a researcher at CNR-IMATI, Pavia (Italy). His research topics are numerical methods for PDEs (in particular, Isogeometric Analysis) and Uncertainty Quantification (UQ). In details, he works on polynomial approximation methods for UQ (mostly stochastic collocation on sparse grids and multi-index stochastic collocation), with applications to groundwater flows and geochemical compaction problems, for both direct and inverse UQ problems. He maintains the Matlab library Sparse Grids Matlab kit. More details can be found at arturo.imati.cnr.it/tamellini/

• Marco Broccardo (Swiss Seismological Service, ETH Zürich, Switzerland)

Marco Broccardo is a Senior Researcher and Lecturer at the Swiss Seismological Service (SED), at ETH Zürich (Switzerland). His research interests focus on developing computational probabilistic and statistical tools for system reliability analysis, earthquake engineering, and stochastic dynamics. Currently, he is engaged in three main research projects: i) probabilistic characterization of fluid-induced seismicity (ii), Hamiltonian Monte Carlo for rare event estimations, (iii), probabilistic system resilience analysis. Dr. Marco Broccardo obtained his Ph.D. in the Structural Engineering, Mechanics, and Materials (SEMM) program with designated emphasis in Computational Science at the University of California, Berkeley, where he also completed the minors in Statistics and Computational Mechanics. For more information visit www.marco-broccardo.com

• Roberta Sirovich (Dipartimento di Matematica “Giuseppe Peano”, University of Turin, Turin, Italy)

Roberta Sirovich is Assistant Professor at the University of Torino, Department of Mathematics. Her education is in Mathematics. Her research interests shifted to applied probability and statistics. Her main topics are: 1. stochastic processes as models for observable phenomena (neuronal firing, cancer growth, supply chain, chemical reaction networks, population dynamics), 2. estimation for stochastic processes (regularization methods, asymptotic properties for estimation from diffusion processes), 3. applied statistics (fashion industry data, advanced sport analytics, DNA sequencing data). For more information visit www.robertasirovich.it

Program

http://datascience.maths.unitn.it/events/pds2019/

Organizers
Claudio Agostinelli (claudio.agostinelli [at] unitn.it)
Andrea Pugliese (andrea.pugliese [at] unitn.it)
Alberto Valli (alberto.valli [at] unitn.it)

Information
In case you need more information you can contact Alberto Valli (alberto.valli [at] unitn.it)