Florence data science

CEN-IBS/GMDS Invited Session on "Causal Inference and Machine Learning"
2020-09-28 15:00 - 17:00 (Europe/Rome)
“Causal Inference and Machine Learning” – a virtual
satellite invited session of the 2020 joint conference of the GMDS &
CEN-IBS – 28 September, 3-5pm (CEST, Berlin time).
More information on the website https://www.eventbrite.de/e/cen-ibsgmds-invited-session-on-causal-inference-and-machine-learning-tickets-116222778459
Note: the event is free, but you need to register via this eventbrite site https://www.eventbrite.de/e/cen-ibsgmds-invited-session-on-causal-inference-and-machine-learning-tickets-116222778459

2020 Applied Bayesian Statistics Summer School: BAYESIAN CAUSAL INFERENCE
2020-06-08 08:00 - 2020-06-12 17:00 (Europe/Rome) | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Applied Bayesian Statistics summer school has been running since 2004. Since 2012 it is organised by:

IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Milano
Dipartimento di Scienze Statistiche Università Cattolica, Milano

The 2020 Applied Bayesian Statistics Summer School (17th edition) will be held in Florence, Italy. The topic chosen for the 2020 school is
Bayesian Causal Inference. The lecturer is Prof. Fan Li (Department of Statistical Science, Duke University, Durham, NC, USA).
Further details are provided on the conference webpage.
Local contributing organization:
Florence Center for Data Science (FDS)
Department of Statistics, Computer Science, Applications (DISIA), University of Florence

DISIA Seminar: From logit to linear regression and back
2020-02-19 12:00 - 13:30 (Europe/Rome)
Title: From logit to linear regression and back
Speaker: Giovanni Maria Marchetti (University of Florence)
Location: Aula 32 – DISIA – Viale Morgagni 59
Abstract

DSG Seminar: The Future of Artificial Intelligence and Fundamental Rights
2020-02-18 17:00 - 19:30 (Europe/Rome) | D4 Via delle Pandette 9, Via delle Pandette 9, Firenze, Italy
Title: The Future of Artificial Intelligence and Fundamental Rights
Speaker: Marc Rotenberg (Georgetown Law School – EPIC)
Location: Building D4, Room 102
Seminar poster

DISIA Seminar: On Spatial Lag Models estimated using crowdsourcing, web-scraping or other unconventionally collected Big Data
2020-02-18 12:00 - 13:30 (Europe/Rome)
Title: On Spatial Lag Models estimated using crowdsourcing, web-scraping or other unconventionally collected Big Data
Speaker: Giuseppe Arbia (Catholic University of the Sacred Heart Milan)
Location: Aula 32 – DISIA – Viale Morgagni 59
Abstract

Adversarial Machine Learning
2020-01-29 11:30 - 12:30 (Europe/Rome) | Aula Caminetto, Via di Santa Marta 3, Firenze, Italy
Speaker: Prof. Fabio Roli, Università di Cagliari
Machine learning algorithms are widely used for cybersecurity
applications, including spam, malware detection, biometric recognition.
In these applications, the learning algorithm has to face intelligent
and adaptive attackers who can carefully manipulate data to purposely
subvert the learning process. As machine learning algorithms have not
been originally designed under such premises, they have been shown to be
vulnerable to well-crafted attacks, including test-time evasion and
training-time poisoning attacks (also known as adversarial examples).
This talk aims to introduce the fundamentals of adversarial machine
learning and some techniques to assess the vulnerability of machine
learning algorithms to adversarial attacks. We report application
examples including object recognition in images, biometric identity
recognition, spam and malware detection.

Fabio Roli is a Full Professor of Computer Engineering at the University
of Cagliari, Italy, and Director of the Pattern Recognition and
Applications laboratory (http://pralab.diee.unica.it/). He is partner
and R&D manager of the company Pluribus One that he co-founded
(https://www.pluribus-one.it ). He has been doing research on the design
of pattern recognition and machine learning systems for thirty years.
His current h-index is 66 according to Google Scholar (December 2019).
He has been appointed Fellow of the IEEE and Fellow of the International
Association for Pattern Recognition. He was a member of NATO advisory
panel for Information and Communications Security, NATO Science for
Peace and Security (2008 – 2011).

DISIA hosts a 3-day Stats Camp Seminar
2020-01-20 - 2020-01-22 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Department of Statistics is hosting a Todd D. Little (Texas Tech University) short course:
Title: The Craft of Model Construction Using Structural Equation Modeling
Comprehensive 3-day Stats Camp Seminar by Todd Little (Texas Tech University)

Syllabus of the short course
Online registration
(For DiSIA members, please contact Carla Rampichini)

BIO
Todd D. Little, Ph.D. is a Professor of Educational Psychology at Texas Tech University (TTU) where, in 2013, he became the founding director of the Institute for Measurement, Methodology, Analysis and Policy (IMMAP). The IMMAP at TTU is a University-designated research and support center that provides expert consulting and assistance on all manner of data collection, data management, and advanced statistical analyses. Little is internationally recognized for his quantitative work on various aspects of applied SEM (e.g., indicator selection, parceling, modeling developmental processes) as well as his substantive developmental research (e.g., action-control processes and motivation, coping, and self-regulation). Prior to joining TTU, Little has guided quantitative training and provided consultation to students, staff, and faculty at the Max Planck Institute for Human Development’s Center for Lifespan Studies (1991-1998), Yale University’s Department of Psychology (1998-2002), and researchers at KU (2002-2013, including as director of the RDA unit at the Lifespan Institute and as director of the Center for Research Methods and Data Analysis). In 2001, Little was elected to membership in the Society for Multivariate Experimental Psychology, a restricted-membership society of quantitative specialists in the behavioral and social sciences.
In 2009, he was elected President of APA’s Division 5 (Evaluation, Measurement, and Statistics). He founded, organizes, and teaches in the internationally renowned ‘Stats Camps’ each June (see statscamp.org for details of the summer training programs) and has given over 150 workshops and talks on methodology topics around the world. As an interdisciplinary-oriented collaborator, Little has published with over 280 persons from around the world in over 65 different peer-reviewed journals. His work has garnered over 11,000 citations. He published Longitudinal Structural Equation Modeling in 2013 and he has edited five books related to methodology, including the Oxford Handbook of Quantitative Methods and the Guildford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card). Little has served on numerous grant review panels for federal agencies such as NSF, NIH, and IES, and private foundations such as the Jacobs Foundation. He has been the principal investigator or co-principal investigator on over 15 grants and contracts and he has served as a statistical consultant on over 70 grants and contracts. In the conduct of his collaborative research, he has participated in the development of over 12 different measurement tools, including the CAMI, the Multi-CAM, the BALES, the BISC, the I FEEL, and the form/function decomposition of aggression.

Self-Organizing Migrating Algorithm - Progress, Innovations and Applications
2020-01-16 15:00 - 16:00 (Europe/Rome) | Sala riunioni DINFO, Via di Santa Marta 3, Firenze, Italy
Speaker: Prof. Ivan Zelinka
Location: Sala riunioni DINFO – Via di S. Marta 3
This seminar discusses recent advances, progress and application on SOMA algorithm (http://somaalgorithm.com), that has been developed in the year 2000 and is based on swarm intelligence principles. During its existence it has been published twice in a book form, numerous journals and was a subject of a few keynotes and tutorials. Recent improvements of this algorithms, published on Congress on Evolutionary Algorithms, GECCO and SEMCCO have set up its performance on the same level as the differential evolution is (actually on the 3rd place amongst the 34 state-of-art algorithms of global optimization like PSO, DE, ABC, FireFly,…). In the seminar will be audience introduced to its latest improvements and modification, testing on representative test functions as well as applications of the SOMA. Selected applications will be from domain of chaos control, real-time control of the plasma reactor, symbolic regression – program synthesis, swarm robot control, design of electronic circuits, neural network synthesis, computer games and astrophysical data processing amongst the others. The seminar is designed as an introduction into SOMA, its structure, results, and content are based on our previous research, journal publications, keynotes, and tutorials and reflect the latest progress in that field.

Recent Advances and Progress in Evolutionary Algorithms and its Dynamics
2020-01-14 15:00 - 16:00 (Europe/Rome) | Sala riunioni DINFO, Via di Santa Marta 3, Firenze, Italy
Title: Recent Advances and Progress in Evolutionary Algorithms and its Dynamics
Speaker: Prof. Ivan Zelinka
Location: Sala riunioni DINFO

This seminar is focused on recent progress in the mutual intersection of a few exciting fields of research whose core topic is evolutionary algorithms in general and will be explained relations between evolutionary dynamics, its visualization as a complex network and its control. It discusses research in evolutionary algorithms that can be considered a discrete dynamical complex system with inherent nonlinear dynamics. These dynamics is visualized and analyzed as a complex network and CML (coupled map lattices) systems. As already reported in many research papers and books, these dynamics can generate different kinds of behavior including chaotic one. Selected evolutionary algorithms discussed in this seminar will be differential evolution, genetic algorithm, particle swarm, artificial bee algorithm, and others. Methodology, converting evolutionary algorithms to the complex network will be introduced including demonstrations. The seminar then continues by explaining how evolutionary dynamics can be converted into so-called CML systems, which are used to model spatiotemporal behavior (including chaotic one) and also will be explained its relation to complex networks. At the end will be demonstrated how we can control EAs dynamics using a feedback loop control scheme. The seminar is designed as a topic overview, its structure, results, and content are based on our previous research, journal publications, keynotes, and tutorials and reflect the latest progress in that field.

Lezione aperta DISEI Nobel 2019
2019-12-05 12:00 - 13:30 (Europe/Rome)
Title: Lezione aperta DISEI Nobel 2019
Speakers: Prof. Fabrizia Mealli (Director of the Florence Center for Data Science, University of Florence) and Prof. Mario Biggeri (University of Florence)
Location: Building D6 Room 0.18 – Via delle pandette, 9

DISEI is pleased to present the 2019 DISEI class on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel.
Prof. Fabrizia Mealli and Prof. Mario Biggeri will present an overview of the main contributions of Abhijit Banerjee, Esther Duflo and Michael Kremer who have received the 2019 “Nobel” prize in economics for their experimental approach to alleviating global poverty.

Seminar @ DISEI : Il diritto On line
2019-11-26 12:00 - 13:30 (Europe/Rome) | D5 Via delle Pandette, Via delle Pandette 9, Firenze, Italy
Title: Blockchain, smart contract e cryptovalute per le imprese
Speaker: Giovanni Brancalion Spadon (Foro di Venezia)
Location: Aula D5 001 – Polo delle Scienze Sociali – Viale delle Pandette 9

DISIA-DSG-DISEI seminar: Nello Cristianini
2019-11-04 10:15 - 12:00 (Europe/Rome) | D6 Via delle Pandette 9, Via delle Pandette 9, Firenze, Italy
Title: Living with Intelligent Machines
Speaker: Nello Cristianini (University of Bristol)
Location: Building D6 Room 1.18 – Via delle pandette, 9
ABSTRACT
The way we build intelligent machines today involves two components: a (fairly) general learning algorithm, and a (very) large set of training examples. These two elements are combined to create machine translation, computer vision, online recommendations, spelling correction, and so on. In many cases we know that the intended behaviour can be closely emulated, but we have done away with modeling the mechanism that generates it, replacing that with statistical correlations. As the training examples are often obtained “from the wild”, they might contain information that we are not aware about, including various cultural biases. If these biases enter in the statistical models, they can be difficult to detect, and become part of shaping the behaviour of the intelligent agent. As we train machines by exposing them to media content and other samples of human behaviour, and these machines have the capability to emulate those behaviours, it is important that we understand the biases present in the data: this is one case in which computational social sciences become an important element of AI design. Other cases include the automation of psychometrics, and the risk of addiction. The social, ethical and legal consequences of using modern-type of AI can be better understood and managed when we think about AI also from a social science perspective.
BIO
Nello Cristianini is Professor of Artificial Intelligence (AI) at the University of Bristol. His current research covers the large-scale analysis of media content (news and social media), using various AI methods, the design of new AI methods, their application to digital humanities and computational social science, and the social impact of Big Data and AI technologies. Cristianini is the co-author of two widely known books in machine learning, An Introduction to Support Vector Machines and Kernel Methods for Pattern Analysis, as well as a book in bioinformatics, Introduction to Computational Genomics. He is also a recipient of the Royal Society Wolfson Research Merit Award and a current holder of a European Research Council Advanced Grant. In 2014, Thomson-Reuters included him in a list of the most influential computer scientists of the decade. Before joining the University of Bristol, he has been a professor of statistics at the University of California, Davis. Currently he is working on social and ethical implications of AI.

Seminar @DISIA : Current Challenges in the Analysis of Brain Signals
2019-10-28 11:00 - 12:30 (Europe/Rome) | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Current Challenges in the Analysis of Brain Signals
Speaker: Hernando Ombao (KAUST)
Location: Aula 32 – DISIA – Viale Morgagni 59
Abstract

Seminar: Complexity of nonconvex optimization
2019-10-04 11:30 - 13:00 (Europe/Rome) | Plesso didattico Morgagni, Viale Morgani 40, Firenze, 50134, Italy
Seminar @Dipartimento di Ingegneria Industriale
Room 108 – Plesso didattico Morgagni
Philippe Toint (University of Namur)
Complexity of nonconvex optimization
Abstract.
We present a review of results on the worst-case complexity of minimization algorithms for nonconvex problems using potentially high-degree models.
Global complexity bound are presented that are valid for any model’s degree and any order of optimality, thereby generalizing known results for first- and second-order methods. An adaptive regularization algorithm using derivatives up to degree p will produce an epsilon-approximate q-th order minimizer in at most O(epsilon^( -(p+1)/(p−q+1) ) evaluations. We will also extend these results to the case of inexact objective function and derivatives with an application to subsampling algorithms for machine learning.

Seminar: A Next Step for Bioinformatics
2019-10-04 11:30 - 12:30 (Europe/Rome) | via di Santa Marta 3, FIrenze
Title: A Next Step for Bioinformatics: Integrated Machine Learning and Stochastic Modeling for Understanding Disease-related Cell Signaling and Regulation
Speaker: Juan Cui (Univ. of Nebraska)
Location: Aula Caminetto – Via di S. Marta 3

MICC seminar: Xavier Alameda-Pineda
2019-07-22 12:30 - 13:30 (Europe/Rome) | Aula Anfiteatro Ex-Farmacologia, Viale Morgagni 65, Firenze, Italy
Title: Probabilistic and deep methods for human behavior understanding
Speaker: Dr. Xavier Alameda-Pineda
Location: Aula Anfiteatro Ex-Farmacologia – Viale Morgagni 65
ABSTRACT
In this talk we will discuss several works on automatic understanding of human behavior. First, I will present recent variational expectation-maximisation techniques for multi-speaker tracking, where a combinatorial problem over time yields to a high-complexity exact solution that is intractable in practice. Variational approximation of the posterior leads to an efficient, yet performant, way to tackle the problem. Secondly, I will present robust deep regression techniques based on probabilistic models and discuss the joint training of the graphical model and the network parameters. Finally, I will propose one way to exploit behavioral diversity for data generation.
BIO
Xavier Alameda-Pineda received M.Sc. in Mathematics (2008), in Telecommunications (2009) and in Computer Science (2010). He obtained his Ph.D. in Mathematics and Computer Science from Universite Joseph Fourier in 2013. Since 2016, he is a Research Scientist at Inria Grenoble, with the Perception Team. He served
as Area Chair at ICCV 2017, at ACM MM 2019 and at ICIAP 2019. He is the recipient of several paper awards, and of the ACM SIGMM Rising Star 2018. He is a Senior Member of the IEEE. His scientific interests lie in computer vision, machine learning and signal processing for human behavior understanding and robotics.

Summer school: Optimization, Big Data and Applications
2019-06-30 - 2019-07-06
DINFO/UNIFI organizes the Second Edition of the Summer School on Optimization, Big Data and Applications (OBA) which will take place from June 30th to July 06th, 2019 in Veroli (FR), Italy
The speakers will be:
Nicolò Cesa-Bianchi, University of Milano
Julien Mairal, INRIA, Grenoble
Miguel Á. Carreira-Perpiñán, Univ of California at Merced
Andrew Bagdanov, Unviersità di Firenze
More information on the website https://webgol.dinfo.unifi.it/oba/

Short course of Tamas Terlaky at DINFO
2019-06-18 - 2019-06-21 | DINFO, via di Santa Marta 3, Firenze
Prof. Tamas Terlaky will give a PhD course on “Modern convex optimization: Duality, Algorithms, Solutions and Interpretations ”
Lectures will be offered on Tuesday June 18, Wed June 19, Thu June 20, Fri June 21 from 10:30 to 13:00 in room 107, via di Santa Marta 3, Firenze.
Abstract
Optimization methodology is the engine of prescriptive analytics. This short course gives a gentle, rigorous introduction to modern convex optimization models and algorithms.
Duality provides optimality conditions and serve as the platform of algorithm design.
First we focus on duality in linear optimization (LO) and convex conic linear optimization (CLO) problems.
CLO includes the LO, second order conic and semidefinite optimization problems, which are solvable by Interior Point Methods (IPMs) in polynomial time, and also the NP-hard classes of copositive and completely positive CLO problems. Robust LO models will motivate the introduction of second order conic optimization problems.
Then algorithmic concepts, such as pivot algorithms and interior point methods (IPMs) are discussed.
Initialization of the algorithms, the computational cost and efficient computation of an iterative step, and characteristics of the produced optimal solutions are discussed.
As time allows we shortly available software packages, sensitivity analysis, and some applications will be discussed.
Note:
Preliminary knowledge of optimization, operations research models and methods is a plus, but everyone with good linear algebra and multi-dimensional calculus skills should be able to follow the course.
PS: if you wish to be informed on the intiatives of the PhD program in Information Engineering, please refer to the official website, where a calendar is also available

Short course Prof. Giovanni Parmigiani at DiSIA
2019-06-12 - 2019-06-13 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Prof. Giovanni Parmigiani will give a PhD course on “Multi-Study Biomarker Analysis”
Check here for more details

First FDS Meeting
2019-06-10 | Auditorium B, Viale Morgagni 40, Firenze, Italy
The first meeting of the Florence Center for Data Science will be held on June 10, 2019, in Viale Morgagni 40, Auditorium B.
The meeting will feature the participation of outstanding researchers in Data Science. Speakers include Francesca Dominici, Giovanni Parmigiani and Donato Malerba.
An overview of research projects and activities conducted at UNIFI around Data Science will also be provided.
Detailed program.

ITACOSM 2019
2019-06-05 - 2019-06-07
The 6th ITAlian COnference on Survey Methodology – ITACOSM 2019 – will be held in Florence from 5 to 7 June 2019 and will take place at the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
Check here for the final program.

Artificial Intelligence in health and well-being
2019-05-29 09:30 UTC | Area di Ricerca CNR, Via Madonna del Piano, 10, Sesto Fiorentino, FI
One-day Workshop organised by IFAC-CNR and DISIA-UNIFI
