2018-2019

Livieri Giulia

Giulia holds a degree in Mathematics from the University of Padova with a score of 110/110 and attended the “Corso di Alta Formazione in Finanza Matematica” at University of Bologna with the score of 30/30 with laude. Now, she is a postdoctoral researcher at Scuola Normale Superiore, where she obtained her Ph.D. in financial mathematics in October 2017 with the score of 70/70 with laude. Her research focuses on financial econometrics for the modeling of financial markets both at high and low frequency, mean field game and corporate finance. 

English

Tonellotto Nicola

Nicola Tonellotto is researcher at the Information Science and Technologies Institute "A. Faedo" of the National Research Council of Italy. He received his Ph.D. in Information Engineering from the University of Dortmund and the University of Pisa in 2008. His research interests are mainly focused on cloud technologies for data processing and information retrieval infrastructures. He is author of more than 50 papers published on the major conferences and journals of information retrieval and cloud computing.

English

Prencipe Giuseppe

Giuseppe Prencipe is currently Associate Professor at the Department of Computer Science at the University of Pisa. His research interests are mainly on distributed systems, mobile and wearable computing; he has published more than 50 scientific publications on international journals and conference proceedings, and participated in several national and international research projects.

English

Advanced topics in network science

Credits: 
1
Hours: 
12
Area: 
Big Data Mining
Academic Year: 
2022-2023
2021-2022
2020-2021
2019-2020
2018-2019
Description: 

In this course we start from the basic notions of graph theory and self-similar phenomena in order to correctly analyse large socio-economic networks. From this analysis we then proceed in the description of the modelling for various classes of phenomena and to the correct definition of benchmarks through an approach inspired by classical statistical physics.

 

Data Driven Innovation

Credits: 
1
Hours: 
12
Area: 
Big Data for Business
Teachers: 
Piccaluga Andrea
Di Minin Alberto
Tutor: 
Pirri Salvatore
Academic Year: 
2018-2019
Description: 

The module aims to show the main characteristics of the innovation processes in companies and institutions. After some basics of innovation economics, the management of the innovation processes will be presented (role of R&D, Open Innovation, etc.). The module also shows new innovation opportunities available after the last progresses in large scale data acquisition and elaboration, the basics of business models and start-ups. An exercise of business model innovation will try to explore che big data potential in opening new business opportunities.

Big data sources, crowdsourcing, crowdsensing

Credits: 
2
Hours: 
20
Area: 
Big Data Sensing & Procurement
Teachers: 
Cresci Stefano
Tutor: 
Nizzoli Leonardo
Academic Year: 
2019-2020
2018-2019
Description: 

This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.

Big Data Ethics

Credits: 
2
Hours: 
24
Area: 
Big Data Ethics
Teachers: 
Monreale Anna
Ruggieri Salvatore
Comandè Giovanni
Academic Year: 
2019-2020
2018-2019
Description: 

The module aims to introduce ethical and legal notions of privacy, anonymity, transparency and discrimination, also referring the Directives and Regulations of the European Union and their ongoing evolution. Tho module will show Privacy-by-Design models and technologies that are useful to protect the users' rights and that allow the analysis of Big Data without harming the right to the protection of personal data, to transparency and to a fair treatment.

Nizzoli Leonardo

Leonardo Nizzoli was born in Pisa in 1984. After graduating in Physics in 2010, he worked more than 5 years as a researcher in a R&D company devoted to the implementation of marine wave energy converters. In 2016 he attended the Post Graduate Master in Big Data Analytics and Social Mining at the University of Pisa. He spent the training period at Enel Global Thermal Generation, where he realized - in collaboration with Angela Italiano - a data driven machine learning model able to predict thermo-acoustic instabilities in natural gas turbines.

English

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