[Forum SIS] 2nd Seminar "D2 Seminar Series" - Florence Center for Data Science

[datascience a unifi.it]

Dear all,

The Florence Center for Data Science is happy to present the second 
Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will 
be held online Friday 4th of June 2021, from 2-3.30 pm.
The seminar will be held by Assistant Professor Cesare Bracco, PhD from 
the Department of Mathematics and Computer Science “Ulisse Dini” and 
Professor Leonardo Boncinelli from the Department of Economics and 
Management of the University of Florence (see below the abstracts).

Register in advance for this webinar (free of charge):
https://us02web.zoom.us/webinar/register/WN_mTISDxUKSMuDqMj-QuKi4w

After registering, you will receive a confirmation email containing 
information about joining the webinar.

We hope to see you there! You are invited to invite also your students, 
PhDs and colleagues who may be interested in the Seminar (you find a 
Flyer with all the info attached).

Kind Regards,
Florence Center for Data Science

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Webinar Speakers

Cesare Bracco (Assistant Professor @Department of Mathematics and 
Computer Science “Ulisse Dini”, University of Florence)
Title: Scattered data: surface reconstruction and fault detection
(joint work with O. Davydov, C. Giannelli, D. Großmann, S. Imperatore, 
D. Mokriš, A. Sestini)

Abstract: We will consider two aspects concerning scattered data 
approximation. The first is reconstructing a (parametrized) surface from 
a set of scattered points: the lack of structure in the data requires 
approximation methods that automatically adapt to the distribution and 
shape of the data themselves. We will discuss an effective approach to 
this issue based on hierarchical spline spaces, which can be locally 
refined, and therefore naturally lead to adaptive algorithms. The 
reconstruction problem contains another interesting problem: detecting 
the discontinuities the surface may have in order to reproduce them. 
Finding the discontinuity curves, usually called faults (or gradient 
faults when gradient discontinuities are considered), is actually an 
important issue in itself, with several applications, for example in 
image processing and geophysics. I will present a method to determine 
which points in the scattered data set lie close to a (gradient) fault, 
based on indicators obtained by using numerical differentiation 
formulas.

Leonardo Boncinelli (Professor @Department of Economics and Management, 
University of Florence)
Title: Game-based education promotes sustainable water use

Abstract: In this study, we estimate the impact of a game-based 
educational program aimed at promoting sustainable water usage among 
2nd-4th grade students and their families living in the municipality of 
Lucca, Italy. To this purpose, we exploited unique data from a 
quasi-experiment involving about two thousand students, one thousand 
participating (the treatment group), and one thousand not participating 
(the control group) in the program. Data were collected by means of a 
survey that we specifically designed and implemented for collecting 
students' self-reported behaviours. Our estimates indicate that the 
program has been successful: the students in the program reported an 
increase in efficient water usage and an increase in the frequency of 
discussions with their parents about water usage; moreover, positive 
effects were still observed after six months. Our findings suggest that 
game-based educational programs can be an effective instrument to 
promote sustainable water consumption behaviors in children and their 
parents.
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