Pedreschi Dino

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Dino Pedreschi is a Professor of Computer Science at the University of Pisa, and a pioneering scientist in mobility data mining, social network mining and privacy-preserving data mining. He co-leads with Fosca Giannotti the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and the Information Science and Technology Institute of the Italian National Research Council, one of the earliest research lab centered on data mining. His research focus is on big data analytics and mining and their impact on society. He is a founder of the Business Informatics MSc program at Univ. Pisa, a course targeted at the education of interdisciplinary data scientists. Dino has been a visiting scientist at Barabasi Lab (Center for Complex Network Research) of Northeastern University, Boston (2009-2010), and earlier at the University of Texas at Austin (1989-90), at CWI Amsterdam (1993) and at UCLA (1995). In 2009, Dino received a Google Research Award for his research on privacy-preserving data mining.
Topics: 
Big Data Analytics
Social Network Analysis and Mining
Analysis of Human Mobility
Privacy-by-Design and Ethical Data Mining
Nowcasting of Socio-Economic Indicators
Complex Network Dynamics
Data Mining and Knowledge Discovery
Privacy Preserving Data Mining
Constraint-Based Pattern Discovery
Data Mining Query Language
Market Basket Analysis
Fraud Detection
Logic in Databases
Non Monotonic Non Deterministic Temporal Reasoning
Deductive and Object-Oriented Databases
Formal Methods
Logic Programming
2009 Google Research Award on Privacy
2017 University of Pisa Ordine del Cherubino
1989-1990 Visiting scientist at University of Texas, Austin
1993 Visiting scientist at CWI Amsterdam
1995 Visiting scientist at UCLA
2009-2010 Visiting scientist at Barabasi Lab (Center for Complex Network Research) of Northeastern University, Boston
2004 Co-Chair of ECML/PKDD
2005 Vice-Chair of ICDM
2014 Vice-Chair of ICDE
1987 Ph.D. in Computer Science at University of Pisa
Position
Full Professor
Affiliation
Department of Computer Science, University of Pisa (DI-UNIPI)
Room
318
Phone
+39 050 221 2752
Email
Homepage
Google Scholar
Dino Pedreschi on Google Scholar
DBLP
Dino Pedreschi on DBLP
Scopus
Dino Pedreschi on Scopus
ArnetMiner
Dino Pedreschi on ArnetMiner
Twitter
Dino Pedreschi on Twitter

Publications

2019

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Blog

Il KDD-Lab, laboratorio congiunto tra Cnr e Università di Pisa, insieme con Windtre, l’Istituto Superiore di Sanità, la Fondazione Bruno Kessler e altri centri di ricerca italiani ed internazionali, ha analizzato la relazione tra la mobilità dei c

Il grande biologo e paleontologo Stephen Jay Gould in Intelligenza e pregiudizio (The Mismeasure of Man, 1981) criticò la pretesa di ridurre l’intelligenza a un’entità da misurare. Cosa significa misurare l’intelligenza?

The Council of Europe and the University of Strasbourg organised the 5th edition of the "AI Breakfast" in the form of a webinar on 16 April 2020.

Yesterday the Italian Ministry for Technological Innovation and Digitization has established a new Task Force for the Covid-19 Emergency.

La polarizzazione, spiega il professor Dino Pedreschi dell'Università di Pisa, è un fenomeno che esiste già da prima del digitale. Ci piace maledettamente avere ragione ed essere esposti a opinioni che rafforzano la nostra opinione.

Dino Pedreschi has been interviewed for the Workshops to design a Human Centered AI roadmap in Europe, in the context of the Humane-AI project.

Sul piano delle risorse umane legate all’intelligenza artificiale si dovrebbe seguire l’esempio olandese e tedesco e lanciare un piano straordinario di reclutamento di professori e ricercatori specializzati.

The ERC Advanced Grant XAI “Science & technology for the eXplanation of AI decision making”, led by Fosca Giannotti of the Italian CNR, in collaboration with the PhD program in “Data Science” by Scuola Normale Superiore in Pisa, invites applic

ISTI News July 2019

On the fifth issue of the ISTI News newsletter you'll find the project Track&Know, the papers "A survey of methods for explaining black box models" by Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri,

L'Intelligenza Artificiale che conosciamo oggi è basata su una precisa tecnologia: Big Data + Machine Learning, cioè i dati uniti all'apprendimento automatico.

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