BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Florence data science - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Florence data science
X-ORIGINAL-URL:https://datascience.unifi.it
X-WR-CALDESC:Events for Florence data science
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Rome
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20211031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20221030T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20231029T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230420T120000
DTEND;TZID=Europe/Rome:20230420T130000
DTSTAMP:20260505T150841
CREATED:20230331T143356Z
LAST-MODIFIED:20230403T152226Z
UID:5302-1681992000-1681995600@datascience.unifi.it
SUMMARY:DiSIA seminar
DESCRIPTION:Speaker: Maurizio Pisati and Mario Lucchini (Università di Milano-Bicocca) \nTitle: L’indagine ITA.LI quantitativa: Disegno di campionamento\, pesi campionari e varianza degli stimatori \nAbstract: \nLo scopo di questo seminario è illustrare alcuni aspetti essenziali della prima rilevazione dell’indagine ITA.LI quantitativa. In primo luogo verrà descritto il disegno di campionamento\, soffermandosi sulle caratteristiche di ciascuno stadio. Successivamente sarà presentata la procedura di costruzione dei pesi campionari\, con particolare attenzione agli aggiustamenti per la non risposta e alla calibrazione post-stratificazione. L’ultima parte sarà dedicata all’incertezza degli stimatori in una prospettiva design-based. \nThe event is organized in collaboration with UPS – Population and Society Unit.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-13/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230417T120000
DTEND;TZID=Europe/Rome:20230417T133000
DTSTAMP:20260505T150841
CREATED:20230331T143027Z
LAST-MODIFIED:20230331T143027Z
UID:5300-1681732800-1681738200@datascience.unifi.it
SUMMARY:DiSIA "Welcome" Seminar - Daniele Castellana\, Andrea Marino\, Francesco Tiezzi
DESCRIPTION:Speaker: Daniele Castellana \nTitle: Machine Learning in Structured Domains \nAbstract: \nMachine Learning (ML) for structured data aims to build ML models which can handle structured data (e.g. sequence\, trees\, and graphs). In this setting\, common ML approaches for flat data (i.e. vectors) cannot be applied directly. In this seminar\, we show the challenges of learning from structured data and discuss how to overcome them. We introduce a tensor framework for learning from trees highlighting how tensors naturally arise in this context and the role of tensor decompositions to reduce the computational complexity of such approach. Interestingly\, this framework can be applied to both probabilistic and neural models for trees. Lastly\, we talk about learning from graphs. In particular\, we introduce a probabilistic model for graph based on a Bayesian Non-Parametric technique showing its effectiveness in inferring suitable hyper-parameters. \n  \n\nSpeaker: Andrea Marino \nTitle: Algorithms for the Analysis of (Temporal) Graphs \nAbstract: \nIn a society in which we are almost always “connected”\, the capability of acquiring different information has become a source of huge amounts of data to be elaborated and analysed. Graphs are data structures that allow modeling the relationships in these data\, effectively representing millions of nodes and billions of connections between nodes as edges. For example\, a social network can be seen as a graph whose nodes are the users and whose edges correspond to the friendships between them. In such a context\, discovering the most important or influential users\, which communities they belong to\, and which is the structure of their society can be translated into discovering suitable patterns on graphs. Sometimes in some scenarios additional constraints must be considered: for instance\, in temporal graphs\, connections are available only at prescribed times\, in the same way as connections of a public transportation system are available according to a time schedule. In temporal graphs\, walks and paths make sense only if the legs of the trip are time consistent\, i.e. the departure of a leg is after the arrival of the previous one\, and structural patterns must take into account this constraint. Our work is devoted to understand whether such patterns can be computed efficiently\, i.e. in time polynomial wrt the size of the graph\, hopefully linearly if the considered graphs are big. During the talk we will see a high level overview of our contributions in the field. \n\nSpeaker: Francesco Tiezzi \nTitle: A Tale on Domain-Specific System Engineering: the Case of Multi-Robot Systems \nAbstract: \nThe first part of the seminar introduces challenges and methodologies concerning the (possibly formal) engineering of domain-specific distributed systems. The second part focuses on a specific case: the development of software for robotics applications involving multiple heterogeneous robots. Programming such distributed software requires coordinating multiple cooperating tasks to avoid unsatisfactory emerging behaviors. It is then presented a programming language devised to face this challenge. The language supports an approach for programming multi-robot systems at a high abstraction level\, allowing developers to focus on the system behavior\, achieving readable\, reusable\, and maintainable code.
URL:https://datascience.unifi.it/index.php/event/disia-welcome-seminar-daniele-castellana-andrea-marino-francesco-tiezzi/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230404T150000
DTEND;TZID=Europe/Rome:20230404T160000
DTSTAMP:20260505T150841
CREATED:20230323T151141Z
LAST-MODIFIED:20230323T151141Z
UID:5293-1680620400-1680624000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Andrea Meilan Vila (Department of Statistics\, Universidad Carlos III de Madrid) \nTitle: Kernel regression estimation for a circular response and different types of covariates  \nAbstract: \nThe analysis of a variable of interest that depends on other variable(s) is a typical issue appearing in many practical problems. Regression analysis provides the statistical tools to address this type of problem. This topic has been deeply studied\, especially when the variables in the study are of Euclidean type. However\, there are situations where the data present certain kinds of complexities\, for example\, the involved variables are of circular or functional type\, and the classical regression procedures designed for Euclidean data may not be appropriate. In these scenarios\, these techniques would have to be conveniently modified to provide useful results. Moreover\, it might occur that the variables of interest can present a certain type of dependence. For example\, they can be spatially correlated\, where observations that are close in space tend to be more similar than observations that are far apart. This work aims to design and study new approaches to deal with regression function estimation for models with a circular response and different types of covariates. For an R^d-valued covariate\, nonparametric proposals to estimate the circular regression function are provided and studied\, under the assumption of independence and also for spatially correlated errors. These estimators are also adapted for regression models with a functional covariate. In the above-mentioned frameworks\, the asymptotic bias and variance of the proposed estimators are calculated. Some guidelines for their practical implementation are provided\, checking their sample performance through simulations. Finally\, the behavior of the estimators is also illustrated with real data sets.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-12/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230330T120000
DTEND;TZID=Europe/Rome:20230330T130000
DTSTAMP:20260505T150841
CREATED:20230309T102113Z
LAST-MODIFIED:20230309T102208Z
UID:5232-1680177600-1680181200@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Andrea Sottosanti (Università di Padova) \nTitle: Co-clustering Models for Spatial Transcriptomics \nAbstract: \nSpatial transcriptomics is a cutting-edge technology that\, differently from traditional transcriptomic methods\, allows researchers to recover the spatial organization of cells within tissues and to map where genes are expressed in space. By examining previously hidden spatial patterns of gene expression\, researchers can identify distinct cell types and study the interactions between cells in different tissue regions\, leading to a deeper understanding of several key biological mechanisms\, such as cell-cell communication or tumour-microenvironment interaction. \n\nDuring this talk\, we will be presenting novel statistical tools that exploit the previously unavailable spatial information in transcriptomics to coherently group cells and genes. First\, we will introduce SpaRTaCo\, a new model that clusters the gene expression profiles according to a partition of the tissue. This is accomplished by performing a co-clustering\, that is\, a simultaneous clustering of the genes using their expression across the tissue\, and of the image areas using the gene expression in the locations where the RNA is collected. Then\, we will show how to use SpaRTaCo when a previous annotation of the cell types is available\, incorporating biological knowledge into the statistical analysis. Last\, we will discuss a new modelling solution that exploits recent advances in sparse Bayesian estimation of covariance matrices to reconstruct the spatial covariance of the data in a sparse and flexible manner\, significantly reducing the computational cost of the model estimation.\nBy applying these tools to tissue samples processed with recent spatial transcriptomic technologies\, we can gain interesting and promising biological insights. The models are in fact able to reveal the presence of specific variation patterns in some restricted areas of the tissue that cannot be directly uncovered using other methods in the literature\, and\, inside each image cluster\, they can detect genes that carry out specific and relevant biological functions.\n 
URL:https://datascience.unifi.it/index.php/event/disia-seminar-11/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230328T113000
DTEND;TZID=Europe/Rome:20230328T130000
DTSTAMP:20260505T150841
CREATED:20230306T115050Z
LAST-MODIFIED:20230306T115715Z
UID:5194-1680003000-1680008400@datascience.unifi.it
SUMMARY:Economics Lecture by Nobel Prize 2021: Guido W. Imbens "Combining Experimental and Observational Data"
DESCRIPTION:The Florence Center for Data Science and the Department of Statistics\, Computer Science\, Application “G. Parenti” DiSIA in a joint event with the European University Institute EUI are happy to invite you to the Economics Lecture by Nobel Prize 2021: Guido W. Imbens.\n\n\nThe event will take place Tuesday 28th March 2023  from 11:30 to 12:45 at Auditorium A – Polo Didattico\, Viale Morgani\,40 – Università degli Studi di Firenze\n  \nThe event does not require prior registration. \n\n\n\nTitle: Combining Experimental and Observational Data \n\n\nPresented by Guido W. Imbens (Stanford University)\, Nobel Prize in Economics 2021\n\n\nAbstract:\nIn the social sciences there has been an increase in interest in randomized experiments to estimate causal effects\, partly\nbecause their internal validity tends to be high\, but they are often small and contain information on only a few variables. At the same time\, as part of the big data revolution\, large\, detailed\, and representative\, administrative data sets have become more widely available. However\, the credibility of estimates of causal effects based on such data sets alone can be low.\nIn this paper\, we develop statistical methods for systematically combining experimental and observational data to improve the credibility of estimates of the causal effects. We focus on a setting with a binary treatment where we are interested in the effect on a primary outcome that we only observe in the observational sample. Both the observational and experimental samples contain data about a treatment\, observable individual characteristics\, and a secondary (often short term) outcome. To estimate the effect of a treatment on the primary outcome\, while accounting for the potential confounding in the observational sample\, we propose a method that makes use of estimates of the relationship between the treatment and the secondary outcome from the experimental sample. We interpret differences in the estimated causal effects on the secondary outcome between the two samples as evidence of unobserved confounders in the observational sample\, and develop control function methods for using those differences to adjust the estimates of the treatment effects on the primary outcome. We illustrate these ideas by combining data on class size and third grade test scores from the Project STAR experiment with observational data on class size and both third and eighth grade test scores from the New York school system.Co-author: Susan Athey and Raj Chetty
URL:https://datascience.unifi.it/index.php/event/economics-lecture-by-nobel-prize-2021-guido-w-imbens-combining-experimental-and-observational-data/
LOCATION:Auditorium A – Viale Morgagni 40\, Auditorium A- Polo Didattico\, Viale Morgani\, 40 - Università degli Studi di Firenze\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/03/Flyer-Imbens-28th-Mar-short-e1678115649434.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230327T160000
DTEND;TZID=Europe/Rome:20230327T170000
DTSTAMP:20260505T150841
CREATED:20230314T113703Z
LAST-MODIFIED:20230314T113945Z
UID:5247-1679932800-1679936400@datascience.unifi.it
SUMMARY:DiSIA - ARTES4.0 Webinar
DESCRIPTION:The Department of Statistics\, Computer Science\, Applications DiSIA as part of the activities of the UNIFI Macronode in the ARTES4.0 Competence Center organizes the following webinar scheduled for March 27\, 2023 at 4 pm. \nWebinar DISIA#3: Explainable Artificial Intelligences;\nTitle: Counterfactual Explanations of (some) Machine Learning Models\nSpeaker: Prof. FABRIZIO SILVESTRI\,\nLanguage: Italian\n\n\nFor those interested in participating\, please  register at the link:  https://lp.artes4.it/it/ webinar-27-marzo-2023\n\n\nWebinar contents:\nStart 16.00: Introduction\n16.05 – Talk\n16.45- Discussion\, questions with participants\n17.00 – Conclusion\n\nThe third webinar promoted by DISIA focuses on Machine Learning (ML)\, in the context of Explicable Artificial Intelligence understood as a set of methodologies that allow a human to understand and trust the results produced by an ML algorithm. Such methodologies are used to describe the AI ​​model in terms of expected impact and potential bias in the decision-making processes in which an ML algorithm is integrated. These aspects are crucial to ensure that decision-making processes are impartial\, transparent and accurate.\n\nAbstract : In this presentation we will review some of the most recent contributions in the field of Explainable Artificial Intelligence (XAI) and\, in particular\, those of counterfactual (CF) explanations. We will start by reviewing our first contribution to the field and show some recent papers on CF explanations of graph neural network models. Guided by the interest of generalizing the CF explainability of ML models as much as possible\, we propose a solution based on reinforcement learning that treats the model as a black box that we cannot access directly.\nBrief Biography of Fabrizio Silvestri: Fabrizio Silvestri is a full professor at the DIAG of the Sapienza University of Rome. His research interests concern artificial intelligence and in particular machine learning applied to web search and natural language processing problems. He is the author of more than 150 articles in international journals and conference proceedings. He holds nine industrial patents. At Facebook AI\, Fabrizio Silvestri led research groups to develop artificial intelligence techniques to combat malicious actors who use the Facebook platform for malicious purposes (hate speech\, disinformation\, terrorism\, etc.). Fabrizio Silvestri holds a PhD in computer science from the University of Pisa.
URL:https://datascience.unifi.it/index.php/event/disia-artes4-0-webinar/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/03/Logo-Webinar-ARTES-Disia.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230323T120000
DTEND;TZID=Europe/Rome:20230323T130000
DTSTAMP:20260505T150841
CREATED:20230220T170558Z
LAST-MODIFIED:20230220T170826Z
UID:5162-1679572800-1679576400@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Sirio Legramanti (University of Bergamo) \nTitle: Concentration of discrepancy-based ABC via Rademacher complexity \nAbstract: \nThere has been increasing interest on summary-free versions of approximate Bayesian computation (ABC)\, which replace distances among summaries with discrepancies between the whole empirical distributions of the observed data and the synthetic samples generated under the proposed parameter values. The success of these solutions has motivated theoretical studies on the concentration properties of the induced posteriors. However\, current results are often specific to the selected discrepancy\, and mostly rely on existence arguments which are typically difficult to verify and provide bounds not readily interpretable. We address these issues via a novel bridge between the concept of Rademacher complexity and recent concentration theory for discrepancy-based ABC. This perspective yields a unified and interpretable theoretical framework that relates the concentration of ABC posteriors to the behavior of the Rademacher complexity associated to the chosen discrepancy in the broad class of integral probability semimetrics. This class extends summary-based ABC\, and includes the widely-implemented Wasserstein distance and maximum mean discrepancy (MMD)\, which admit interpretable bounds for the corresponding Rademacher complexity along with constructive sufficient conditions for the existence of such bounds. Therefore\, this unique bridge crucially contributes towards an improved understanding of ABC\, as further clarified through a focus of this theory on the MMD setting and via an illustrative simulation.\n\n\n(Joint work with Daniele Durante and Pierre Alquier)\, Link: https://arxiv.org/abs/2206.06991
URL:https://datascience.unifi.it/index.php/event/disia-seminar-9/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230320T153000
DTEND;TZID=Europe/Rome:20230320T170000
DTSTAMP:20260505T150841
CREATED:20230314T115331Z
LAST-MODIFIED:20230314T115331Z
UID:5253-1679326200-1679331600@datascience.unifi.it
SUMMARY:DiSIA - StEering - itENBIS Seminar
DESCRIPTION:The Department of Statistics\, Computer Science\, Applications DiSIA is happy to invite you to a seminar organized with the StEering – Statistics for Engineering: design\, quality and reliability and itENBIS the Italian group of the European Network for Business and Industrial Statistics \nSpeaker: G. Geoffrey Vining (Virginia Tech\, USA) \nTitle: Shewhart and Profile Monitoring for Industry 4.0 \nAbstract: \nAn important current area within statistical process control/monitoring is profile monitoring\, which assumes that the underlying profile of the data over time is some linear\, nonlinear\, or nonparametric model.  Let y be the characteristic of interest\, and let f(y; θ\, x)  be the underlying model\, where x is the p x 1 vector of variables that explain the behavior of y over time and θ is an unknown vector of model parameters relating  to y.  The standard approach taken by the profile monitoring community uses the following algorithm: \n(1)  Estimate θ for each individual value of y.  Let $\hat{\theta}_{i}$ be the resulting vector of estimates associated with each individual $y_{i}$.  Let $\hat{\theta}_{avg}$ be the average value of the $\hat{\theta}_{i}$s. \n(2)  Estimate the variance of $\hat{\theta}_{i}$ by computing estimates of every variance and covariance involving the components of x. \n(3)  Construct control limits using some variation of Hotelling’s $T^{2}$ statistic. \n\nThis approach historically assumes that p is very small.  There are many serious issues from a linear-models perspective to this approach\, not the least of which is an unnecessary need to estimate $p+\binom{p}{2}$ variance components\, which typically require much larger sample sizes to estimate than averages. \nIndustry 4.0 increases the number of sensors that can provide huge amounts of information in real time.  As a result\, there are opportunities to align data on those variables known to impact a critical quality characteristic to improve the monitoring of that characteristic.  The case study that underlies this talk has very good information on 40 variables known to impact the performance of the critical quality characteristic.  The current profile monitoring approach requires the estimation of 780 variances/covariances\, which is completely unrealistic. \nThis talk outlines how to incorporate the extra information efficiently and effectively.  Ironically\, this approach has its origins in Shewhart’s original ideas underlying control charts.  Seeing the connection is important for advances in statistical process monitoring within Industry 4.0. \nThe seminar will be held in DiSIA ‘s meeting room 205\, and also online. \nIn order to get the link for the webinar you should register by 20 March at noon – sending an email to centro.steering@disia.unifi.it with the subject: “Webinar-Vining”.
URL:https://datascience.unifi.it/index.php/event/disia-steering-itenbis-seminar/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230317T143000
DTEND;TZID=Europe/Rome:20230317T160000
DTSTAMP:20260505T150841
CREATED:20230124T145611Z
LAST-MODIFIED:20230316T083129Z
UID:5001-1679063400-1679068800@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome to another seminar of the D2 Seminar Series of the Florence Center for Data Science!\n\nWe’re happy to host Alberto Cassese and Chiara Bocci from the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence. \nThe Seminar will be held both on-site and online Friday 17th of March 2023\, from 2.30-4 pm.\n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe Seminar will be available also online. Please register here to participate online:\nhttps://us02web.zoom.us/webinar/register/WN_DIlCeuHERia0L7SLrmuWGQ\n\nSpeaker: Alberto Cassese\nTitle: "Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data"\nAbstract: A Bayesian hierarchical mixture regression model is developed for studying the association between a multivariate response\, measured as counts on a set of features\, and a set of covariates. We have available RNASeq and DNA methylation data on breast cancer patients at different stages of the disease. We account for heterogeneity and over-dispersion of count data by considering a mixture of negative binomial distributions and incorporate the covariates into the model via a linear modeling construction on the mean components. Our modeling construction employs selection techniques allowing the identification of a small subset of features that best discriminate the samples\, simultaneously selecting a set of covariates associated to each feature. Additionally\, it incorporates known dependencies into the feature selection process via Markov random field priors. On simulated data\, we show how incorporating existing information via the prior model can improve the accuracy of feature selection. In the case study\, we incorporate knowledge on relationships among genes via a gene network\, extracted from the KEGG database. Our data analysis identifies genes that are discriminatory of cancer stages and simultaneously selects significant associations between those genes and DNA methylation sites. A biological interpretation of our findings reveals several biomarkers that can help to understand the effect of DNA methylation on gene expression transcription across cancer stages.\n\nSpeaker: Chiara Bocci\nTitle: "Sampling design for large-scale geospatial phenomena using remote sensing data" \n\nAbstract: In many fields of application\, it is common to be interested in spatially-related phenomena and\, in particular\, to deal with attributes that\, being defined on continuous spatial domains\, are observed on a fine grid. For such kind of data\, selecting the units spatially well spread over the study area allows to collect more information and consequently provides a better estimation of the population parameters. Moreover\, technological advances have led to a growing availability of ready-to-use low-cost spatial data\, like remote sensing data\, which can be used as auxiliary information in the sampling design development process in addition to the units' spatial location.\nSeveral sampling methods in literature simultaneously implement the selection of well-spread samples and the use of auxiliary variables in the selection process. These designs implicitly assume that\, besides having a spatial pattern\, the study variable shows a relationship with some auxiliary variables. However\, this relationship is never known exactly\, and for large-scale phenomena\, it is often unrealistic to assume a unique relationship that holds everywhere. Therefore\, we propose a two-step sampling design to identify when and how it is useful to exploit the auxiliary information\, in addition to the units' spatial location\, in the second step of the sampling selection process. We evaluate the performance of our proposal through Monte Carlo experiments in two simulation studies: one on pseudo-real datasets and one on synthetic datasets. \nThis talk is based on joint work with Saverio Francini and Emilia Rocco.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-8/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230316T120000
DTEND;TZID=Europe/Rome:20230316T120000
DTSTAMP:20260505T150841
CREATED:20230214T122203Z
LAST-MODIFIED:20230214T122234Z
UID:5093-1678968000-1678968000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Chen-Hao Hsu (University of Bamberg) \nTitle: How women’s employment instability affects birth transitions? The moderating role of family policies in 27 European Countries \nAbstract: \nWhy women in some countries are more likely than others to postpone childbirth when facing employment instability? This study uses 2010-2019 EU-SILC panel data to explore whether women’s unemployment and temporary employment affect their first- and second-birth transitions and how such patterns differ across 27 European countries. Results show that while unemployment and temporary employment generally delay women’s first- and second-birth transition\, such effects vary across European countries and depend on the levels of family policy provisions. More generous family cash benefits may mitigate the negative effects of women’s unemployment on the first birth and temporary employment on the second birth transitions. On the other hand\, the effect of women’s employment instability depends less on the length of paid maternity/parental leaves. Most strikingly\, higher childcare coverage rates are associated with more negative effects of women’s temporary employment on the first birth and unemployment on the second birth transitions.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-8/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230306T110000
DTEND;TZID=Europe/Rome:20230306T120000
DTSTAMP:20260505T150841
CREATED:20230227T110459Z
LAST-MODIFIED:20230227T110459Z
UID:5178-1678100400-1678104000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Nicola Prezza (Università Ca’ Foscari\, Venezia) \nTitle: A Theory of (co-lex) Ordered Regular Languages\n\nAbstract: NFAs are inherently unordered objects\, but they represent regular languages on which one can very naturally define a total order: for example\, the co-lexicographic order in which words are compared alphabetically from right to left. In this talk I will show that interesting things happen when one tries to map this total order to the states of an accepting NFA for the language: the resulting order of the states is a partial pre-order whose width p turns out to be an important parameter for NFAs and regular languages. For example\, take the classic powerset determinization algorithm for converting an NFA of size n into an equivalent DFA: while a straightforward analysis shows that the size of the resulting DFA is at most 2^n\, we prove that it is actually at most (n-p+1)*2^p. This implies that PSPACE-complete problems such as NFA equivalence or universality are actually easy on NFAs of small width p (the case p=1 – total order – is particularly interesting). Another implication of this theory is that we can compress NFAs to just O(log p) bits per transition while supporting fast membership queries in the substring closure of the language.\n\nBiosketch: Nicola Prezza is an Associate professor at Ca’ Foscari University of Venice\, Italy. He received a PhD in Computer Science from the University of Udine in 2017 with a thesis on dynamic compressed data structures. After that\, he worked as post-doc researcher at the universities of Pisa and Copenhagen (DTU) and as Assistant professor at LUISS (Rome). His current research is focused on the relations existing between data structures\, data compression\, and regular languages. In 2018\, he received the “Best Italian Young Researcher in Theoretical Computer Science” award from the Italian chapter of EATCS. In 2021\, he won an ERC starting grant on the topic of regular language compression and indexing.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-10/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230303T143000
DTEND;TZID=Europe/Rome:20230303T160000
DTSTAMP:20260505T150841
CREATED:20230119T092627Z
LAST-MODIFIED:20230127T123533Z
UID:4989-1677853800-1677859200@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome to another seminar of the D2 Seminar Series of the Florence Center for Data Science! \nWe’re happy to host Augusto Cerqua and Marco Letta from the Department of Social Sciences and Economics of Sapienza University of Rome. \nThe Seminar will be held both on-site and online Friday 3rd of March 2023\, from 2.30-4 pm.\n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe Seminar will be available also online. Please register here to participate online:\n\n\nhttps://us02web.zoom.us/webinar/register/WN_GprXsGF7Ti6sQ8uyNfqKpQ\n\nSpeakers: Augusto Cerqua and Marco Letta\nTitle: "Losing control (group)? The Machine Learning Control Method for counterfactual forecasting"\nAbstract: The standard way of estimating treatment effects relies on the availability of a similar group of untreated units. Without it\, the most widespread counterfactual methodologies cannot be applied. We tackle this limitation by presenting the Machine Learning Control Method (MLCM)\, a new causal inference technique for aggregate data based on counterfactual forecasting via machine learning. The MLCM is suitable for the estimation of individual\, average\, and conditional average treatment effects in evaluation settings with short panels and no controls. The method is formalized within the Rubin’s Potential Outcomes Model and comes with a full set of diagnostic\, performance\, and placebo tests. We illustrate our methodology with an empirical application on the short-run impacts of the COVID-19 crisis on income inequality in Italy\, which reveals a striking heterogeneity in the inequality effects of the pandemic across the Italian local labor markets.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-7/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230228T143000
DTEND;TZID=Europe/Rome:20230228T153000
DTSTAMP:20260505T150841
CREATED:20230220T150814Z
LAST-MODIFIED:20230509T163442Z
UID:5095-1677594600-1677598200@datascience.unifi.it
SUMMARY:Young researcher Seminar – Florence Center for Data Science
DESCRIPTION:Welcome to the “Young Researchers Seminar Series“! \n\n\n\nThe Seminar will be held both on-site and online Tuesday 28th of February 2023\, from 2.30 – 3.15 PM.\n\n\n\n\nOur guest will be Riccardo Michielan from University of Twente. \n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe seminar will be available also online. Please register here to participate online:\nhttps://us02web.zoom.us/webinar/register/WN_O2wv8qTvRBWYKcyZw2qOrQ\n\n\nTitle: "Is there geometry in real networks?"\n\nAbstract: \nIn the past decade\, many geometric network models have been developed\, assuming that each vertex is associated a position in some underlying topologic space. Geometric models formalize the idea that similar vertices are naturally likely to connect. Moreover\, these models are able reproduce many properties which are commonly observed in real networks. On the other hand\, it is not always possible to infer the presence of geometry in real networks\, if the edge connections are the only observables. The aim of this talk is to formalize a simple statistic which counts weighted triangles: this statistic discounts the triangles that are almost surely not caused by geometry. Then\, using weighted triangles we will be able to elaborate a robust technique to distinguish whether real networks are embedded in a geometric space or not.
URL:https://datascience.unifi.it/index.php/event/young-researcher-seminar-florence-center-for-data-science/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/08/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230217T143000
DTEND;TZID=Europe/Rome:20230217T160000
DTSTAMP:20260505T150841
CREATED:20230105T101735Z
LAST-MODIFIED:20230207T162800Z
UID:4918-1676644200-1676649600@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome back to a new seminar of the D2 Seminar Series of the Florence Center for Data Science! \nWe are happy to host Elena Stanghellini from the Department of Economics\, University of Perugia and Gianluca Iannucci from the Department of Economics and Management\, University of Florence.\n\nThe Seminar will be held both on-site and online Friday 17th of February 2023\, from 2.30-4 pm.\n\n\n\n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). \nThe Seminar will be available also online. Please register here to participate online:\n\nhttps://us02web.zoom.us/webinar/register/WN_SLRoRT_DQL-nCqVPJb6xLQ\n\n———\n\nSpeaker: Elena Stanghellini – Department of Economics\, University of Perugia \n\nTitle:“Causal effects for binary variables: parametric formulation and sensitivity” \nAbstract: “The talk will focus on causal effects of a treatment on a binary outcome. I shall review some results for one single binary mediator\, and show how these can be extended to the multiple mediator case. Particular focus shall be put on two mediators\, with the aim to isolate sensitivity parameters against the identifying assumptions. If time permits\, extensions to outcome dependent sampling schemes will be also addressed. This talk is based on joint work with: Paolo Berta\, Marco Doretti\, Minna Genbäck\, Martina Raggi.” \nEssential references:  \nDaniel R.\, De Stavola B.\, Counsens S.N. and Vansteelandt S. (2015). Causal Mediation Analysis with Multiple Mediators. Biometrics. \n Stanghellini E. and Doretti M. (2019). On marginal and conditional parameters in logistic regression models. Biometrika.  \nDoretti M.\, Genback M. and Stanghellini E. (2022). Mediation analysis with case-control sampling: identification and estimation in the presence of a binary mediator. Submitted. \n\n\nSpeaker: Gianluca Iannucci – Department of Economics and Management\, University of Florence \n\nTitle:“The interaction between emission tax and insurance in an evolutionary oligopoly” \nAbstract: “It is now commonly accepted that polluting companies deeply contribute to climate change. Environmental losses significantly impact companies’ profits so they have to man- age them through different strategies to survive on the market. The model assumes two types of firms\, polluting and non-polluting\, playing a Cournot-Nash game. Due to the different impact on the environment\, polluting firms have to pay an emission tax. Both types of firms are risk averse and can cover the potential climate change loss choosing insurance coverage. From the comparative static analysis computed at the equilibrium\, it emerges a substitution effect between insurance and taxation. Moreover\, insurance can help clean firms to compete with dirty ones. Finally\, we endogenize the market structure through an evolutionary setting and we perform comparative dynamics to confirm the interplay of taxation and insurance that arise from analytical results in order to nudge an ecological transition.” \nWorking Paper: https://www.disei.unifi.it/upload/sub/pubblicazioni/repec/frz/wpqmss/pdf/wp02_2023.pdf
URL:https://datascience.unifi.it/index.php/event/4918/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230216
DTEND;VALUE=DATE:20230217
DTSTAMP:20260505T150841
CREATED:20230207T121322Z
LAST-MODIFIED:20230209T121849Z
UID:5066-1676505600-1676591999@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:THE SEMINAR HAS BEEN POSTPONED TO A LATER DATE\n  \n\nWe will inform you of updates. \n  \n\nSpeaker: Fulvia Pennoni (Università degli Studi Milano-Bicocca) \nTitle: A causal latent transition model: evidence from evaluating human capital development \nAbstract: \nA new causal transition model with multivariate outcomes to account for unobserved heterogeneity is introduced. It is formulated according to potential versions of discrete latent variables representing the individual characteristic of interest. It can be considered as an alternative method to the Difference-in-Difference approach to evaluate the effect of a policy or treatment with pre- and post-treatment outcomes. Maximum likelihood estimation of the model parameters can be implemented relatively simply. The proposal is illustrated through simulation results and an application concerning the effect of programs developed in Italy on pupils in the 6th and 7th grades in order to improve non-cognitive skills. \n  \nCheck the DiSIA website out!
URL:https://datascience.unifi.it/index.php/event/disia-seminar-7/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230207T163000
DTEND;TZID=Europe/Rome:20230207T183000
DTSTAMP:20260505T150841
CREATED:20230207T171121Z
LAST-MODIFIED:20230207T171121Z
UID:5074-1675787400-1675794600@datascience.unifi.it
SUMMARY:Kick-off meeting of the Master in Data Science and Statistical Learning MD2SL
DESCRIPTION: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.\nHere attached you can find the program of the event. The Kick-off meeting will take place online on Tuesday\, 7th of February 2023 starting from 4.30 pm.\n\n\nYou can register for the event using this link https://us02web.zoom.us/meeting/register/tZwrfumuqzwrH9JcaHgeMnPFyMdb2iRMS8QT \nAfter the registration\, you wilt receive an email with the link to access the zoom meeting.
URL:https://datascience.unifi.it/index.php/event/kick-off-meeting-of-the-master-in-data-science-and-statistical-learning-md2sl-2/
LOCATION:Online
CATEGORIES:Kick-off meeting
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/01/Kick-off-meeting.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230207T100000
DTEND;TZID=Europe/Rome:20230207T110000
DTSTAMP:20260505T150841
CREATED:20230112T154056Z
LAST-MODIFIED:20230119T092838Z
UID:4964-1675764000-1675767600@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Jaesik Jeong (Chonnam National University) \nTitle: Double truncation method for controlling local false discovery rate in case of spiky null \nAbstract: \nMany multiple test procedures\, which control false discovery rate (FDR)\, have been developed to identify some cases (e.g. genes) showing statistically significant difference between groups. Highly spiky null is often reported in some data sets from practice. When it occurs\, currently existing methods have a difficulty of controlling type I error due to the ‘inflated false positives’. No attention has been given to this in previous literature. Recently\, a part of us has encountered the problem in the analysis of SET4 gene deletion data and proposed to model the null with a scale mixture normal distribution. However\, its use is very limited due to the strong assumptions on the spiky peak (e.g. symmetric peak with respect to 0). In this paper\, we propose a new multiple test procedure that can be applied to any type of spiky peak data\, even to the situation with no spiky peak or with more than one spiky peaks. In our procedure\, we truncate the central statistics around 0\, which mainly contributes to the spike of the null\, as well as two tails that are possibly contaminated by the alternative. We name it as ‘double truncation method’. After double truncation\, the null density estimation is done by the doubly truncated maximum likelihood estimator (DTMLE). We numerically show that the proposed method controls the false discovery rate at the aimed level on simulated data. Also\, we apply our method to two real data sets such as SET protein data and peony data. \nThe seminar will be held at 10AM on Tuesday 7th of February 2023\, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). \nCheck the DiSIA website out!
URL:https://datascience.unifi.it/index.php/event/disia-seminar-5/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230203T143000
DTEND;TZID=Europe/Rome:20230203T160000
DTSTAMP:20260505T150841
CREATED:20221222T164451Z
LAST-MODIFIED:20230124T145739Z
UID:4913-1675434600-1675440000@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome back to the new edition of the D2 Seminar Series of the Florence Center for Data Science! \nWe are happy to host Nicola Del Sarto from the Department of Economics and Management\, University of Florence and Andrea Mercatanti from Department of Statistical Sciences\, Sapienza University of Rome. \nThe Seminar will be held both on-site and online Friday 3rd of February 2023\, from 2.30-4 pm.\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe Seminar will be available also online. Please register here to participate online:\nhttps://us02web.zoom.us/webinar/register/WN_mHAHeMr0RkKgv-eXiUsyzQ\n  \n———\n\n\n\nSpeaker: Nicola Del Sarto – Department of Economics and Management\, University of Florence \n\nTitle:“One size does not fit all. Business models heterogeneity among Internet of Things architecture layers” \nAbstract: “The new paradigm known as the Internet of Things (IoT) is expected to have a significant impact on business during the next years\, as it leads to the connection of physical objects and the interaction between the digital and physical worlds. While prior literature addressing the business implications arising from this paradigm has largely considered IoT as an integrated technology\, in this study we examine different components of IoT and assess whether firms concerned with the development of IoT solutions have adopted original business models to exploit the opportunities offered by the specific IoT architecture layer they operate in. In turn\, based on primary survey data collected on a sample of IoT Italy association’s members\, we explore different dimensions of the business model and offer a reinterpretation of the business model Canvas framework adapted to the IoT environment. We show that the specificities of each IoT layer require firms to adopt adhoc business models and focus on different dimensions of the business model Canvas. We believe our research provides some important contributions for both academics and practitioners. For the latter\, we provide a tool useful for making decisions on how to design the business model for IoT applications.”  \nLink: https://doi.org/10.1080/09537325.2021.1921138 \n\n\n\n\n  \n\nSpeaker: Andrea Mercatanti – Department of Statistical Sciences\, Sapienza University of Rome \n\nTitle: “A Regression Discontinuity Design for ordinal running variables: evaluating Central Bank purchases of corporate bonds.” (Joint work with F. Li\, T. Makinen\, A. Silvestrini) \nAbstract: Regression discontinuity (RD) is a widely used quasi-experimental design for causal inference. In the standard RD\, the assignment to treatment is determined by a continuous pretreatment variable (i.e.\, running variable) falling above or below a pre-fixed threshold. Recent applications increasingly feature ordered categorical or ordinal running variables\, which pose challenges to RD estimation due to the lack of a meaningful measure of distance. We proposes an RD approach for ordinal running variables under the local randomization framework. The proposal first estimates an ordered probit model for the ordinal running variable. The estimated probability of being assigned to treatment is then adopted as a latent continuous running variable\nand used to identify a covariate-balanced subsample around the threshold. Assuming local unconfoundedness of the treatment in the subsample\, an estimate of the effect of the program is obtained by employing a weighted estimator of the average treatment effect. Two weighting estimators—overlap weights and ATT weights—as well as their augmented versions are considered. We apply the method to evaluate the causal effects of the corporate sector purchase programme (CSPP) of the European Central Bank\, which involves large-scale purchases of securities issued by corporations in the euro area. We find a statistically significant and negative effect of the CSPP on corporate bond spreads at issuance.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-6/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230126T120000
DTEND;TZID=Europe/Rome:20230126T130000
DTSTAMP:20260505T150841
CREATED:20230119T093626Z
LAST-MODIFIED:20230119T093626Z
UID:4992-1674734400-1674738000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker:Sabrina Molinaro e Elisa Benedetti (Consiglio Nazionale delle Ricerche-CNR) \nTitle: Il monitoraggio delle popolazioni nascoste e la valutazione delle policy in ambito di dipendenze \nAbstract: \nIl focus del seminario consisterà nella descrizione dei metodi di monitoraggio in uso per lo studio delle popolazioni nascoste in ambito di dipendenze da sostanze e comportamentali (es. soggetti con disturbi da uso di sostanze\, giocatori d’azzardo patologici). Verranno descritti gli studi ad hoc sviluppati dal Laboratorio di Epidemiologia e ricerca sui servizi sanitari di IFC-CNR con particolare attenzione a quelli utilizzati per stimarne la prevalenza\, considerando sia indagini ad hoc che studi ecologici che originano dall’integrazione di diverse fonti di dati. Ci si concentrerà poi sulle basi di dati disponibili con l’obiettivo di sviluppare metodiche di analisi nuove per stimare il fenomeno e analizzarne le caratteristiche. Verranno poi presentati alcuni esempi di studi di valutazione delle politiche sanitarie sviluppati attraverso l’uso dei dati prodotti. L’integrazione di diverse fonti di dati per la valutazione di impatto delle politiche pubbliche in ambito di dipendenze è infatti la sfida più recente che il laboratorio ha intrapreso. L’obiettivo è sviluppare una comunicazione multidisciplinare efficace fra il mondo dell’epidemiologia e quello di altre discipline\, quali statistica sociale ed economia\, al fine di offrire elementi per lo sviluppo di politiche pubbliche evidence-based. \nThe seminar will be held at Noon on Thursday 26th of January 2023\, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). \nCheck the DiSIA website out!
URL:https://datascience.unifi.it/index.php/event/disia-seminar-6/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230120T143000
DTEND;TZID=Europe/Rome:20230120T160000
DTSTAMP:20260505T150841
CREATED:20221222T164110Z
LAST-MODIFIED:20230110T171027Z
UID:4911-1674225000-1674230400@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome back to the new edition of the D2 Seminar Series of the Florence Center for Data Science! \nWe are happy to host Giacomo Toscano from the Department of Economics and Management\, University of Florence and Gabriele Fiorentini from the Department of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence. \nThe Seminar will be held both on-site and online Friday 20th of Jenuary 2023\, from 2.30-4 pm.\n\n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe Seminar will be available also online. Please register here to participate online: https://us02web.zoom.us/webinar/register/WN_mEFLIP8NRFeKE8mQh8BcNw \n\n\n\n———\n\nSpeaker: Giacomo Toscano – Department of Economics and Management\, University of Florence  \n\nTitle: “Central limit theorems for the Fourier-transform estimator of the volatility of volatility” \nAbstract: “We study the asymptotic normality of two feasible estimators of the integrated volatility of volatility based on the Fourier methodology\, which does not require the pre-estimation of the spot volatility. We show that the bias-corrected estimator reaches the optimal rate n1/4\, while the estimator without bias correction has a slower convergence rate and a smaller asymptotic variance. Additionally\, we provide simulation results that support the theoretical asymptotic distribution of the rate-efficient estimator and show the accuracy of the latter in comparison with a rate-optimal estimator based on the pre-estimation of the spot volatility. Finally\, using the rate-optimal Fourier estimator\, we reconstruct the time series of the daily volatility of volatility of the S&P500 and EUROSTOXX50 indices over long samples and provide novel insight into the existence of stylized facts about the volatility of volatility dynamics.” \nLink: https://doi.org/10.1093/jjfinec/nbac035 \nSpeaker: Gabriele Fiorentini – Department of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence \n\nTitle: “Specification tests for non-Gaussian structural vector autoregressions” \nAbstract: We propose specification tests for independent component analysis and structural vector autoregressions that assess the assumed cross-sectional independence of the non-Gaussian shocks. Our tests effectively compare their joint cumulative distribution with the product of their marginals at discrete or continuous grids of values for its arguments\, the latter yielding a consistent test. We explicitly consider the sampling variability from using consistent estimators to compute the shocks. We study the finite sample size of our tests in several simulation exercises\, with special attention to resampling procedures. We also show that they have non-negligible power against a variety of empirically plausible alternatives.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-5/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230117T120000
DTEND;TZID=Europe/Rome:20230117T130000
DTSTAMP:20260505T150841
CREATED:20230112T153503Z
LAST-MODIFIED:20230112T153739Z
UID:4961-1673956800-1673960400@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speakers: Nedka Nikiforova\, Valentina Tocchioni\, Pamela Vignolini (DiSIA – Università degli Studi di Firenze) \nTitle: \nNedka Nikiforova: ​Design of experiments for technology and for consumers’ preferences\nValentina Tocchioni: ​A snapshot of my research: from childlessness to higher education research\nPamela Vignolini: Crocus sativus L. flowers valorisation as sources of bioactive compounds \nAbstract: \nNedka Nikiforova:\nDesign of experiments (DoE) is a wide and fundamental methodology of the statistics theory. It plays a relevant role to improve and solve issues in the fields of technology and consumers’ behaviour. In this seminar\, I will present a general overview of my research related to DoE. First\, I will focus on a study related to a split-plot design in the technological field. Following\, I will address computer experiments and Kriging modelling to solve complex engineering and technological issues\, for which physical experimentation could be too costly\, or in certain cases\, impossible to be performed. Lastly\, I will present my research related to innovative approaches to build optimal designs for the technological field\, and for choice experiments to analyze consumers’ preferences. A further research topic related to the field of quantitative marketing will be also briefly outlined during the talk.\nValentina Tocchioni:\nDuring this seminar I will illustrate a general overview of my past research. In particular\, I will concentrate on four socio-demographic topics I have been dealing with\, such as childlessness\, family dynamics – family formation\, fertility\, and divorce – and their interrelationship with economic uncertainty\, sexual behaviours\, and higher education in terms of students’ university tracks and PhD students’ and graduates’ work trajectories. Most of the presentation will be based on previous published research\, but some hints of actual and future directions of my research will be given.\nPamela Vignolini:\nThe application of circular economy principles is of particular interest for the agricultural and agri-food sector\, given the large amount of waste matrix of some plant species. In recent decades the attention towards the cultivation of saffron (Crocus sativus L.) has been rediscovered. The saffron produced from dried stigmas of Crocus sativus L. has been known since ancient times for its numerous therapeutic properties. The spice is obtained from the stigmas of the flowers\, while petals and stamens are 90% waste material. The recovery of the flowers\, considering the considerable amount of polyphenols with high antioxidant activity present in this matrix allows its use for innovative purposes in different product sectors such as foods\, cosmetics and biomedical applications. In this context\, the present work evaluated the polyphenol content in flowers of C. sativus grown in Tuscany\, in order to characterize this product from a qualitative-quantitative point of view for various product sectors. The quali-quantitative analysis of the extracts was carried out by HPLC/DAD/MS analysis. Given the potential of this matrix\, another aspect of the research consists in evaluating the possible tumor growth inhibition activity on bladder cancer cell lines by the extracts of petals. \nThe seminar will be held at noon on Tuesday 17th of January 2023\, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). \nCheck the DiSIA website out!
URL:https://datascience.unifi.it/index.php/event/disia-seminar-4/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230112T120000
DTEND;TZID=Europe/Rome:20230112T130000
DTSTAMP:20260505T150841
CREATED:20221114T092148Z
LAST-MODIFIED:20230105T101451Z
UID:4851-1673524800-1673528400@datascience.unifi.it
SUMMARY:DISIA Seminar
DESCRIPTION:Speaker: Fulvia Mecatti (Università degli Studi di Milano-Bicocca) \nTitle: A fresh look to Multiple Frame Surveys for a multi data source world \nAbstract: Multiple Frame (MF) Surveys have been around since the 1960s as an effective tool to deal with traditional challenges in sample survey: to reduce costs and improve population coverage\, to cope against “imperfect” (or even non-existent) sampling frame for not being able to directly representing the target population\, and to increase sample size for sub-populations of interest. In recent years multiple-frame surveys are increasingly considered to deal with newer challenges and emerging needs in our multi data source world. In this seminar a fresh look to MF surveys will be given and to their potential to serve as an organising framework to untangle modern complex multi-structured data problems. Building upon the multiplicity approach as a simplified\, unifying and principled approach to MF estimation\, we will illustrate how the MF paradigm and reasoning can help with the general issue of integrate data from different sources\, and in particular to produce good estimates upon complex panel data from large scale longitudinal surveys such as SHARE. \nThe seminar will be held on Thursday 12th of January 2023\, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). \nCheck the DiSIA website out! \n  \n 
URL:https://datascience.unifi.it/index.php/event/disia-seminar-3/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221222T110000
DTEND;TZID=Europe/Rome:20221222T133000
DTSTAMP:20260505T150841
CREATED:20221206T144946Z
LAST-MODIFIED:20230118T155634Z
UID:4888-1671706800-1671715800@datascience.unifi.it
SUMMARY:DiSIA Xmas Lecture
DESCRIPTION:The Department of Statistics\, Computer Science\, Applications “G. Parenti” together with the Florence Center for Data Science is glad to invite you to the Christmas Seminar: \nChristmas Lecture \nDecember 22\, 2022 – 11:00 am\nRoom 327\, Centro Didattico Morgagni\, Viale Morgagni\, 40\, Firenze \nSpeaker: Fabrizia Mealli\n(Department of Statistics\, Computer Science\, Applications “G. Parenti\, University of Florence) \nTitle: Causal inference: past\, present\, future \nYou can download the slides of the lecture here.
URL:https://datascience.unifi.it/index.php/event/disia-xmas-lecture/
LOCATION:Plesso didattico Morgagni\, Viale Morgani 40\, Firenze\, 50134\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/12/logo-con-cappello.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221221T120000
DTEND;TZID=Europe/Rome:20221221T130000
DTSTAMP:20260505T150841
CREATED:20221114T092048Z
LAST-MODIFIED:20221219T083307Z
UID:4849-1671624000-1671627600@datascience.unifi.it
SUMMARY:DISIA Seminar
DESCRIPTION:Speaker: Moreno Mancosu (Università di Torino) \nTitle: Socio-demographic cues and willingness to talk about politics: an experimental approach \nAbstract: Recently\, the debate around political discussions argued that people tend to use socio-political cues to indirectly identify the partisanship of their discussants (by relying on their lifestyle): when exposed to a lifestyle stereotype of a right-wing/left-wing person (such as a latte drinker/a pickup truck driver in the US)\, people tend to avoid them\, as they represent an outgroup stereotype. An alternative approach (the social distance/homophily argument)\, states that people generally look for homophily – namely\, (not necessarily political) interactions with people who are similar to them in terms of socio-demographic characteristics. The present paper aims at combining the homophily/political discussions arguments\, by testing whether the sole socio-demographic differences between people lead to higher/lower propensities to talk about politics. In other words\, we ask ourselves whether people are able to indirectly “guess” another individual’s current affairs views by just relying on their socio-demographic properties. To do so\, we use a CAWI survey administered via Pollstar\, an opt-in community managed by academics\, and we design a vignette experiment. In the experiment\, respondents (n~2\,000) are requested to declare the likelihood of talking about current affairs with a person having specific characteristics. The hypothetical discussant presents randomized socio-demographic characteristics (age\, gender\, income\, and educational level). The randomized characteristics are successively coupled with the bogus respondent’s characteristics\, to provide measures of social distance between the respondent and the hypothetical discussant. We believe that the results of the experiment will shed light on the relationship between homophily and political behavior. \nThe seminar will be held on Wednesday 21st of December 2022\, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). \nCheck the DiSIA website out!
URL:https://datascience.unifi.it/index.php/event/disia-seminar-2/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221213T080000
DTEND;TZID=Europe/Rome:20221216T170000
DTSTAMP:20260505T150841
CREATED:20221206T145255Z
LAST-MODIFIED:20230306T115931Z
UID:4891-1670918400-1671210000@datascience.unifi.it
SUMMARY:IMS International Conference on Statistics and Data Science (ICSDS)
DESCRIPTION:In response to the call from the 2021 IMS Survey report to expand membership from emerging areas of data science\, underrepresented groups and from regions outside of North America\, the IMS Council has just approved the launch of annual IMS International Conference on Statistics and Data Science (ICSDS).T \nhe first 2022 IMS International Conference on Statistics and Data Science (ICSDS) will be a four-day conference to be held in Florence\, Italy in December 2022\, organized by IMS with the collaboration of The Florence center for Data Science and the Department of Statistics\, Computer Science\, Application of the University of Florence. \nIts objective is to bring together researchers in statistics and data science from academia\, industry and government in a stimulating environment to exchange ideas on the developments of modern statistics\, machine learning theory\, methods and applications in data science. The conference will consist of several plenary sessions\, and about 50 invited\, contributed and poster sessions; with a portion of invited sessions designated for young researchers. The expected size of the conference is 300-400 participants. The conference will present topics with broad appeal\, including: deep learning\, causal inference\, precision medicine\, unsupervised\, semi-supervised and supervised learning\, nonparametrics\, Bayesian statistics\, environment statistics\, network and graphic models\, recommender systems\, bioinformatics\, high-dimensional data\, functional data\, genomics\, drug discovery\, statistics computations\, imaging\, intrusion and fraud detection\, etc. \n\nVisit the official website: https://sites.google.com/view/icsds2022/ \nThe conference will consist of 4 plenary sessions : \nEmmanuel Candès – Stanford University\, Guido Imbens – Stanford University\, Susan Murphy – Harvard University\, Sylvia Richardson –University of Cambridge. \nMoreover there will be about 50 invited\, contributed and also a poster session (check the program book for all information here) \nYoung researchers are particularly encouraged to participate\, as a portion of the invited sessions will be designated for young researchers. \nSave the date and see you in Florence in December 2022! \nRegina Liu (IMS Past-President) and Annie Qu (IMS Program Secretary) \nProgram Co-chairs
URL:https://datascience.unifi.it/index.php/event/ims-international-conference-on-statistics-and-data-science-icsds/
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/06/Immagine-2022-06-01-115403.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221202T120000
DTEND;TZID=Europe/Rome:20221202T133000
DTSTAMP:20260505T150841
CREATED:20221011T141105Z
LAST-MODIFIED:20221114T091402Z
UID:4695-1669982400-1669987800@datascience.unifi.it
SUMMARY:DISIA Welcome Seminar
DESCRIPTION:Welcome seminar: Alberto Cassese\, Giulia Cereda\, Cecilia Viscardi \nThe seminar will be held on Friday 2nd December 2022\, in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).  \n——————– \nSpeaker: GIULIA CEREDA \nTitle: Comparing different methods for the rare type match problem \nAbstract: A classical problem of forensic statistics is that of evaluating a match between a DNA profile found on the crime scene and a suspect’s DNA profile\, in the light of the two competing hypotheses (the crime stain has been left by the suspect or by another person).\nThe evaluation is based on the calculation of the likelihood ratio\, but the likelihood of the data under the competing hypotheses is unknown. The “rare type match problem” is the situation in which the matching DNA profile is not in the database of reference\, hence it is difficult to have an idea of its frequency in the population. In the last years\, I have proposed and analyzed different models and methods (frequentist\, Bayesian\, parametric and non-parametric) to evaluate the LR for the rare type match case. They are based on quite diverse assumptions and data reduction\, and deserve a comparative framework to compare such contributions both theoretically\, discussing their rationales\, and empirically\, by assessing their performances through some validation experiments and appropriate metrics. This is realized by tailoring to the rare type match problem the  ECE (Empirical Cross Entropy) plots\, a graphical tool based on information theory that allows to study the accuracy of each method according to their discrimination power and calibration. \n*******\nSpeaker: CECILIA VISCARDI \nTitle: Approximate Bayesian computation: methodological developments and novel applications \nAbstract: Approximate Bayesian computation (ABC) is a class of simulation-based methods for drawing Bayesian inference when the likelihood function is unavailable or computationally demanding to evaluate. ABC methods dispense with exact likelihood computation as they only require the availability of a simulator model — a computer program which takes parameter values as input\, performs stochastic calculations\, and returns simulated data.  In the simplest form\, ABC algorithms draw parameter proposals from the prior distribution\, run the simulator with those values as inputs\, and retain proposals such that the simulated data are sufficiently close to the observed data. Despite ABC algorithms having had a tremendous evolution in the last 20 years\, most of them still suffer from shortcomings related to i) the waste of computational resources due to the typical rejection step; ii) the inefficient exploration of the parameter space; iii) the computational cost of the simulator. During this talk\, I will outline some methodological developments motivated by the above mentioned problems\, as well as possible applications in the civil engineering\, epidemiological and forensic fields. \n*******\nSpeaker: ALBERTO CASSESE \nTitle: Long story short: 11 years of (my) research summarized in 30 minutes \nAbstract: In this welcome seminar I will show a general overview of the research projects I worked on (and I am still working on). In the first half\, I will focus on my work in the field of Bayesian analysis\, specifically on methods for the analysis of high dimensional data and Bayesian non-parametrics. In the second half I will focus on more recent work on studying two-way interaction by means of biclustering and optimization of research study designs in reliability and agreement studies. \n 
URL:https://datascience.unifi.it/index.php/event/disia-welcome-seminar-2/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221125T143000
DTEND;TZID=Europe/Rome:20221125T160000
DTSTAMP:20260505T150841
CREATED:20221025T084854Z
LAST-MODIFIED:20221116T115743Z
UID:4809-1669386600-1669392000@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome back to the new edition of the D2 Seminar Series of the Florence Center for Data Science! \nWe are happy to host Monica Bianchini from the Department of Information engineering and mathematics of the University of Siena and Giulio Bottazzi from the Institute of Economics of the Sant’Anna School of Advanced Studies of Pisa. \nThe Seminar will be held both on-site and online Friday 25th of November 2022\, from 2.30-4 pm.\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe Seminar will be available also online. Please register here to participate online:\nhttps://us02web.zoom.us/webinar/register/WN_XdDW5nAKQOOtuzTSB-DJfw\n\n———\n\n\n\nSpeaker: Monica Bianchini – Department of Information Engineering and Mathematics\, University of Siena\n\nTitle: A gentle introduction to Graph Neural Networks\nAbstract: This talk will introduce Graph Neural Networks\, which are a powerful deep learning tool for processing graphs in their entirety. Indeed\, considering graphs as a whole allows to take into account the essential sub-symbolic information contained in the relationships described by the arcs (as well as the symbolic information collected in the node labels)\, also enabling alternative learning frameworks based on information diffusion. Some real-world applications\, in which graphs are the most natural way to represent data\, will be presented\, ranging from image processing to the prediction of drug side-effects.\n\n\n \n\nSpeaker: Giulio Bottazzi – Institute of Economics\, Sant’Anna School of Advanced Studies of Pisa\n\nTitle: Persistence in firm growth: inference from conditional quantile transition matrices\nAbstract: We propose a new methodology to assess the degree of persistence in firm growth\, based on Conditional Quantile Transition Probability Matrices (CQTPMs) and well-known indexes of intra-distributional mobility. Improving upon previous studies\, the method allows for exact statistical inference about TPMs properties\, at the same time controlling for spurious sources of persistence due to confounding factors such as firm size\, and sector-\, country- and time-effects. We apply our methodology to study manufacturing firms in the UK and four major European economies over the period 2010-2017. The findings reveal that\, despite we reject the null of fully independent firm growth process\, growth patterns display considerable turbulence and large bouncing effects. We also document that productivity\, openness to trade\, and business dynamism are the primary sources of firm growth persistence across sectors. Our approach is flexible and suitable to wide applicability in firm empirics\, beyond firm growth studies\, as a tool to examine persistence in other dimensions of firm performance.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-4/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221115T120000
DTEND;TZID=Europe/Rome:20221115T130000
DTSTAMP:20260505T150841
CREATED:20221114T091743Z
LAST-MODIFIED:20221114T091743Z
UID:4847-1668513600-1668517200@datascience.unifi.it
SUMMARY:DISIA Seminar: Connectivity Problems on Temporal Graphs
DESCRIPTION:Title: Connectivity Problems on Temporal Graphs \nSpeaker: Ana Shirley Ferreira da Silva (Universidade Federal do Ceará UFC\, Brasil & visiting DISIA) \nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 \nAbstract:A temporal graph is a graph that changes in time\, meaning that\, at each timestamp\, only a subset of the edges is active. These structure models all sorts of real-life situations\, from social networks to public transportation\, having also been used for contact tracing during the COVID pandemic. Despite its broad applicability\, and despite being around for more than two decades\, only recently has this structure received more attention from the community. In this talk\, we will discuss how to bring some connectivity concepts to the temporal context\, and we will learn about the state of the art of complexity results of the related problems. Additionally\, we will see various possible adaptations of Menger’s Theorem\, only a few of which also hold on temporal graphs. \nBiosketch: Ana Silva is Associate Professor at the Mathematics Department of Universidade Federal do Ceará\, Brazil\, and is currently a Visiting Professor at the Universitá degli Studi di Firenze (Italy). She obtained her PhD degree in Mathematics and Computer Science by the Université de Grenoble (France) in November 2010 under the supervision of Frédéric Maffray. She was head of the Math Department at UFC from 2013 to 2015\, and was a member of the Gender Committee of the Brazilian Mathematics Society from 2020 to 2021. In 2014\, she received the L’Óreal/UNESCO/ABC Prize for Women in Science\, and in 2021 was elected affiliated member of the ABC (Academia Brasileira de Ciências)\, a position that she will occupy until December 2025. Her work concerns mainly graph problems\, in particular coloring problems and convexity problems\, and lately she has been interested in Temporal Graphs.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-connectivity-problems-on-temporal-graphs/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221111T143000
DTEND;TZID=Europe/Rome:20221111T160000
DTSTAMP:20260505T150841
CREATED:20221025T084356Z
LAST-MODIFIED:20221104T101801Z
UID:4807-1668177000-1668182400@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome back to the new edition of the D2 Seminar Series of the Florence Center for Data Science! \nWe are happy to host Gianmarco Bet from the Department of Mathematics and Computer Science “Ulisse Dini” and Agnese Panzera from the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence. \nGianmarco Bet will present a seminar on “Detecting anomalies in geometric networks” and Agnese Panzera will present a seminar on “Density estimation for circular data observed with errors“\n\n  \nThe Seminar will be held both on-site and online Friday 11th of November 2022\, from 2.30-4 pm.\n\n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59). \nThe Seminar will be available also online. Please register here to participate online:\nhttps://us02web.zoom.us/webinar/register/WN_c7BZb5pyT_OklsBsYIELwA\n\n\n\n\n——-\n\n\n\nSpeaker: Gianmarco Bet – Department of Mathematics and Computer Science “Ulisse Dini”\, University of Florence\n\nTitle:  Detecting anomalies in geometric networks\nAbstract: Recently there has been an increasing interest in the development of statistical techniques and algorithms that exploit the structure of large complex-network data to analyze networks more efficiently. For this talk\, I will focus on detection problems. In this context\, the goal is to detect the presence of some sort of anomaly in the network\, and possibly even identify the nodes/edges responsible. Our work is inspired by the problem of detecting so-called botnets. Examples are fake user profiles in a social network or servers infected by a computer virus on the internet. Typically a botnet represents a potentially malicious anomaly in the network\, and thus it is of great practical interest to detect its presence and\, when detected\, to identify the corresponding vertices. Accordingly\, numerous empirical studies have analyzed botnet detection problems and techniques. However\, theoretical models and algorithmic guarantees are missing so far. We introduce a simplified model for a botnet\, and approach the detection problem from a statistical perspective. More precisely\, under the null hypothesis we model the network as a sample from a geometric random graph\, whereas under the alternative hypothesis there are a few botnet vertices that ignore the underlying geometry and simply connect to other vertices in an independent fashion. We present two statistical tests to detect the presence of these botnets\, and we show that they are asymptotically powerful\, i.e.\, they correctly distinguish the null and the alternative with probability tending to one as the number of vertices increases. We also propose a method to identify the botnet vertices. We will argue\, using numerical simulations\, that our tests perform well for finite networks\, even when the underlying graph model is slightly perturbed. Our work is not limited in scope to botnet detection\, and in fact is relevant whenever the nature of the anomaly to be detected is a change in the underlying connection criteria.\nBased on joint work with Kay Bogerd (TU/e)\, Rui Pires da Silva Castro (TU/e) and Remco van der Hofstad (TU/e).\n\n\n\n \n\nSpeaker: Agnese Panzera – Department of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence \n\nTitle: Density estimation for circular data observed with errors\nAbstract: Density estimation represents a core tool in statistics for both exploring data structures and as a starting task in more challenging problems. We consider nonparametric estimation of circular densities\, which are periodic probability density functions having the unit circle as their support. Starting from the basic idea of kernel estimation of circular densities\, we present some related methods for the case where data are observed with errors.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-3/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221110T120000
DTEND;TZID=Europe/Rome:20221110T130000
DTSTAMP:20260505T150841
CREATED:20221011T141341Z
LAST-MODIFIED:20221011T141402Z
UID:4697-1668081600-1668085200@datascience.unifi.it
SUMMARY:DISIA Seminar: Finding the needle by modelling the haystack: pulmonary embolism in an emergency patient with cardiorespiratory manifestations
DESCRIPTION:Title: Finding the needle by modelling the haystack: pulmonary embolism in an emergency patient with cardiorespiratory manifestations \nSpeaker: Davide Luciani (IRCCS Istituto di Ricerche Farmacologiche Mario Negri\, Milano) \nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 \nAbstract: A Bayesian Network (BN) was developed to perform a diagnosis covering 129 acute cardiopulmonary disorders in patients admitted to emergency departments\, given an observable domain of 235 clinical\, laboratory and imaging manifestations. Once the network was given a causal structure\, the BN inferences could be deemed aligned to a medical reasoning framed in hundreds of pathophysiological and pathogenic related events. The structure was anticipated by experts in pneumology\, cardiology and coagulations disorders\, while 1\,417 model parameters were estimated\, via Markov chain Monte Carlo\, from data of 282 records collected at the main hospital of Bergamo. The BN structure was refined until precision of diagnostic inferences improved\, as long as medical literature supported any enforced structural change. Diagnostic performance was assessed by looking at the precision of predictions concerning six diagnoses\, given testing findings collected from 284 records in six hospitals not including the hospital of Bergamo. Thanks to its large-size domain\, the model addresses rare disorders even in patients complaining of generic symptoms. However\, the size and the complexity of the model involved serious methodological challenges: to what extent causal knowledge was useful to exploit data as noisy but rich of medical information as clinical records? Was the BN causal structure faithful to the process underlying the generation of sampled data? The main lessons learned from answering these questions are introduced by taking an interdisciplinary perspective\, at the intersection of knowledge engineering\, evidence-based medicine\, and Bayesian statistics.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-finding-the-needle-by-modelling-the-haystack-pulmonary-embolism-in-an-emergency-patient-with-cardiorespiratory-manifestations/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2019/12/logo-DiSIA.png
END:VEVENT
END:VCALENDAR