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DTSTART;TZID=Europe/Rome:20230228T143000
DTEND;TZID=Europe/Rome:20230228T153000
DTSTAMP:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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:20260508T114531
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
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221103T110000
DTEND;TZID=Europe/Rome:20221103T123000
DTSTAMP:20260508T114531
CREATED:20221011T140344Z
LAST-MODIFIED:20221011T140344Z
UID:4691-1667473200-1667478600@datascience.unifi.it
SUMMARY:DISIA Welcome Seminar
DESCRIPTION:– Next WELCOME SEMINAR –\nThursday 3rd of November 2022\, 11.00 am \n\nSpeaker: Francesco Sera & Gianluca Severi.\nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59\n\n\nSpeaker: Francesco Sera.\nTitle: Extended two-stage designs for the evaluation of the short-term health effects of environmental hazards.\nAbstract: The two-stage design has become a standard tool in environmental epidemiology to model short-term effects with multi-location data giving valuable information for preventive public health strategies. In the seminar\, I illustrate multiple design extensions of the classical two-stage method. These are based on improvements of the standard two-stage meta-analytic models along the lines of linear mixed-effects models\, by allowing location-specific estimates to be pooled through flexible fixed and random-effects structures. This permits the analysis of associations characterised by combinations of multivariate outcomes\, hierarchical geographical structures\, repeated measures\, and/or longitudinal settings. The design extensions will be illustrated in examples using data collected by the Multi-Country Multi-City research network.\nBiosketch: Francesco Sera is a Research Fellow at the University of Florence. Francesco is a statistician and epidemiologist and he has worked on several epidemiological projects with more than 180 publications. His current research interests focus on short-term health effects of environmental exposures such as temperature and air pollution\, and related methodological aspects\, such as time series models\, and pooling results from multi-centre studies. Working with colleagues of the Multi- Country Multi-City MCC Collaborative Research Network contributed to increasing the evidence on environmental exposure health-impact with papers published in high-impact journals.\n\n\nSpeaker: Gianluca Severi\nTitle: New approaches to the study of individual susceptibility\, lifestyle and the environment and their role in human health.\nAbstract: The term exposome has been coined to describe the multiple\, often interacting dimensions of our behaviours as well as the environmental and socio-economic context in which we live. The concept of human exposome may be helpful to build more realistic models to answer key questions such as how diet\, physical activity and environmental exposures affect our health but the implementation of the  “exposome approach” poses several challenges. In this seminar I will discuss some of these challenges using examples of research I conduct with my team on the human exposome and its influence on health and disease\, focusing in particular on chronic diseases such as cancer. In particular\, I will draw examples from studies nested within prospective cohorts such as the Melbourne Collaborative Cohort Study\, the familial E3N-E4N cohort\, EPIC and Constances in which we use concepts such as exposome\, biological fingerprint and molecular signature to better characterize risk or protective behaviours\, quantify environmental exposures\, explore pathological mechanisms and improve risk prediction.\nBiosketch: I am an Associate Professor of biostatistics and epidemiology at the University of Florence and a Research Director at Inserm where I lead the  “Exposome and Heredity” group (CESP U1018). After an initial career as biostatistician at the European Institute of Oncology in Milan\, I completed a PhD in cancer studies at the University of Birmingham and pursued a career as a molecular epidemiologist working mainly on cancer. After almost 10 years in Melbourne\, Australia as Deputy Director of the Cancer Epidemiology Centre of the Cancer Council Victoria\, in 2013 I moved back to Europe to take up the role of Director of the Italian Institute for Genomic Medicine in Turin (aka HuGeF) and to further its development before moving to my current research and teaching positions. My main research interest is the use of innovative tools to study the exposome and its related biological fingerprints (e.g. epigenetic marks) and to identify the key physiological systems and health outcomes affected by the exposome.
URL:https://datascience.unifi.it/index.php/event/disia-welcome-seminar/
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:20221028T143000
DTEND;TZID=Europe/Rome:20221028T160000
DTSTAMP:20260508T114531
CREATED:20221011T142049Z
LAST-MODIFIED:20221025T084951Z
UID:4706-1666967400-1666972800@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 Alessio Brini from Duke University Pratt School of Engineering and Matteo Pedone from the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence \nAlessio Brini will present a seminar on “Reinforcement Learning Policy Recommendation for Interbank Network Stability” and Matteo Pedone will present a seminar on “A Bayesian nonparametric approach to personalized treatment selection“\n\n  \nThe Seminar will be held both on-site and online Friday 28th of October 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_IxNMe0XmThisZx4DmsDOpA\n\n\n  \n——-\n\n\n\nSpeaker: Alessio Brini from Duke University Pratt School of Engineering \n\nTitle: Reinforcement Learning Policy Recommendation for Interbank Network Stability (joint work with Gabriele Tedeschi and Daniele Tantari)\nAbstract:  In this paper\, we analyze the effect of a policy recommendation on the performance of an artificial interbank market. Financial institutions stipulate lending agreements following a public recommendation and their individual information. The former is modeled by a reinforcement learning optimal policy that maximizes the system’s fitness and gathers information on the economic environment. The policy recommendation directs economic actors to create credit relationships through the optimal choice between a low interest rate or a high liquidity supply. The latter\, based on the agents’ balance sheet\, allows to determine the liquidity supply and interest rate that the banks optimally offer their clients within the market. Thanks to the combination between the public and the private signal\, financial institutions create or cut their credit connections over time via a preferential attachment evolving procedure able to generate a dynamic network. Our results show that the emergence of a core-periphery interbank network\, combined with a certain level of homogeneity in the size of lenders and borrowers\, is essential to ensure the system’s resilience. Moreover\, the optimal policy recommendation obtained through reinforcement learning is crucial in mitigating systemic risk. \n\n\n \n\nSpeaker: Matteo Pedone from the University of Florence \n\nTitle: A Bayesian nonparametric approach to personalized treatment selection\nAbstract: Precision medicine is an approach to disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics\, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies\, via predictive inference\, which one among a set of therapeutic strategies is better suited for a new patient. The proposed method is fully model-based\, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures\, and belongs to the class of product partition models with covariates\, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by large heterogeneity among the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally\, being model-based\, the approach allows estimating clusters’ specific random effects and then identifying patients that are more likely to benefit from personalized treatment.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-2/
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:20221027T170000
DTEND;TZID=Europe/Rome:20221027T180000
DTSTAMP:20260508T114531
CREATED:20221011T141924Z
LAST-MODIFIED:20221024T141407Z
UID:4702-1666890000-1666893600@datascience.unifi.it
SUMMARY:Online Open Day Master MD2SL
DESCRIPTION:The Master in Data Science and Statistical Learning (MD2SL) of the University of Florence and the IMT School Alti Studi Lucca invites you to the online open day of the Master which will be held on 27 October 2022 from 17.00 to 18.00 on Zoom. There will be a short presentation of the Master and then we will open to any questions and doubts.\n\nIf you are interested you can register for the event using this link.
URL:https://datascience.unifi.it/index.php/event/online-open-day-master-md2sl/
LOCATION:Online
CATEGORIES:open day
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/10/ONLINE-OPEN-DAY-2022.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221024T110000
DTEND;TZID=Europe/Rome:20221024T120000
DTSTAMP:20260508T114531
CREATED:20221011T141542Z
LAST-MODIFIED:20221011T141542Z
UID:4700-1666609200-1666612800@datascience.unifi.it
SUMMARY:DISIA Seminar
DESCRIPTION:Nicola Prezza (Università Ca’ Foscari\, Venezia) \nMore info will be proveded soon.
URL:https://datascience.unifi.it/index.php/event/disia-seminar/
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:20221020T143000
DTEND;TZID=Europe/Rome:20221020T160000
DTSTAMP:20260508T114531
CREATED:20221011T140926Z
LAST-MODIFIED:20221011T140926Z
UID:4693-1666276200-1666281600@datascience.unifi.it
SUMMARY:DISIA Seminar: Hierarchical normalized finite point process: predictive structure and clustering
DESCRIPTION:Title: Hierarchical normalized finite point process: predictive structure and clustering \nSpeaker: Raffaele Argiento (Università degli Studi di Bergamo) \nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 \nAbstract: Almost surely discrete random probability measures have received close attention in the Bayesian nonparametric community. They have been used to model populations of individuals or latent parameters (in the mixture model setting) composed of unfixed species with unknown proportions. In this framework\, data are usually assumed to be exchangeable. However\, the latter assumption is not appropriate when data are divided in multiple groups which may share the same species. If so\, partially exchangeability accommodates the dependence across populations.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-hierarchical-normalized-finite-point-process-predictive-structure-and-clustering/
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:20221014T140000
DTEND;TZID=Europe/Rome:20221014T153000
DTSTAMP:20260508T114531
CREATED:20220428T090832Z
LAST-MODIFIED:20221010T082435Z
UID:4166-1665756000-1665761400@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 Claudio Durastanti from the Department of Basic and Applied Sciences for Engineering (SBAI) of Sapienza University and Cecilia Viscardi from the Department of Statistics\, Computer Science\, Applications “G. Parenti” from the University of Florence \nClaudio Durastanti will present a seminar on “Spherical Poisson Waves” and Cecilia Viscardi will present a seminar on “Likelihood-free Transport Monte Carlo“\n \nThe Seminar will be held both on-site and online Friday 14th of October 2022\, from 2-3.30 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:\n\nhttps://us02web.zoom.us/webinar/register/WN_JHEtiFMQQD69OsLbxMBtTg\n\n\n\n——-\n\n\n\nSpeaker: Claudio Durastanti from Sapienza University \n\nTitle: Spherical Poisson Waves\nAbstract: During this talk\, we will discuss a model of Poisson random waves defined in the sphere\, to study Quantitative Central Limit Theorems when both the rate of the Poisson process (that is\, the expected number of the observations sampled at a fixed time) and the energy (i.e.\, frequency) of the waves (eigenfunctions) diverge to infinity. We consider finite-dimensional distributions\, harmonic coefficients and convergence in law in functional spaces\, and we investigate carefully the interplay between the rates of divergence of eigenvalues and Poisson governing measures.\n\n\n \n\nSpeaker: Cecilia Viscardi from University of Florence\n\nTitle: Likelihood-free Transport Monte Carlo  — Joint with Dr Dennis Prangle (University of Bristol)\nAbstract: Approximate Bayesian computation (ABC) is a class of methods for drawing inferences when the likelihood function is unavailable or computationally demanding to evaluate. Importance sampling and other algorithms using sequential importance sampling steps are state-of-art methods in ABC. Most of them get samples from tempered approximate posterior distributions defined by considering a decreasing sequence of ABC tolerance thresholds. Their efficiency is sensitive to the choice of an adequate proposal distribution and/or forward kernel function. We present a novel ABC method addressing this problem by combining importance sampling steps and optimization procedures. We resort to Normalising Flows (NFs) to optimize proposal distributions over a family of densities to transport particles drawn at each step towards the next tempered target. Therefore\, the combination of sampling and optimization steps allows tempered distributions to get efficiently closer to the target posterior. Finally\, we show the performance of our method on examples that are a common benchmark for likelihood-free inference.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-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/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20221007T103000
DTEND;TZID=Europe/Rome:20221007T113000
DTSTAMP:20260508T114531
CREATED:20220802T135324Z
LAST-MODIFIED:20221010T081915Z
UID:4608-1665138600-1665142200@datascience.unifi.it
SUMMARY:Seminar of the "Special Guest Seminar Series" - Kosuke Imai
DESCRIPTION:Welcome to the “Special Guest Seminar Series“! \nThe Seminar will be held on-site and online Friday 7th October 2022 from 10.30 – 11.30 am.  \nOur guest will be Kosuke Imai – Professor of Government and of Statistics\, Harvard University. \nThe seminar will be held in Aula 205 (ex 32) – Viale Morgagni 59. The Seminar will be available also online. Please register here to participate online: https://us02web.zoom.us/webinar/register/WN_vqVkwNmmSp2194Ne3Z4WsQ \n  \nTitle: Statistical Inference for Heterogeneous Treatment Effects and Individualized Treatment Rules Discovered by Generic Machine Learning in Randomized Experiments \nAbstract: Researchers are increasingly turning to machine learning (ML) algorithms to estimate heterogeneous treatment effects (HET) and develop individualized treatment rules (ITR) using randomized experiments. Despite their promise\, ML algorithms may fail to accurately ascertain HET or produce efficacious ITR under practical settings with many covariates and small sample size. In addition\, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for estimating HET and evaluating ITR discovered by a generic ML algorithm. We utilize Neyman’s repeated sampling framework\, which is solely based on the randomization of treatment assignment and random sampling of units.  Unlike some of the existing methods\, the proposed methodology does not require modeling assumptions\, asymptotic approximation\, or resampling methods. We extend our analytical framework to a common setting\, in which the same experimental data is used to both train ML algorithms and evaluate HET/ITR. In this case\, our statistical inference incorporates the additional uncertainty due to random splits of data used for cross-fitting. \n 
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-special-guest-seminar-series-kosuke-imai/
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:20221003T120000
DTEND;TZID=Europe/Rome:20221003T130000
DTSTAMP:20260508T114531
CREATED:20220928T090046Z
LAST-MODIFIED:20220928T090046Z
UID:4676-1664798400-1664802000@datascience.unifi.it
SUMMARY:DISIA Seminar: Social background inequality in academic track enrolment: How the role of individual competencies\, teachers’ assessments and family decisions varies across Italian provinces
DESCRIPTION:Title: Social background inequality in academic track enrolment: How the role of individual competencies\, teachers’ assessments and family decisions varies across Italian provinces \nSpeaker: Moris Triventi e Emanuele Fedeli (Università degli Studi di Trento) \nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 \nAbstract: We aim to understand the main sources of social background inequalities in academic track enrolment in Italy and whether their relative importance varies across provinces. Italy is a well-suited case study since it is characterized by low educational attainment rates\, high levels of educational inequalities and strong geographical divides in school outcomes. We distinguish between three main general channels by which social inequalities in educational transitions are reproduced\, the so-called ‘primary’\, ‘secondary’\, and ‘tertiary effects’ (Boudon 1974; Esser 2016). They refer respectively to the role of individual competencies\, teachers’ assessments and family decisions. We compiled a student population panel dataset from the Invalsi-SNV\, following 1\,344 million students from five cohorts (2013 – 2017) enrolled in the 8th grade of lower secondary school (untracked) to the 10th grade of upper secondary education (tracked). We use binomial logistic regression models to measure social background inequality and the KHB method to decompose it into the three channels (Karlson et al. 2012). We find that families’ choices\, irrespective of students’ abilities and teachers’ evaluations\, are the prevalent source of reproduction of inequalities in academic track enrolment\, followed by tertiary and then primary effects. Interestingly\, we find more geographical heterogeneity in the channels by which educational inequalities are reproduced than in the total inequality by social background\, a novel finding in the literature. With this work we complement the cross-national literature and provide new evidence that heterogeneity across contexts does not only refer to the level of social disparities but also to how inequalities are (re)produced.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-social-background-inequality-in-academic-track-enrolment-how-the-role-of-individual-competencies-teachers-assessments-and-family-decisions-varies-across-italian-provinces/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220629T160000
DTEND;TZID=Europe/Rome:20220629T170000
DTSTAMP:20260508T114531
CREATED:20220505T080406Z
LAST-MODIFIED:20220622T115454Z
UID:4254-1656518400-1656522000@datascience.unifi.it
SUMMARY:Seminar of the "Special Guest Seminar Series" - Iavor Bojinov
DESCRIPTION:Welcome to the “Special Guest Seminar Series“! \nThe Seminar will be held on-site and online Wednesday 29th of June 2022.  \nOur guest will be Iavor Bojinov from Harvard Business School \n\n\nThe seminar will be held in Aula 005 (ex C) (DISIA – Viale Morgagni 59). \nThe Seminar will be available also online. Please register here to participate online:\n\n\n\n\nhttps://unifirenze.webex.com/unifirenze/j.php?RGID=r4fee0e61279d106d8d6c2bdd3ff73f0d\n\n\n\n  \nTitle: Design & Analysis of Dynamic Panel Experiments \nAbstract: 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. \n 
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-special-guest-seminar-series-iavor-bojinov/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220620T150000
DTEND;TZID=Europe/Rome:20220620T163000
DTSTAMP:20260508T114531
CREATED:20220613T123728Z
LAST-MODIFIED:20220613T123728Z
UID:4422-1655737200-1655742600@datascience.unifi.it
SUMMARY:DISIA Seminar: Exploring the Educational Paradox on Preterm Births in Colombia
DESCRIPTION:Title: Exploring the Educational Paradox on Preterm Births in Colombia \nSpeaker: Harold Mera León (Universitat Pompeu Fabra\, Barcelona) \nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 (need to register here https://labdisia.disia.unifi.it/reserve205/) \nAbstract: 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.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-exploring-the-educational-paradox-on-preterm-births-in-colombia/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220617T140000
DTEND;TZID=Europe/Rome:20220617T153000
DTSTAMP:20260508T114531
CREATED:20220428T092610Z
LAST-MODIFIED:20220609T121702Z
UID:4169-1655474400-1655479800@datascience.unifi.it
SUMMARY:Seminar of the "Special Guest Seminar Series" - Dante Amengual
DESCRIPTION:Welcome to the “Special Guest Seminar Series“! \nThe Seminar will be held on-site and online Friday 17th June 2022.  \nOur guest will be Dante Amengual from CEMFI in Madrid\, Spain. \nThe 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/\nThe Seminar will be available also online. Please register here to participate online:\nhttps://unifirenze.webex.com/unifirenze/j.php?RGID=rd77cde701a83e73a6dc672a2e9644c85 \n\nTitle: Hypothesis tests with a repeatedly singular information matrix \nAbstract: We study score-type tests in likelihood contexts in which the nullity of the information matrix under the null is larger than one\, thereby\ngeneralizing 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.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-special-guest-seminar-series-florence-center-for-data-science/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220601T150000
DTEND;TZID=Europe/Rome:20220601T163000
DTSTAMP:20260508T114531
CREATED:20220530T141149Z
LAST-MODIFIED:20220530T141715Z
UID:4367-1654095600-1654101000@datascience.unifi.it
SUMMARY:DISEI Seminar: Algorithmic Bias and Problematic Use of Social Media
DESCRIPTION:Title: Algorithmic Bias and Problematic Use of Social Media \nSpeaker: Nello Cristianini (University of Bristol) \nLocation: Campus di Novoli aula D6 0.18 or online here https://tinyurl.com/5n894whe \n \n 
URL:https://datascience.unifi.it/index.php/event/disei-seminar-algorithmic-bias-and-problematic-use-of-social-media/
LOCATION:D6 Via delle Pandette 9\, Via delle Pandette 9\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
END:VCALENDAR