Florence data science

Seminar of the "Special Guest Seminar Series" - Iavor Bojinov
2022-06-29 16:00 - 17:00 (Europe/Rome) | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Welcome to the “Special Guest Seminar Series“!
The Seminar will be held on-site and online Wednesday 29th of June 2022.
Our guest will be Iavor Bojinov from Harvard Business School

The seminar will be held in Aula 005 (ex C) (DISIA – Viale Morgagni 59).
The Seminar will be available also online. Please register here to participate online:

https://unifirenze.webex.com/unifirenze/j.php?RGID=r4fee0e61279d106d8d6c2bdd3ff73f0d

Title: Design & Analysis of Dynamic Panel Experiments
Abstract: Over the past few years, firms have begun to transition away from the static single intervention A/B testing into dynamic experiments, where customers’ treatments can change over time within the same experiment. This talk will present the design-based foundations for analyzing such dynamic (or sequential experiments), starting with the extreme case of running an experiment on a single unit—what’s known as time-series experiments. Next, motivated by my work to understand if humans or algorithms are better at executing large financial trades, I will lay out a framework for designing and analyzing switchback experiments, a special case of time-series experiments. Then, I will explain how to extend this framework to multiple units and what happens when these units are subject to population interference (the setting where one unit’s treatment can impact another’s outcomes). Finally, if time allows, I will conclude with a brief discussion of an empirical study that leveraged over 1,000 experiments conducted at LinkedIn to quantify the additional benefits of adopting dynamic experimentation.

DISIA Seminar: Exploring the Educational Paradox on Preterm Births in Colombia
2022-06-20 15:00 - 16:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Exploring the Educational Paradox on Preterm Births in Colombia
Speaker: Harold Mera León (Universitat Pompeu Fabra, Barcelona)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 (need to register here https://labdisia.disia.unifi.it/reserve205/)
Abstract: Why could mothers with higher education be more prone to preterm births? Preterm birth (PTB) is widely recognized as a primary causal connection to birth and early childhood losses. We build on Bronfenbrenner’s bioecological approach and assess the effect of a mother’s education level on PTB odds. Combining Bronfenbrenner’s framework with empirical population observations, we analyze data from the National Health Statistics Surveys (NHSS), the National Centre of Historical Memory (NCHM), the 2012 Poverty Mission, and the Information System of Victims Unit. We fit a logistic model to explore the paradoxical relation between mothers with higher education and the odds of PTB (Mera, 2021) by estimating the moderation effect of higher education over regional violence. We argue that during 2002, pregnant women who could complete university level before labor were more prone to give PTB (under 38 weeks of gestational time) due to the high levels of unemployment and violence. However, when considering the interaction between regional violence and a mother’s education level, the odds of PTB increase when mothers cannot reach university level, and the effect of violence over the dyad reduces for mothers who could complete university. Hence, even though a pregnant woman with university-level living in regions with high levels of violence and unemployment is more likely to experience stress, the education level operates as a shielding factor, moderating the harmless effect of violence only in specific cases and regions where unemployment is not that high.

Seminar of the "Special Guest Seminar Series" - Dante Amengual
2022-06-17 14:00 - 15:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Welcome to the “Special Guest Seminar Series“!
The Seminar will be held on-site and online Friday 17th June 2022.
Our guest will be Dante Amengual from CEMFI in Madrid, Spain.
The seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). Participation on site is restricted and you need to register here https://labdisia.disia.unifi.it/reserve205/
The Seminar will be available also online. Please register here to participate online:
https://unifirenze.webex.com/unifirenze/j.php?RGID=rd77cde701a83e73a6dc672a2e9644c85

Title: Hypothesis tests with a repeatedly singular information matrix
Abstract: We study score-type tests in likelihood contexts in which the nullity of the information matrix under the null is larger than one, thereby
generalizing earlier results in the literature. Examples include multivariate skew-normal distributions, Hermite expansions of Gaussian copulas, purely non-linear predictive regressions, multiplicative seasonal time series models, and multivariate regression models with selectivity. Our proposal, which involves higher-order derivatives, is asymptotically equivalent to the likelihood ratio but only requires estimation under the null. We conduct extensive Monte Carlo exercises that study the finite sample size and power properties of our proposal and compare it to alternative approaches.

DISEI Seminar: Algorithmic Bias and Problematic Use of Social Media
2022-06-01 15:00 - 16:30 | D6 Via delle Pandette 9, Via delle Pandette 9, Firenze, Italy
Title: Algorithmic Bias and Problematic Use of Social Media
Speaker: Nello Cristianini (University of Bristol)
Location: Campus di Novoli aula D6 0.18 or online here https://tinyurl.com/5n894whe

FDS Seminar - Pedro J. Gutiérrez Diez
2022-05-27 14:00 - 15:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Seminar will be held both on-site and online Friday 27th of May 2022, from 2-3 PM.

Pedro J. Gutiérrez Diez from the Department of Economic Theory / Mathematical Research Institute (IMUVa) of the University of Valladolid will present a seminar on “Analysis of the epigenetic changes in the breast after pregnancy” (see abstract below).

The seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). Participation on site is restricted and you need to register here https://labdisia.disia.unifi.it/reserve205/
The Seminar will be available also online. Please register here to participate online:

https://unifirenze.webex.com/unifirenze/j.php?RGID=re6d19dd000a6188d9c63e6667b9c1a8e

Speaker: Pedro J. Gutiérrez Diez – Department of Economic Theory / Mathematical Research Institute (IMUVa) – University of Valladolid
Title: Analysis of the epigenetic changes in the breast after pregnancy
Abstract: Full-term pregnancy at an early age (FFTP) confers long-term protection against breast cancer, being a guide for research on cancer prevention. The correct design of strategies based on this protective effect of pregnancy requires the characterization of its genomic consequences. In this respect, published literature suggests that pregnancy causes a specific transcriptomic profile controlling chromatin remodeling after pregnancy, therefore implying multiple and complex changes in gene expressions. In this research we analyze from several perspectives the modifications in the gene expression after FFTP, concluding that, independently of the changes in the gene expression at the individual level usually considered, there are significant changes in gene-gene interactions and gene cluster behaviors.

17th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-05-13 10:00 - 11:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Florence Center for Data Science is happy to present the last Seminar of the “D2 Seminar Series” for this year launched by the FDS. The Seminar will be held on-site and online Friday 13th of May 2022 from 10 to 11.30 am.
Our guests will be Georgia Papadogeorgou and Joseph Antonelli from the Department of Statistics at the University of Florida.
The seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). Participation on site is restricted and you need to register here https://labdisia.disia.unifi.it/reserve205/
The Seminar will be available also online. Please register here to participate online:

https://unifirenze.webex.com/unifirenze/j.php?RGID=rddf7e0689ad2f9918485ada9101dbe17

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

Speaker: Georgia Papadogeorgou – Department of Statistics, University of Florida
Title: Unmeasured spatial confounding
Abstract: Spatial confounding has different interpretations in the spatial and causal inference literature. I will begin this talk by clarifying these two interpretations. Then, seeing spatial con-founding through the causal inference lens, I discuss two approaches to account for unmeasured variables that are spatially structured when we are interested in estimating causal effects. The first approach is based on the propensity score. We introduce the distance adjusted propensity scores (DAPS) that combine spatial distance and propensity score difference of treated and control units in a single quantity. Treated units are then matched to control units if their corresponding DAPS is low. We can show that this approach is consistent, and we propose a way to choose how much matching weight should be given to unmeasured spatial variables. In the second approach, we aim to bridge the spatial and causal inference literature by estimating causal effects in the presence of unmeasured spatial variables using outcome modeling tools that are popular in spatial statistics. Motivated by the bias term of commonly-used estimators in spatial statistics, we propose an affine estimator that addresses this deficiency. I will discuss that estimation of causal parameters in the presence of unmeasured spatial confounding can only be achieved under an untestable set of assumptions. We provide one such set of assumptions that describe how the exposure and outcome of interest relate to the unmeasured variables.
Speaker: Joseph Antonelli – Department of Statistics, University of Florida
Title: Heterogeneous causal effects of neighborhood policing in New York City with staggered adoption of the policy
Abstract: In New York City, neighborhood policing was adopted at the police precinct level over the years 2015-2018, and it is of interest to both (1) evaluate the impact of the policy, and (2) understand what types of communities are most impacted by the policy, raising questions of heterogeneous treatment effects. We develop novel statistical approaches that are robust to unmeasured confounding bias to study the causal effect of policies implemented at the community level. We find that neighborhood policing decreases discretionary arrests in certain areas of the city, but has little effect on crime or racial disparities in arrest rates.

16th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-04-22 14:00 - 15:30 | Online
The Florence Center for Data Science is happy to present the next Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held on-site and online Friday 22nd of April 2022, from 2-3.30 pm.

Andrea Barucci from IFAC-CNR Institute of Applied Physics will present a seminar on “Exploring Egyptian Hieroglyphs with Convolutional Neural Networks” and Alessandra Mattei from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence will present a seminar on “Selecting Subpopulations for Causal Inference in Regression Discontinuity Designs” (see abstract below).
The seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). Participation on site is restricted and you need to register here https://labdisia.disia.unifi.it/reserve205/
The Seminar will be available also online. Please register here to participate online:
https://unifirenze.webex.com/unifirenze/j.php?RGID=rab9a115b6fac3b81f6b3fb36bb96f6da
After registering, you will receive a confirmation email containing information about joining the webinar.

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Speaker: Andrea Barucci – IFAC-CNR Institute of Applied Physics
Title: Exploring Egyptian Hieroglyphs with Convolutional Neural Networks
Abstract: Deep Learning is expanding in every domain of knowledge, allowing specialists to build tools to support their work in fields apparently unrelated to information technology. In this study, we exploit this opportunity by focusing on Egyptian hieroglyphic texts and inscriptions. We investigate the ability of several convolutional neural networks (CNNs) to segment glyphs and classify images of ancient Egyptian hieroglyphs derived from various image datasets. Three well-known CNN architectures (ResNet-50, Inception-v3, and Xception) were considered for classification and trained on the supplied pictures. Furthermore, we constructed a specifically devoted CNN, termed Glyphnet, by changing the architecture of one of the prior networks and customizing its complexity to our classification goal. The suggested Glyphnet outperformed the others in terms of performance, ease of training, and computational savings, as judged by established measures. The hieroglyphs segmentation was faced in parallel, using a deep neural network architecture known as Mask-RCNN. This work shows how the ancient Egyptian hieroglyphs identification task can be supported by the Deep Learning paradigm, laying the foundation for developing novel information tools for automatic documents recognition, classification and, most importantly, the language translation task.

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Speaker: Alessandra Mattei – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Selecting Subpopulations for Causal Inference in Regression Discontinuity Designs (Joint work with Laura Forastiere e Fabrizia Mealli)
Abstract: The Brazil Bolsa Famı́lia program is a conditional cash transfer program aimed to reduce short-term poverty by direct cash transfers and to fight long-term poverty by increasing human capital among poor Brazilian people. Eligibility for Bolsa Famı́lia benefits depends on a type of cutoff formula, which classifies the Bolsa Famı́lia study as a regression discontinuity (RD) design. Extracting causal information from RD studies is challenging. Following Li, Mattei and Mealli (2015) and Branson and Mealli (2019), we formally describe the Bolsa Famı́lia RD design as a local randomized experiment within the potential outcome approach. Under this framework, causal inference concerns Brazilian families belonging to some subpopulation where a local overlap assumption, a local SUTVA and a local ignorability assumption hold. We first discuss the potential advantages of this framework, in settings were assumptions are judged plausible, over local regression methods based on continuity assumptions, namely a) it generates treatment effects for subpopulation members rather than local average treatment effects for those at the cutoff only, making the results more easily generalizable; b) it avoids modeling assumptions on the relationship between the running variable and the outcome; c) it allows the treatment assignment mechanism to be random rather than deterministic as in typical RD analyses, so that finite population inference can be used; d) it allows to easily account for discrete running variables. A critical issue of the approach is how to choose subpopulations for which we can draw valid causal inference. We propose to use a Bayesian model-based finite mixture approach to clustering to classify observations into subpopulations where the RD assumptions hold and do not hold on the basis of the observed data. This approach has important advantages: a) it allows to account for the uncertainty about the subpopulation membership, which is typically neglected; b) it does not impose any constraint on the shape of the subpopulation (bandwidth); c) it can be used as a design phase of any analysis; d) it is scalable to high-dimensional settings; e) and it allows to account for rare outcomes. We apply the framework to assess causal effects of the Borsa Famı́lia program on leprosy incidence in 2009, which is a rare outcome, using information on a large sample of Brazilian families who registered in the Single Registry in 2007-2008 for the first time.

15th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-04-08 15:00 - 16:30 | Online
The Florence Center for Data Science is happy to present the 15th Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 8th of April 2022, from 3-4.30 pm.
Fabio Schoen from the Department of Information Engineering of the University of Florence will present a seminar on “Clustering for Optimization, Optimization for Clustering” and Alessandro Panunzi and Lorenzo Gregori from the Department of Humanities of the University of Florence will present a seminar on “Towards action concepts identification through unsupervised and semi-supervised clustering on a multimodal cross-linguistic ontology”.
Register for this webinar: https://unifirenze.webex.com/unifirenze/j.php?RGID=ref67f44b0c3e01a0b79245a5ef1f5e92

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

Speaker: Fabio Schoen – Department of Information Engineering, University of Florence
Title: Clustering for Optimization, Optimization for Clustering
Abstract: In this tak I will present two fundamental problems in data science: the global optimization problem (i.e., how to find globally optimal solutions to a mathematical programming problem) and the problem of clustering multi-dimensional data (i.e. how to efficiently group data according to ismilarity). The aim of this talk is to present the connections between these fundamental problems and to show how each of them can be used to improve the performance of the other one. For Global Optimization problems, the idea of clustering dates back to the 80’s, when researchers used clustering techniques to recognize the regions of attraction of local optima, in the search for the global one. Due to reasons that I will be explaining during the seminar, those approaches were abandoned, however we have shown that, provided some modifications are introduced, they might prove very interesting for modern global optimization. On the other side, clustering high dimensional data is clearly an optimization problem, as we would like to group points so that a measure of similarity within groups is maximized. Recent computational approaches have been developed in which classical clustering techniques, like, e.g., K-means, are used as local optimization tools which, when embedded in a higher level global optimization strategy, can produce significantly better clusters.
This talk is partly based on research done in collaboration with dr. Luca Tigli, PhD, and dr. Pierluigi Mansueto
Speaker: Alessandro Panunzi & Lorenzo Gregori – Department of Humanities, University of Florence
Title: Towards action concepts identification through unsupervised and semi-supervised clustering on a multimodal cross-linguistic ontology
Abstract: This work presents the steps performed on IMAGACT ontology of action to identify cognitively consistent action concepts through machine learning methods. IMAGACT contains a set of 1,010 actions, represented by video scenes, and enriched with linguistic data in 14 languages. Each scene is linked to the full set of verbs that can be used to refer the depicted action, in every language. Starting from these data, an automatic clustering of scenes has been performed, using the linked lexical items as a feature set, following the idea that similar actions can be referred by a similar group of verbs. In order to obtain an evaluation of the clusters, a wide set of surveys have been set up, and action similarity judgements from human raters have been collected. These data have been analyzed together with automatic clustering metrics to evaluate the clustering and to tune the algorithm. The presentation will also focus on similarity evaluation issues emerging from a task that involves human perception and cognitive processing.

DISIA Seminar: A new programming interface for Gaussian process regression
2022-04-08 11:00 - 12:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: A new programming interface for Gaussian process regression
Speaker: Giacomo Petrillo (Department of Statistics, Computer Science, Applications, University of Florence)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 (need to register here https://tinyurl.com/mrx654pn).
The seminar will be also online and you can participate at the following link: https://tinyurl.com/yc8a78zm
Abstract: A Gaussian process is a multivariate Normal distribution over a space of functions. Gaussian processes are commonly used as a prior in a Bayesian setting to infer an unknown function without specifying a finitely parameterized model. (In non-Bayesian contexts, this is known as kriging.) This technique is very flexible, but at the same time allows to provide strong prior information, when available, which would be difficult to encode in a model, like the degree of smoothness of the function or its periodicity. From the point of view of non-statisticians or applied statisticians, Gaussian processes are used through a pre-written program, much like most statistical methods. I will present a Python module designed for the task which introduces a new kind of interface to define the structure of the problem and manipulate the information, focused on maintaining a high degree of flexibility while keeping the user code as short and readable as possible. I will show how the program improves on existing implementations, then I will continue with some ideas for its future development, trying to fill in what is missing in other programs.

DISIA Seminar: Blockchain: what it is and why it matters
2022-04-06 14:30 - 16:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Blockchain: what it is and why it matters
Speaker: Laura Ricci & Damiano di Francesco Maesa (Department of Computer Science, University of Pisa)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 (need to register here https://tinyurl.com/mrx654pn).
The seminar will be also online and you can participate at the following link: https://tinyurl.com/5f7tuh5u
Abstract: A blockchain protocol is employed to implement a tamper-free distributed ledger that stores transactions created by the nodes of a P2P network and agreed upon through a distributed consensus algorithm, avoiding the need for a central authority. Blockchain technology has a great potential to radically change our socio-economical systems by guaranteeing secure transactions between untrusted entities, reducing their cost, and simplifying many processes. Such technology is going to be exploited in many areas like IoT, social networking, health care, electronic voting and so on. This talk will introduce the basic principles of this new, disruptive technology, highlighting a set of “killer applications”. We will also show the innovative potential for research in such a field, showing results on the study of blockchain transaction graphs to characterize users’ behaviors.

DIMAI: Dini Mathematics Colloquium
2022-03-31 14:30 - 16:00 | Online
The first “Dini Mathematics Colloquium” is scheduled for Thursday 31st March at 2.30 pm, it will be held by Prof. Alfio Quarteroni (Polimi – EPFL).

Title: Physics-based and data-driven mathematical models for the simulation of the heart function
Abstract: This seminar focuses on machine learning (the computers’ ability to learn based on training from large data sets) and computational science (the use of mathematical models originated from fundamental principles of physics) in solving mathematical problems of interest in real life. Similarities and differences, potentials, and limitations are discussed, as well as the enormous possibilities offered by their synergistic use. The driving application will be the simulation of the cardiac function, in both physiological and pathological regimes

You can participate by using the form you find at this link https://forms.gle/6ee2TXYbPG6axHCBA
On the morning of March 31, everyone will receive a link to a Webex meeting via email to join remotely.
For more info, visit this website https://www.dimai.unifi.it/art-408-colloquio-di-matematica-del-dini.html

14th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-03-25 14:30 - 16:00 | Online
The Florence Center for Data Science is happy to present the 13th Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 25th of March 2021, from 2.30-4 pm.
Brunero Liseo from the Department of Methods and Models for Economics, Territory, and Finance of the Sapienza University will present a seminar on “ABCC: Approximate Bayesian Conditional Copulae (with Clara Grazian and Luciana Dalla Valle)” and Ernesto De Vito from the Department of Mathematics of the University of Genova will present a seminar on “Understanding Neural Networks with Reproducing Kernel Banach Spaces“.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.

Speaker: Brunero Liseo – Department of Methods and Models for Economics, Territory, and Finance of the Sapienza University
Title: ABCC: Approximate Bayesian Conditional Copulae (with Clara Grazian and Luciana Dalla Valle)
Abstract: Copula models are flexible tools to represent complex structures of dependence for multivariate random variables. According to Sklar’s theorem (Sklar, 1959), any d-dimensional absolutely continuous density can be uniquely represented as the product of the marginal distributions and a copula function that captures the dependence structure among the vector components. In real data applications, the interest of the analyses often lies on specific functionals of the dependence, which quantify aspects of it in a few numerical values. A broad literature exists on such functionals, however, extensions to include covariates are still limited. This is mainly due to the lack of unbiased estimators of the copula function, especially when one does not have enough information to select the copula model. Recent advances in computational methodologies and algorithms have allowed inference in the presence of complicated likelihood functions, especially in the Bayesian approach, whose methods, despite being computationally intensive, allow us to better evaluate the uncertainty of the estimates. In this work, we present several Bayesian methods to approximate the posterior distribution of functionals of the dependence, using nonparametric models which avoid the selection of the copula function. These methods are compared in simulation studies and in two realistic applications, from civil engineering and astrophysics.

Speaker: Ernesto De Vito – Department of Mathematics of the University of Genova
Title: Understanding Neural Networks with Reproducing Kernel Banach Spaces
Abstract: Characterizing the function spaces corresponding to neural networks can provide a way to understand their properties. The talk is devoted to showing how the theory of reproducing kernel Banach spaces can be used to characterize the function spaces corresponding to neural networks. In particular, I will show a representer theorem for a class of reproducing kernel Banach spaces, which includes one hidden layer neural network of possibly infinite width. Furthermore, I will prove that, for a suitable class of ReLU activation functions, the norm in the corresponding reproducing kernel Banach space can be characterized in terms of the inverse Radon transform of a bounded real measure. The talk is based on on joint work with F. Bartolucci, L. Rosasco and S. Vigogna.

13th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-03-11 14:00 - 15:00 | Online
The Florence Center for Data Science is happy to present the 13th Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 11th of March 2021, from 2-3 pm.
The seminar on “ Paths and flows for centrality measures in networks” will be held by Daniela Bubboloni from the Department of Mathematics and Computer Science “Ulisse Dini” of the University of Florence.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Daniela Bubboloni – Department of Mathematics and Computer Science “Ulisse Dini”, University of Florence
Title: Paths and flows for centrality measures in networks
Abstract: Consider the number of paths that must pass through a subset X of vertices of a capacitated network N in a maximum sequence of arc-disjoint paths connecting two vertices y and z. Consider then the difference between the maximum flow value from y to z in N and the maximum flow value from y to z in the network obtained by N by setting to zero the capacities of all the arcs incident to X. When X is a singleton, those quantities are involved in defining and computing the flow betweenness centrality and are commonly identified without any rigorous proof justifying the identification. That surprising gap in the literature is the starting point of our research. On the basis of a deep analysis of the interplay between paths and flows, we prove that, when X is a singleton, those quantities coincide. On the other hand, when X has at least two elements, those quantities may be different from each other. By means of the considered quantities, two conceptually different group centrality measures, respectively based on paths and flows, can be naturally defined. Such group centrality measures both extend the flow betweenness centrality to groups of vertices and satisfy a desirable form of monotonicity.

12th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-02-25 14:30 - 16:00 | Online
The Florence Center for Data Science is happy to present the twelfth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 25th of February 2021, from 2.30-4.00 pm.
The seminar will be held by Marco Pangallo from the Institute of Economics and of the Economics and Management in the era of Data Science (EMbeDS) of Sant’Anna School of Advanced Studies – Pisa and Fiammetta Menchetti from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_W9PHbIB-TQOs98cL7Z0Ilw
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Marco Pangallo – Institute of Economics and of the Economics and Management in the era of Data Science (EMbeDS) of Sant’Anna School of Advanced Studies – Pisa
Title: Making a housing market agent-based model learnable
Abstract: Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems based on micro-level assumptions. Often, some of their micro-level variables cannot be observed in empirical data. These latent variables make it difficult to initialize an ABM in order to use it to track and forecast empirical time series. In this paper, we propose a protocol to learn the latent variables of an ABM. We show how a complex ABM can be reduced to a probabilistic model, characterized by a computationally tractable likelihood. This reduction can be abstracted into two general design principles: balance of stochasticity and data availability, and replacement of unobservable discrete choices with differentiable approximations. We showcase our protocol by applying it to an ABM of the housing market, in which agents with different incomes bid higher prices to live in high-income neighborhoods. We show that the obtained model preserves the general behavior of the ABM, and at the same time it allows the estimation of latent variables through the optimization of its likelihood. In synthetic experiments, we show that we can learn the latent variables with good accuracy, and that our estimates make out-of-sample forecasting more precise compared to alternative benchmarks. Our protocol can be seen as an alternative to black-box data assimilation methods, forcing the modeler to lay bare the assumptions of the model, think about the inferential process, and identify potential identification problems.
Speaker: Fiammetta Menchetti – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Combining counterfactual outcomes and ARIMA models for policy evaluation
Abstract: The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes.In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of ARIMA models, which are instead very common in the econometrics literature. We propose a novel approach, Causal-ARIMA (C-ARIMA), to define and estimate the causal effect of an intervention in observational time series settings under the RCM. We first formalize the assumptions enabling the definition, the estimation and the attribution of the effect to the intervention. In the empirical application, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales. The Causal-ARIMA R package provides an implementation of our proposed approach.

11th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-02-11 14:30 - 16:00 | Online
The Florence Center for Data Science is happy to present the eleventh Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 11th of February 2021, from 2.30-4.00 pm.
The seminar will be held by Fabio Corradi and Michela Baccini from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Fabio Corradi – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Learning the two parameters of the Poisson-Dirichlet distribution with a forensic application
Abstract: This contribution is motivated by the rare type match problem, a relevant forensic issue. There, difficulties arise to evaluate the likelihood ratio comparing the defense and the prosecution hypotheses since the specific matching characteristic from the suspect and the crime scene is not in the reference database. A recently proposed solution approximates the likelihood ratio by plugging in the parameters MLE of a Poisson Dirichlet distribution, a
Bayesian nonparametric prior modeling probability mass function showing a power-law behavior in the infinite dimensional space. We instead consider how to learn the parameters of a Posson-Dirichlet and we propose two sampling schemes: Monte Carlo Markov Chain and Approximate Bayesian Computation. We demonstrate that the previously employed plug-in solution produces a systematic bias that Bayesian inference avoids entirely. Finally, we evaluate the method using a database of Y-chromosome haplotypes.
Speaker: Michela Baccini – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Combining and comparing regional epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and model assessment via Global Sensitivity Analysis
Abstract: During autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number R0(t), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R0(t) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R0(t) ranged from 2.61 (North) to 2.15 (South) with an increase following school reopening and a decline at the end of October. Average temperature, urbanization, characteristics of family medicine and health care system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R0(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons.

Kick-off meeting of the Master in Data Science and Statistical Learning MD2SL
2022-02-01 16:30 - 18:30 | Online
We are pleased to invite you to the Kick-off meeting of the Master in Data Science and Statistical Learning of the University of Florence in collaboration with the Scuola IMT Alti Studi Lucca.
Here attached you can find the program of the event. The Kick-off meeting will take place online on Tuesday, 1st of February 2022 starting from 4.30 pm.

You can register for the event using this link https://us02web.zoom.us/meeting/register/tZwrfumuqzwrH9JcaHgeMnPFyMdb2iRMS8QT
After the registration, you wilt receive an email with the link to access the zoom meeting.

10th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2022-01-28 14:30 - 16:00 | Onine
The Florence Center for Data Science is happy to present the tenth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 28th of January 2021, from 2.30-4.00 pm.
The seminar will be held by Lorenzo Seidenari from the Department of Information Engineering of the University of Florence and Francesco Calabrò from the Department of Mathematics and Applications “Renato Caccioppoli” of the University of Naples “Federico II”.
Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Lorenzo Seidenari – Department of Information Engineering, University of Florence
Title: Predicting Multiple Future Trajectories for Safe Self-Driving Cars
Abstract: Autonomous navigating agents are becoming a reality. Pedestrians and drivers are expected to safely navigate complex urban environments along with several non-cooperating agents. Autonomous vehicles will soon replicate this capability. Agents must learn a representation of the world and must make decisions ensuring safety for themselves and others. Apart from sensing objects, knowing, and abiding traffic regulations a driving agent must plan a safe path. This requires predicting motion patterns of observed agents for a far enough future. Moreover, with the rise of autonomous cars, a lot of attention is also drawn by the explainability of machine learning models for self-driving cars. In this talk, I will go over our recent contributions in the field of self-driving systems. I will present our recent works on multimodal trajectory prediction exploiting a novel use of memory augmented neural networks. Finally, we will look at simple explainable models for driving and trajectory prediction.
Speaker: Francesco Calabrò – Department of Mathematics and Applications “Renato Caccioppoli”, University of Naples “Federico II”
Title: The use of neural networks for the resolution of Partial Differential Equations
Abstract: In this talk, we present the construction of a Physics-Informed method for the resolution of stationary Partial Differential Equations. Our method relies on the construction of a Feedforward Neural Network (FNN) with a single hidden layer and sigmoidal transfer functions randomly generated, the so-called Extreme Learning Machines (ELM). We use ELM random projection networks as discrete space where to look for the solution of PDEs. Free parameters (N external weights) are fixed by imposing exactness on M (eventually located randomly) points via collocation. In order to obtain accurate solutions, we underdeterminate the collocation equations (N>M). For linear PDEs, the weights are computed by a one-step least-square solution of the linear system. The least-square solution is capable of automatically selecting the important features, i.e. the functions in the space that are more influent for the solution. This leads to a one-shot automatic method and there is no need for adaptive procedures or tuning of the parameters as done when learning in other methods based on FNN. We present results for elliptic benchmark problems both in the linear case [1] and for the resolution and construction of bifurcation diagrams of nonlinear problems [2]. The results are obtained in collaboration with Gianluca Fabiani and Costantinos Siettos.
[1] Calabrò, F., Fabiani, G., & Siettos, C. (2021). Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients. Computer Methods in Applied Mechanics and Engineering, 387, 114188.
[2] Fabiani, G., Calabrò, F., Russo, L. & Siettos, C. (2021). Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines. J Sci Comput 89, 44

IFAC Seminar Series «Artificial Intelligence Applications»
2021-12-20 11:00 - 12:00 | Area di Ricerca CNR, Via Madonna del Piano, 10, Sesto Fiorentino, FI
The Florence Center for Data Science is happy to forward the invitation to a seminar of the seminar series «Artificial Intelligence Applications» organized by Andrea Barucci from IFAC-CNR.
This seminar held by Eng. Lorenzo Python on December 20th 2021 from 11 to 12 am and will be on “On the segmentation and classification of ancient Egyptian hieroglyphs through deep learning techniques”.
Lorenzo has graduated with Andrea Barucci and Prof. Argenti (DINFO-UniFI) in Electronics and Telecommunications Engineering. This seminar aims to show the power of deep learning tools on a specific example. As you know, these methods are transversal and can be adapted to various applications, among which we are sure those of your interest. The seminar will illustrate the implementation of a neural network capable of performing object detection and segmentation tasks, applied to the case of Egyptian hieroglyphs taken from ancient texts.
The seminar will be available in two ways: in-person and remotely (Webex details below). If you are interested in attending it in person, please notify Andrea Barucci (a.barucci@ifac.cnr.it) as soon as possible, given the Covid19 restrictions.

ACCEDI A RIUNIONE WEBEX
https://ifac-cnr.webex.com/ifac-cnr-it/j.php?MTID=m68d26293af558dc566e15300fec76152
Numero riunione (codice di accesso): 2734 341 9533
Password riunione: upJQH8p5M2w

Data Science Xmas Lecture
2021-12-16 15:00 - 16:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Florence Center for Data Science is happy to present the “Data Science Christmas Lecture” which is going to be held by Marina Vannucci, Noah Harding Professor of Statistics, Rice University
When: 16th December 2021, from 3 to 4.30 pm
Where: Aula 205 (ex 32) – DISIA – Viale Morgagni 59. The participation on site is restricted and you need to register here https://labdisia.disia.unifi.it/reserve205/.
If you decide to attend the event, after registering please send us an email at this email address datascience@unifi.it
The Lecture will be available also online. Please register here to participate https://us02web.zoom.us/webinar/register/WN_0QB9L_b3RlS9Zp5Rix30_g
Speaker: Marina Vannucci – Noah Harding Professor of Statistics, Rice University
Title: Bayesian Models for Microbiome Data with Variable Selection
Abstract: I will describe Bayesian models developed for understanding how the microbiome varies within a population of interest. I will focus on integrative analyses, where the goal is to combine microbiome data with other available information (e.g. dietary patterns) to identify significant associations between taxa and a set of predictors. For this, I will describe a general class of hierarchical Dirichlet-Multinomial (DM) regression models which use spike-and-slab priors for the selection of the significant associations. I will also describe a joint model that efficiently embeds DM regression models and compositional regression frameworks, in order to investigate how the microbiome may affect the relation between dietary factors and phenotypic responses, such as body mass index. I will discuss the advantages and limitations of the proposed methods with respect to current standard approaches used in the microbiome community and will present results on the analysis of real datasets. If time allows, I will also briefly present extensions of the model to mediation analysis.

9th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-12-10 15:00 - 16:30 | Onine
The Florence Center for Data Science is happy to present the ninth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 10th of December 2021, from 3-4.30 pm.
The seminar will be held by Raffaele Guetto and Andrea Marino from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Raffaele Guetto – Department of Statistics, Computer Science, Applications “G. Parenti” – University of FlorenceTitle: Italy’s lowest-low fertility in times of uncertainty
Abstract: The generalized and relatively homogeneous fertility decline across European countries in the aftermath of the Great Recession poses serious challenges to our knowledge of contemporary low fertility patterns. The rise of economic uncertainty has often been identified, in the sociological and demographic literature, as the main cause of this state of affairs. The forces of uncertainty have been traditionally operationalized through objective indicators of individuals’ actual and past labour market situations. However, this presentation argues that the role of uncertainty needs to be conceptualized and operationalized taking into account that people use works of imagination, producing their own narrative of the future, also influenced by the media. To outline such an approach, I review contemporary drivers of Italy’s lowest-low fertility, placing special emphasis on the role of uncertainty fueled by labour market deregulations and – more recently – the Covid-19 pandemic. I discuss the effects of the objective (labour-market related) and subjective (individuals’ perceptions, including future outlooks) sides of uncertainty on fertility, based on a set of recent empirical findings obtained through a variety of data and methods. In doing so, I highlight the potential contribution of so-called “big data” and techniques of media content analysis and Natural Language Processing for the analysis of the effects of media-conveyed narratives of the economy.
Speaker: Andrea Marino – Department of Statistics, Computer Science, Applications “G. Parenti” – University of FlorenceTitle: Approximating the Neighborhood Function of (Temporal) GraphsAbstract: The average distance in graphs (like, for instance, the Facebook friendship network and the Internet Movie Database collaboration network), often referred to as degrees of separation, has been largely investigated. However, if the number of nodes is very large (millions or billions), computing this measure needs prohibitive time and space costs as it requires to compute for each node the so-called neighbourhood function, i.e. for each vertex v and for each h, how many nodes are within distance h from v. Temporal graphs are a special kind of graphs where edges have temporal labels, specifying their occurring times, in the same way as the connections of the public transportation network of a city are available only at scheduled times. Here, paths make sense only if they correspond to sequences of edges with strictly increasing labels. A possible notion of distance between two nodes in a temporal network is the earliest arrival time of the temporal paths connecting the two nodes. In this case, the temporal neighbourhood function is defined as the number of nodes reachable from a given one in a given time interval, and it is also expensive to compute. We introduce probabilistic counting in order to approximate the size of sets and we show how both plain and temporal neighbourhood functions can be approximated by plugging this technique into a simple dynamic programming algorithm.

8th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-11-26 14:00 - 15:30 | Onine
The Florence Center for Data Science is happy to present the eighth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 26th of November 2021, from 2-3.30 pm.
The seminar will be held by Giorgio Ricchiuti from the Department of Economics and Management and Marco Bertini from the Department of Information Engineering of the University of Florence.Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN__m4iKOO3R6WBL4uVvT-v4A
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Giorgio Ricchiuti – Department of Economics and Management, University of Florence
Title: State Space Model to Detect Cycles in Heterogeneous Agents Models (joint work with Filippo Gusella)
Abstract: We propose an empirical test to depict possible endogenous cycles within Heterogeneous Agent Models (HAMs). We consider a 2-type HAM into a standard small-scale dynamic asset pricing framework. On the one hand, fundamentalists base their expectations on the deviation of fundamental value from market price expecting a convergence between them. On the other hand, chartists, subject to self-fulling moods, consider the level of past prices and relate it to the fundamental value acting as contrarians. These pricing strategies, by their nature, cannot be directly observed but can cause the response of the observed data. For this reason, we consider the agents’ beliefs as unobserved state components from which, through a state space model formulation, the heterogeneity of fundamentalist-chartist trader cycles can be mathematically derived and empirically tested. The model is estimated using the S&P500 index, for the period 1990-2020 at different time scales, specifically, daily, monthly, and quarterly.

Speaker: Marco Bertini – Department of Information Engineering, University of Florence
Title: High quality video experience using deep neural networks
Abstract: Lossy image and video compression algorithms are the enabling technology for a large variety of multimedia applications, reducing the bandwidth required for image transmission and video streaming. However, lossy image and video compression codecs decrease the perceived visual quality, eliminate higher frequency details and in certain cases add noise or small image structures. There are two main drawbacks of this phenomenon. First, images and videos appear much less pleasant to the human eye, reducing the quality of experience. Second, computer vision algorithms such as object detectors may be hindered and their performance reduced. Removing such artefacts means recovering the original image from a perturbed version of it. This means that one ideally should invert the compression process through a complicated non-linear image transformation. In this talk, I’ll present our most recent works based on the GAN framework that allows us to produce images with photorealistic details from highly compressed inputs.

DISIA Seminar: Mortality and morbidity drivers of the global distribution of health
2021-11-25 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Mortality and morbidity drivers of the global distribution of health
Speaker: Inaki Permanyer (Centre d’Estudis Demogràfics, Barcelona)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 The participation on site is restricted to max 20 people (need to register here https://labdisia.disia.unifi.it/reserve205/). However, the seminar will be also online and you can participate at the following link: http://meet.google.com/aio-dhut-nah
You can find the abstract here: Abstract

DISIA Seminar: Innovation on methods for the evaluation of the short-term health effects of environmental hazards
2021-11-24 16:00 - 17:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Innovation on methods for the evaluation of the short-term health effects of environmental hazards.
Speaker: Francesco Sera (DiSIA) e Dominic Royé (University of Santiago de Compostela)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 The participation on site is restricted to max 20 people (need to register here https://labdisia.disia.unifi.it/reserve205/). However, the seminar will be also online and you can participate at the following link: http://meet.google.com/aio-dhut-nah
You can find the abstract here: Abstract

Seminar for "Nobel Prize for Economics 2021"
2021-11-23 11:30 - 12:30 | D6 Via delle Pandette 9, Via delle Pandette 9, Firenze, Italy
The Florence Center for Data Science is happy to invite you to the Seminar for “Nobel Prize for Economics 2021” organized by the DISEi and the School of Economics and Management of the University of Florence that will take place Tuesday 23rd of November 2021.
Click here to book your seat in advance. Soon we will provide the link for the live streaming.
This event will be in Italian.

7th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-11-12 14:00 - 15:30 | Onine
The Florence Center for Data Science is happy to present the seventh Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 12th of November 2021, from 2-3.30 pm.
The seminar will be held by Luigi Brugnano from the Department of Mathematics and Computer Science “Ulisse Dini” and Veronica Ballerini from the Department of Statistics, Computer Science and Applications “G. Parenti” of the University of Florence.
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_hjjYNaS1Tmqz1debdP-BQA
After registering, you will receive a confirmation email containing information about joining the webinar.

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Speaker: Luigi Brugnano – Department of Mathematics and Computer Science “Ulisse Dini”, University of Florence
Title: Recent advances in bibliometric indexes and their implementation
Abstract: Bibliometric indexes are nowadays very commonly used for assessing scientific production, research groups, journals, etc. It must be stressed that such indexes cannot substitute to enter the merit of the specific research but, nonetheless, they can provide a gross evaluation of its impact on the scientific community. That premise, the currently used indexes often have drawbacks and/or sensibly vary for different subjects of investigation. For this reason, in [1] an alternative index has been proposed, based on an idea akin to that of the Google PageRank. Its actual implementation has been recently done in the Scopus database [2]. In this talk, the basic facts and results of this approach will be recalled.
[1] P.Amodio, L.Brugnano. Recent advances in bibliometric indexes and the PaperRank problem. Journal of Computational and Applied Mathematics 267 (2014) 182-194. http://doi.org/10.1016/j.cam.2014.02.018
[2] P.Amodio, L.Brugnano, F.Scarselli. Implementation of the PaperRank and AuthorRank indices in the Scopus database. Journal of Infometrics 15 (2021) 101206. https://doi.org/10.1016/j.joi.2021.101206

Speaker: Veronica Ballerini – Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence
Title: Fisher’s Noncentral Hypergeometric Distribution for the Size Estimation of Unemployed Graduates in Italy (joint work with Brunero Liseo, University Sapienza di Roma)
Abstract: To quantify unemployment among those who have never been employed is often tough. The lack of an administrative data flow attributable to such individuals makes them an elusive population. Hence, one must rely on surveys. However, individuals’ response rates to questions on their occupation may differ according to their employment status, implying a not-at-random missing data generation mechanism. We exploit the underused Fisher’s noncentral hypergeometric distribution (FNCH) to solve such a biased urn experiment. FNCH has been underemployed in the statistical literature mainly because of the computational burden given by its probability mass function. Indeed, as the number of draws and the number of different categories in the population increases, any method involving the evaluation of the likelihood is practically unfeasible. Firstly, we present a methodology that allows the approximation of the posterior distribution of the population size via MCMC and ABC methods. Then, we apply such methodology to the case of graduated unemployed in Italy, exploiting information from different data sources.

FDS Seminar: Giulia Iori from the City University of London
2021-11-05 14:30 - 15:30 | Onine
Due to technical problems during the 6th seminar, we were not able to listen to the seminar of Giulia Iori. That’s why the Florence Center for Data Science is happy to invite you again to the seminar on “Performance-based research funding: Evidence from the largest natural experiment worldwide” which is going to be held online Friday 5th of November 2021, from 2.30-3.30 pm.
The seminar will be held by Giulia Iori from the Department of Economics of the City University of London.
Register for this webinar: https://us02web.zoom.us/webinar/register/WN_M2w6xh0yT9OMKGcqNztFpw

Speaker: Giulia Iori – Department of Economics of the City University of London
Title: Performance-based research funding: Evidence from the largest natural experiment worldwide
Abstract: The Research Excellence Framework (REF) is the main UK government policy on public research in the last 30 years. The primary aim of this policy is to promote and reward research excellence through competition for scarce research resources. Surprisingly, and despite the severe criticisms, little has been done to systematically evaluate its effects. In this paper, we evaluate the impact of the REF 2014. We exploit a large database that contains all publications in Economics, Business, Management, and Finance available in Scopus since 2001. We use a synthetic control method to compare the performance of each of the universities from the UK with counter-factual similar units in terms of past research constructed using data for US universities. We find a significant positive increase, relative to the control group, in the number of published papers, and in the proportion of papers published in highly ranked journals within the Economics/Econometrics area and the Business, Management and Finance area. Both Russell and non-Russell Group universities benefited from the REF, with the Russell Group universities experiencing an overall significant increase in the number of publications and number of publications in top journals, and the non-Russell group experiencing a significant increase in the proportion of publications in top journals in all areas. Interestingly, the non-Russell group experienced a comparatively stronger increase in the proportion of top publications in Economics/Econometrics while the Russell Group experienced a comparatively stronger increase in the proportion of top publications in Business, Management and Finance. However, we see an insignificant effect when we focus on per-author output measures indicating that growth in output was mostly achieved by an increase in the number of research active academics rather than an overall increase in research productivity.

6th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-10-29 14:00 - 15:30 | Online
The Florence Center for Data Science is happy to present the sixth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 29th of October 2021, from 2.30-4 pm.
The seminar will be held by Giulia Iori from the Department of Economics, School of Social Science of the City University of London and Massimo Fornasier from the Department of Mathematics of the Technical University of Munich.
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_B20LTH2CR1SrVYCcD0Eqlw
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Giulia Iori – Department of Economics, School of Social Science of the City University of London
Title: Performance-based research funding: Evidence from the largest natural experiment worldwide
Abstract: The Research Excellence Framework (REF) is the main UK government policy on public research in the last 30 years. The primary aim of this policy is to promote and reward research excellence through competition for scarce research resources. Surprisingly, and despite the severe criticisms, little has been done to systematically evaluate its effects. In this paper, we evaluate the impact of the REF 2014. We exploit a large database that contains all publications in Economics, Business, Management, and Finance available in Scopus since 2001. We use a synthetic control method to compare the performance of each of the universities from the UK with counter-factual similar units in terms of past research constructed using data for US universities. We find a significant positive increase, relative to the control group, in the number of published papers, and in the proportion of papers published in highly ranked journals within the Economics/Econometrics area and the Business, Management and Finance area. Both Russell and non-Russell Group universities benefited from the REF, with the Russell Group universities experiencing an overall significant increase in the number of publications and number of publications in top journals, and the non-Russell group experiencing a significant increase in the proportion of publications in top journals in all areas. Interestingly, the non-Russell group experienced a comparatively stronger increase in the proportion of top publications in Economics/Econometrics while the Russell Group experienced a comparatively stronger increase in the proportion of top publications in Business, Management and Finance. However, we see an insignificant effect when we focus on per-author output measures indicating that growth in output was mostly achieved by an increase in the number of research active academics rather than an overall increase in research productivity.
Speaker: Massimo Fornasier – Department of Mathematics of the Technical University of Munich
Title: Consensus-based optimization
Abstract: Consensus-based optimization (CBO) is a multi-agent metaheuristic derivative-free optimization method that can globally minimize nonconvex nonsmooth functions and is amenable to theoretical analysis. In fact, optimizing agents (particles) move on the optimization domain driven by a drift towards an instantaneous consensus point, which is computed as a convex combination of particle locations, weighted by the cost function according to Laplace’s principle, and it represents an approximation to a global minimizer. The dynamics are further perturbed by a random vector field to favor exploration, whose variance is a function of thedistance of the particles to the consensus point. Based on an experimentally supported intuition that CBO always performs a gradient descent of the squared Euclidean distance to the global minimizer, we show a novel technique for proving the global convergence to the global minimizer in mean-field law for a rich class of objective functions. We further present formulations of CBO over compact hypersurfaces. We conclude the talk with a few numerical experiments, which show that CBO scales well with the dimension and is extremely versatile.
https://arxiv.org/pdf/2103.15130.pdf to appear in Found. Comput. Math.https://arxiv.org/pdf/2001.11994.pdf appeared in M3AShttps://arxiv.org/pdf/2001.11988.pdf appeared in J. Mach. Learn. Rechhttps://arxiv.org/pdf/2104.00420.pdf to appear in SIAM J. Opt.

DISIA Seminar: Uncertainty across the “Contact Line”: armed conflict, COVID-19, and perceptions of fertility decline in Eastern Ukraine
2021-10-28 12:00 - 13:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Title: Uncertainty across the “Contact Line”: armed conflict, COVID-19, and perceptions of fertility decline in Eastern Ukraine
Speaker: Brienna Perelli-Harris (University of Southampton)
Location: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 The participation on site is restricted to max 15 people (need to contact raffaele.guetto@unifi.it). However, the seminar will be also online and you can participate at the following link: meet.google.com/atb-qtxf-nvb You can find the abstract here: Abstract

5th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2021-10-15 14:00 - 15:30 | Onine
The Florence Center for Data Science is happy to present the fifth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 15th of October 2021, from 2-3.30 pm.
The seminar will be held by Leonardo Bargigli from the Department of Economics and Management of the University of Florence and Chiara Marzi from the Institute of Applied Physics ‘Nello Carrara’ (IFAC) – National Research Council (CNR).
Register in advance for this webinar:https://us02web.zoom.us/webinar/register/WN_3tq7BbbwTSeA956KFQIK0w
After registering, you will receive a confirmation email containing information about joining the webinar.
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Speaker: Leonardo Bargigli – Department of Economics and Management, University of Florence
Title: Endogenous and Exogenous Volatility in the Foreign Exchange Market (with G. Cifarelli)
Abstract: We study two sources of heteroscedasticity in high-frequency financial data. The first, endogenous, source is the behaviour of bounded rational market participants. The second, exogenous, source is the flow of market relevant information. We estimate the impact of the two sources jointly by means of a Markov switching (MS) SVAR model. Following the original intuition of Rigobon (2003), we achieve identification for all coefficients by assuming that the structural errors of the MS-SVAR model follow a GARCH-DCC process. Using transaction data of the EUR/USD interdealer market in 2016, we firstly detect three regimes of endogenous volatility. Then we show that both kinds of volatility matter for the transmission of shocks, and that the exogenous information is channelled to the market mostly through price variations. This suggests that, on the FX market, liquidity providers are better informed than liquidity takers, who act mostly as feedback traders. The latter are able to profit from trade because, unlike noise traders, they respond immediately to the informative price shocks.
Speaker: Chiara Marzi – Institute of Applied Physics ‘Nello Carrara’ (IFAC) – National Research Council (CNR)
Title: Artificial Intelligence in Neuroimaging
Abstract: Life sciences data coupled with Artificial Intelligence (AI) techniques can help researchers accurately pinpoint novel biomarkers. AI can propose new indices as potential biomarkers while simultaneously aiding in searching for hidden patterns among “well-established” indices. In this webinar, we will take a brief journey through some applications of Machine Learning in neuroimaging. In the first part of the webinar, we will talk about the not so easy “marriage” between AI and clinical data, focusing on Big Data from Radiology Imaging and related issues. In the second part, we will see how we can transfer mathematical, physical, and statistical ideas to the Neuroimaging domain and how AI can help this transfer. An example is the study of the structural complexity of the brain starting from MRI images, using fractal analysis. The Fractal Dimension (FD) can be considered a measure of morphological changes due to healthy ageing and/or the onset of neurological diseases. The use of ML techniques can promote the candidature of FD as a biomarker for many neurological diseases.

DISEI Seminar: Istituzioni e Sviluppo Economico nel capitalismo contemporaneo
2021-10-12 14:00 - 16:00 | Online
Title: Istituzioni e Sviluppo Economico nel capitalismo contemporaneo
Speaker: Elisabetta Basile, Luca Bortolotti, Mario Biggeri, Claudio Cecchi, Franco Volpi The seminar will be held online.
You can find more information at the following link: https://www.disei.unifi.it/vp-360-seminari.html
