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X-ORIGINAL-URL:https://datascience.unifi.it
X-WR-CALDESC:Events for Florence data science
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DTSTART;TZID=Europe/Rome:20221213T080000
DTEND;TZID=Europe/Rome:20221216T170000
DTSTAMP:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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:20260505T103807
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
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220527T140000
DTEND;TZID=Europe/Rome:20220527T150000
DTSTAMP:20260505T103807
CREATED:20220518T151925Z
LAST-MODIFIED:20220518T154539Z
UID:4296-1653660000-1653663600@datascience.unifi.it
SUMMARY:FDS Seminar - Pedro J. Gutiérrez Diez
DESCRIPTION:The Seminar will be held both on-site and online Friday 27th of May 2022\, from 2-3 PM.\n\n\n \nPedro J. Gutiérrez Diez from the Department of Economic Theory / Mathematical Research Institute (IMUVa) of the University of Valladolid will present a seminar on “Analysis of the epigenetic changes in the breast after pregnancy” (see abstract below).\n\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:\n\nhttps://unifirenze.webex.com/unifirenze/j.php?RGID=re6d19dd000a6188d9c63e6667b9c1a8e\n\n\n\n\n\n\n\nSpeaker: Pedro J. Gutiérrez Diez – Department of Economic Theory / Mathematical Research Institute (IMUVa) – University of Valladolid\nTitle: Analysis of the epigenetic changes in the breast after pregnancy\nAbstract: Full-term pregnancy at an early age (FFTP) confers long-term protection against breast cancer\, being a guide for research on cancer prevention. The correct design of strategies based on this protective effect of pregnancy requires the characterization of its genomic consequences. In this respect\, published literature suggests that pregnancy causes a specific transcriptomic profile controlling chromatin remodeling after pregnancy\, therefore implying multiple and complex changes in gene expressions. In this research we analyze from several perspectives the modifications in the gene expression after FFTP\, concluding that\, independently of the changes in the gene expression at the individual level usually considered\, there are significant changes in gene-gene interactions and gene cluster behaviors.
URL:https://datascience.unifi.it/index.php/event/fds-seminar-pedro-j-gutierrez-diez/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220513T100000
DTEND;TZID=Europe/Rome:20220513T113000
DTSTAMP:20260505T103807
CREATED:20220428T084025Z
LAST-MODIFIED:20220516T091010Z
UID:4164-1652436000-1652441400@datascience.unifi.it
SUMMARY:17th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:The Florence Center for Data Science is happy to present the last Seminar of the “D2 Seminar Series” for this year launched by the FDS. The Seminar will be held on-site and online Friday 13th of May 2022 from 10 to 11.30 am.  \nOur guests will be Georgia Papadogeorgou and Joseph Antonelli from the Department of Statistics at the University of Florida. \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:\n \nhttps://unifirenze.webex.com/unifirenze/j.php?RGID=rddf7e0689ad2f9918485ada9101dbe17\n\n \nAfter registering\, you will receive a confirmation email containing information about joining the webinar.\n\n  \n\nSpeaker: Georgia Papadogeorgou – Department of Statistics\, University of Florida\nTitle: Unmeasured spatial confounding\nAbstract: Spatial confounding has different interpretations in the spatial and causal inference literature. I will begin this talk by clarifying these two interpretations. Then\, seeing spatial con-founding through the causal inference lens\, I discuss two approaches to account for unmeasured variables that are spatially structured when we are interested in estimating causal effects. The first approach is based on the propensity score. We introduce the distance adjusted propensity scores (DAPS) that combine spatial distance and propensity score difference of treated and control units in a single quantity. Treated units are then matched to control units if their corresponding DAPS is low. We can show that this approach is consistent\, and we propose a way to choose how much matching weight should be given to unmeasured spatial variables. In the second approach\, we aim to bridge the spatial and causal inference literature by estimating causal effects in the presence of unmeasured spatial variables using outcome modeling tools that are popular in spatial statistics. Motivated by the bias term of commonly-used estimators in spatial statistics\, we propose an affine estimator that addresses this deficiency. I will discuss that estimation of causal parameters in the presence of unmeasured spatial confounding can only be achieved under an untestable set of assumptions. We provide one such set of assumptions that describe how the exposure and outcome of interest relate to the unmeasured variables. \nSpeaker: Joseph Antonelli – Department of Statistics\, University of Florida\nTitle: Heterogeneous causal effects of neighborhood policing in New York City with staggered adoption of the policy\nAbstract: In New York City\, neighborhood policing was adopted at the police precinct level over the years 2015-2018\, and it is of interest to both (1) evaluate the impact of the policy\, and (2) understand what types of communities are most impacted by the policy\, raising questions of heterogeneous treatment effects. We develop novel statistical approaches that are robust to unmeasured confounding bias to study the causal effect of policies implemented at the community level. We find that neighborhood policing decreases discretionary arrests in certain areas of the city\, but has little effect on crime or racial disparities in arrest rates.
URL:https://datascience.unifi.it/index.php/event/17th-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/2021/05/Sfondo-D2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220422T140000
DTEND;TZID=Europe/Rome:20220422T153000
DTSTAMP:20260505T103807
CREATED:20220121T095005Z
LAST-MODIFIED:20220414T135613Z
UID:3885-1650636000-1650641400@datascience.unifi.it
SUMMARY:16th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:The Florence Center for Data Science is happy to present the next Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held on-site and online Friday 22nd of April 2022\, from 2-3.30 pm. \n\nAndrea Barucci from IFAC-CNR Institute of Applied Physics will present a seminar on “Exploring Egyptian Hieroglyphs with Convolutional Neural Networks” and Alessandra Mattei from the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence will present a seminar on “Selecting Subpopulations for Causal Inference in Regression Discontinuity Designs” (see abstract below). \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=rab9a115b6fac3b81f6b3fb36bb96f6da \nAfter registering\, you will receive a confirmation email containing information about joining the webinar. \n\n———————————————————————————–\n\nSpeaker: Andrea Barucci – IFAC-CNR Institute of Applied Physics\nTitle: Exploring Egyptian Hieroglyphs with Convolutional Neural Networks\nAbstract: Deep Learning is expanding in every domain of knowledge\, allowing specialists to build tools to support their work in fields apparently unrelated to information technology. In this study\, we exploit this opportunity by focusing on Egyptian hieroglyphic texts and inscriptions. We investigate the ability of several convolutional neural networks (CNNs) to segment glyphs and classify images of ancient Egyptian hieroglyphs derived from various image datasets. Three well-known CNN architectures (ResNet-50\, Inception-v3\, and Xception) were considered for classification and trained on the supplied pictures. Furthermore\, we constructed a specifically devoted CNN\, termed Glyphnet\, by changing the architecture of one of the prior networks and customizing its complexity to our classification goal. The suggested Glyphnet outperformed the others in terms of performance\, ease of training\, and computational savings\, as judged by established measures. The hieroglyphs segmentation was faced in parallel\, using a deep neural network architecture known as Mask-RCNN. This work shows how the ancient Egyptian hieroglyphs identification task can be supported by the Deep Learning paradigm\, laying the foundation for developing novel information tools for automatic documents recognition\, classification and\, most importantly\, the language translation task.\n\n———————————————————————————-\n\n\n\nSpeaker: Alessandra Mattei – Department of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence\nTitle: Selecting Subpopulations for Causal Inference in Regression Discontinuity Designs (Joint work with Laura Forastiere e Fabrizia Mealli)\nAbstract: The Brazil Bolsa Famı́lia program is a conditional cash transfer program aimed to reduce short-term poverty by direct cash transfers and to fight long-term poverty by increasing human capital among poor Brazilian people. Eligibility for Bolsa Famı́lia benefits depends on a type of cutoff formula\, which classifies the Bolsa Famı́lia study as a regression discontinuity (RD) design. Extracting causal information from RD studies is challenging. Following Li\, Mattei and Mealli (2015) and Branson and Mealli (2019)\, we formally describe the Bolsa Famı́lia RD design as a local randomized experiment within the potential outcome approach. Under this framework\, causal inference concerns Brazilian families belonging to some subpopulation where a local overlap assumption\, a local SUTVA and a local ignorability assumption hold. We first discuss the potential advantages of this framework\, in settings were assumptions are judged plausible\, over local regression methods based on continuity assumptions\, namely a) it generates treatment effects for subpopulation members rather than local average treatment effects for those at the cutoff only\, making the results more easily generalizable; b) it avoids modeling assumptions on the relationship between the running variable and the outcome; c) it allows the treatment assignment mechanism to be random rather than deterministic as in typical RD analyses\, so that finite population inference can be used; d) it allows to easily account for discrete running variables. A critical issue of the approach is how to choose subpopulations for which we can draw valid causal inference. We propose to use a Bayesian model-based finite mixture approach to clustering to classify observations into subpopulations where the RD assumptions hold and do not hold on the basis of the observed data. This approach has important advantages: a) it allows to account for the uncertainty about the subpopulation membership\, which is typically neglected; b) it does not impose any constraint on the shape of the subpopulation (bandwidth); c) it can be used as a design phase of any analysis; d) it is scalable to high-dimensional settings; e) and it allows to account for rare outcomes. We apply the framework to assess causal effects of the Borsa Famı́lia program on leprosy incidence in 2009\, which is a rare outcome\, using information on a large sample of Brazilian families who registered in the Single Registry in 2007-2008 for the first time.
URL:https://datascience.unifi.it/index.php/event/16th-seminar-of-the-d2-seminar-series-florence-center-for-data-science/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2021/05/Sfondo-D2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220408T150000
DTEND;TZID=Europe/Rome:20220408T163000
DTSTAMP:20260505T103807
CREATED:20220121T094926Z
LAST-MODIFIED:20220406T091520Z
UID:3883-1649430000-1649435400@datascience.unifi.it
SUMMARY:15th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:The Florence Center for Data Science is happy to present the 15th Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 8th of April 2022\, from 3-4.30 pm. \nFabio Schoen from the Department of Information Engineering of the University of Florence will present a seminar on “Clustering for Optimization\, Optimization for Clustering”  and Alessandro Panunzi and Lorenzo Gregori from the Department of Humanities of the University of Florence will present a seminar on “Towards action concepts identification through unsupervised and semi-supervised clustering on a multimodal cross-linguistic ontology”. \nRegister for this webinar: https://unifirenze.webex.com/unifirenze/j.php?RGID=ref67f44b0c3e01a0b79245a5ef1f5e92\n\nAfter registering\, you will receive a confirmation email containing information about joining the webinar.\n\n\n\nSpeaker: Fabio Schoen – Department of Information Engineering\, University of Florence\nTitle: Clustering for Optimization\, Optimization for Clustering\nAbstract: In this tak I will present two fundamental problems in data science: the global optimization problem (i.e.\, how to find globally optimal solutions to a mathematical programming problem) and the problem of clustering multi-dimensional data (i.e. how to efficiently group data according to ismilarity). The aim of this talk is to present the connections between these fundamental problems and to show how each of them can be used to improve the performance of the other one. For Global Optimization problems\, the idea of clustering dates back to the 80’s\, when researchers used clustering techniques to recognize the regions of attraction of local optima\, in the search for the global one. Due to reasons that I will be explaining during the seminar\, those approaches were abandoned\, however we have shown that\, provided some modifications are introduced\, they might prove very interesting for modern global optimization. On the other side\, clustering high dimensional data is clearly an optimization problem\, as we would like to group points so that a measure of similarity within groups is maximized. Recent computational approaches have been developed in which classical clustering techniques\, like\, e.g.\, K-means\, are used as local optimization tools which\, when embedded in a higher level global optimization strategy\, can produce significantly better clusters.\nThis talk is partly based on research done in collaboration with dr. Luca Tigli\, PhD\, and dr. Pierluigi Mansueto \nSpeaker: Alessandro Panunzi & Lorenzo Gregori – Department of Humanities\, University of Florence\nTitle: Towards action concepts identification through unsupervised and semi-supervised clustering on a multimodal cross-linguistic ontology\nAbstract: This work presents the steps performed on IMAGACT ontology of action to identify cognitively consistent action concepts through machine learning methods. IMAGACT contains a set of 1\,010 actions\, represented by video scenes\, and enriched with linguistic data in 14 languages. Each scene is linked to the full set of verbs that can be used to refer the depicted action\, in every language. Starting from these data\, an automatic clustering of scenes has been performed\, using the linked lexical items as a feature set\, following the idea that similar actions can be referred by a similar group of verbs. In order to obtain an evaluation of the clusters\, a wide set of surveys have been set up\, and action similarity judgements from human raters have been collected. These data have been analyzed together with automatic clustering metrics to evaluate the clustering and to tune the algorithm. The presentation will also focus on similarity evaluation issues emerging from a task that involves human perception and cognitive processing.
URL:https://datascience.unifi.it/index.php/event/15th-seminar-of-the-d2-seminar-series-florence-center-for-data-science/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2021/05/Sfondo-D2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220408T110000
DTEND;TZID=Europe/Rome:20220408T120000
DTSTAMP:20260505T103807
CREATED:20220324T102426Z
LAST-MODIFIED:20220324T102426Z
UID:4131-1649415600-1649419200@datascience.unifi.it
SUMMARY:DISIA Seminar: A new programming interface for Gaussian process regression
DESCRIPTION:Title: A new programming interface for Gaussian process regression \nSpeaker: Giacomo Petrillo (Department of Statistics\, Computer Science\, Applications\, University of Florence) \nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 (need to register here https://tinyurl.com/mrx654pn). \nThe seminar will be also online and you can participate at the following link: https://tinyurl.com/yc8a78zm \nAbstract: A Gaussian process is a multivariate Normal distribution over a space of functions. Gaussian processes are commonly used as a prior in a Bayesian setting to infer an unknown function without specifying a finitely parameterized model. (In non-Bayesian contexts\, this is known as kriging.) This technique is very flexible\, but at the same time allows to provide strong prior information\, when available\, which would be difficult to encode in a model\, like the degree of smoothness of the function or its periodicity. From the point of view of non-statisticians or applied statisticians\, Gaussian processes are used through a pre-written program\, much like most statistical methods. I will present a Python module designed for the task which introduces a new kind of interface to define the structure of the problem and manipulate the information\, focused on maintaining a high degree of flexibility while keeping the user code as short and readable as possible. I will show how the program improves on existing implementations\, then I will continue with some ideas for its future development\, trying to fill in what is missing in other programs.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-a-new-programming-interface-for-gaussian-process-regression/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220406T143000
DTEND;TZID=Europe/Rome:20220406T160000
DTSTAMP:20260505T103807
CREATED:20220324T102109Z
LAST-MODIFIED:20220324T102109Z
UID:4128-1649255400-1649260800@datascience.unifi.it
SUMMARY:DISIA Seminar: Blockchain: what it is and why it matters
DESCRIPTION:Title: Blockchain: what it is and why it matters \nSpeaker: Laura Ricci & Damiano di Francesco Maesa (Department of Computer Science\, University of Pisa) \nLocation: Aula 205 (ex 32) – DISIA – Viale Morgagni 59 (need to register here https://tinyurl.com/mrx654pn). \nThe seminar will be also online and you can participate at the following link: https://tinyurl.com/5f7tuh5u \nAbstract: A blockchain protocol is employed to implement a tamper-free distributed ledger that stores transactions created by the nodes of a P2P network and agreed upon through a distributed consensus algorithm\, avoiding the need for a central authority. Blockchain technology has a great potential to radically change our socio-economical systems by guaranteeing secure transactions between untrusted entities\, reducing their cost\, and simplifying many processes. Such technology is going to be exploited in many areas like IoT\, social networking\, health care\, electronic voting and so on. This talk will introduce the basic principles of this new\, disruptive technology\, highlighting a set of “killer applications”. We will also show the innovative potential for research in such a field\, showing results on the study of blockchain transaction graphs to characterize users’ behaviors.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-blockchain-what-it-is-and-why-it-matters/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220331T143000
DTEND;TZID=Europe/Rome:20220331T160000
DTSTAMP:20260505T103807
CREATED:20220330T101037Z
LAST-MODIFIED:20220330T101118Z
UID:4139-1648737000-1648742400@datascience.unifi.it
SUMMARY:DIMAI: Dini Mathematics Colloquium
DESCRIPTION:The first “Dini Mathematics Colloquium” is scheduled for Thursday 31st March at 2.30 pm\, it will be held by Prof. Alfio Quarteroni (Polimi – EPFL). \n\n\nTitle: Physics-based and data-driven mathematical models for the simulation of the heart function \nAbstract: This seminar focuses on machine learning (the computers’ ability to learn based on training from large data sets) and computational science (the use of mathematical models originated from fundamental principles of physics) in solving mathematical problems of interest in real life. Similarities and differences\, potentials\, and limitations are discussed\, as well as the enormous possibilities offered by their synergistic use. The driving application will be the simulation of the cardiac function\, in both physiological and pathological regimes \n\nYou can participate by using the form you find at this link https://forms.gle/6ee2TXYbPG6axHCBA\nOn the morning of March 31\, everyone will receive a link to a Webex meeting via email to join remotely.\nFor more info\, visit this website  https://www.dimai.unifi.it/art-408-colloquio-di-matematica-del-dini.html
URL:https://datascience.unifi.it/index.php/event/dimai-dini-mathematics-colloquium/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2020/09/Screenshot-2020-09-03-at-14.58.49.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220325T143000
DTEND;TZID=Europe/Rome:20220325T160000
DTSTAMP:20260505T103807
CREATED:20220121T094825Z
LAST-MODIFIED:20220317T161039Z
UID:3881-1648218600-1648224000@datascience.unifi.it
SUMMARY:14th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:The Florence Center for Data Science is happy to present the 13th Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 25th of March 2021\, from 2.30-4 pm. \nBrunero Liseo from the Department of Methods and Models for Economics\, Territory\, and Finance of the Sapienza University will present a seminar on “ABCC: Approximate Bayesian Conditional Copulae (with Clara Grazian and Luciana Dalla Valle)” and Ernesto De Vito from the Department of Mathematics of the University of Genova will present a seminar on “Understanding Neural Networks with Reproducing Kernel Banach Spaces“. \nRegister in advance for this webinar:\nhttps://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA \nAfter registering\, you will receive a confirmation email containing information about joining the webinar. \n\n\nSpeaker: Brunero Liseo – Department of Methods and Models for Economics\, Territory\, and Finance of the Sapienza University\nTitle: ABCC: Approximate Bayesian Conditional Copulae (with Clara Grazian and Luciana Dalla Valle)\nAbstract: Copula models are flexible tools to represent complex structures of dependence for multivariate random variables. According to Sklar’s theorem (Sklar\, 1959)\, any d-dimensional absolutely continuous density can be uniquely represented as the product of the marginal distributions and a copula function that captures the dependence structure among the vector components. In real data applications\, the interest of the analyses often lies on specific functionals of the dependence\, which quantify aspects of it in a few numerical values. A broad literature exists on such functionals\, however\, extensions to include covariates are still limited. This is mainly due to the lack of unbiased estimators of the copula function\, especially when one does not have enough information to select the copula model. Recent advances in computational methodologies and algorithms have allowed inference in the presence of complicated likelihood functions\, especially in the Bayesian approach\, whose methods\, despite being computationally intensive\, allow us to better evaluate the uncertainty of the estimates. In this work\, we present several Bayesian methods to approximate the posterior distribution of functionals of the dependence\, using nonparametric models which avoid the selection of the copula function. These methods are compared in simulation studies and in two realistic applications\, from civil engineering and astrophysics.\n\n \n\nSpeaker: Ernesto De Vito – Department of Mathematics of the University of Genova\nTitle: Understanding Neural Networks with Reproducing Kernel Banach Spaces\nAbstract: Characterizing the function spaces corresponding to neural networks can provide a way to understand their properties. The talk is devoted to showing how the theory of reproducing kernel Banach spaces can be used to characterize the function spaces corresponding to neural networks. In particular\, I will show a representer theorem for a class of reproducing kernel Banach spaces\, which includes one hidden layer neural network of possibly infinite width. Furthermore\, I will prove that\, for a suitable class of ReLU activation functions\, the norm in the corresponding reproducing kernel Banach space can be characterized in terms of the inverse Radon transform of a bounded real measure. The talk is based on on joint work with F. Bartolucci\, L. Rosasco and S. Vigogna.
URL:https://datascience.unifi.it/index.php/event/14th-seminar-of-the-d2-seminar-series-florence-center-for-data-science/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2021/05/Sfondo-D2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220311T140000
DTEND;TZID=Europe/Rome:20220311T150000
DTSTAMP:20260505T103807
CREATED:20220121T094524Z
LAST-MODIFIED:20220308T114110Z
UID:3879-1647007200-1647010800@datascience.unifi.it
SUMMARY:13th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:The Florence Center for Data Science is happy to present the 13th Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 11th of March 2021\, from 2-3 pm. \nThe seminar on “ Paths and flows for centrality measures in networks” will be held by Daniela Bubboloni from the Department of Mathematics and Computer Science “Ulisse Dini” of the University of Florence. \nRegister in advance for this webinar:\nhttps://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA \nAfter registering\, you will receive a confirmation email containing information about joining the webinar. \n——————— \nSpeaker: Daniela Bubboloni – Department of Mathematics and Computer Science “Ulisse Dini”\, University of Florence \nTitle: Paths and flows for centrality measures in networks \nAbstract: Consider the number of paths that must pass through a subset X of vertices of a capacitated network N in a maximum sequence of arc-disjoint paths connecting two vertices y and z. Consider then the difference between the maximum flow value from y to z in N and the maximum flow value from y to z in the network obtained by N by setting to zero the capacities of all the arcs incident to X. When X is a singleton\, those quantities are involved in defining and computing the flow betweenness centrality and are commonly identified without any rigorous proof justifying the identification. That surprising gap in the literature is the starting point of our research. On the basis of a deep analysis of the interplay between paths and flows\, we prove that\, when X is a singleton\, those quantities coincide. On the other hand\, when X has at least two elements\, those quantities may be different from each other. By means of the considered quantities\, two conceptually different group centrality measures\, respectively based on paths and flows\, can be naturally defined. Such group centrality measures both extend the flow betweenness centrality to groups of vertices and satisfy a desirable form of monotonicity.
URL:https://datascience.unifi.it/index.php/event/13th-seminar-of-the-d2-seminar-series-florence-center-for-data-science/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2021/05/Sfondo-D2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220225T143000
DTEND;TZID=Europe/Rome:20220225T160000
DTSTAMP:20260505T103807
CREATED:20220121T093941Z
LAST-MODIFIED:20220221T151131Z
UID:3877-1645799400-1645804800@datascience.unifi.it
SUMMARY:12th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:The Florence Center for Data Science is happy to present the twelfth Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 25th of February 2021\, from 2.30-4.00 pm. \nThe seminar will be held by Marco Pangallo from the Institute of Economics and of the Economics and Management in the era of Data Science (EMbeDS) of Sant’Anna School of Advanced Studies – Pisa and Fiammetta Menchetti from the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence. \nRegister in advance for this webinar:\nhttps://us02web.zoom.us/webinar/register/WN_W9PHbIB-TQOs98cL7Z0Ilw \nAfter registering\, you will receive a confirmation email containing information about joining the webinar. \n——————— \nSpeaker: Marco Pangallo – Institute of Economics and of the Economics and Management in the era of Data Science (EMbeDS) of Sant’Anna School of Advanced Studies – Pisa\nTitle: Making a housing market agent-based model learnable \nAbstract: Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems based on micro-level assumptions. Often\, some of their micro-level variables cannot be observed in empirical data. These latent variables make it difficult to initialize an ABM in order to use it to track and forecast empirical time series. In this paper\, we propose a protocol to learn the latent variables of an ABM. We show how a complex ABM can be reduced to a probabilistic model\, characterized by a computationally tractable likelihood. This reduction can be abstracted into two general design principles: balance of stochasticity and data availability\, and replacement of unobservable discrete choices with differentiable approximations. We showcase our protocol by applying it to an ABM of the housing market\, in which agents with different incomes bid higher prices to live in high-income neighborhoods. We show that the obtained model preserves the general behavior of the ABM\, and at the same time it allows the estimation of latent variables through the optimization of its likelihood. In synthetic experiments\, we show that we can learn the latent variables with good accuracy\, and that our estimates make out-of-sample forecasting more precise compared to alternative benchmarks. Our protocol can be seen as an alternative to black-box data assimilation methods\, forcing the modeler to lay bare the assumptions of the model\, think about the inferential process\, and identify potential identification problems. \nSpeaker: Fiammetta Menchetti – Department of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence\nTitle: Combining counterfactual outcomes and ARIMA models for policy evaluation \nAbstract: The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes.In recent years\, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of ARIMA models\, which are instead very common in the econometrics literature. We propose a novel approach\, Causal-ARIMA (C-ARIMA)\, to define and estimate the causal effect of an intervention in observational time series settings under the RCM. We first formalize the assumptions enabling the definition\, the estimation and the attribution of the effect to the intervention. In the empirical application\, we use C-ARIMA to assess the causal effect of a permanent price reduction on supermarket sales. The Causal-ARIMA R package provides an implementation of our proposed approach.
URL:https://datascience.unifi.it/index.php/event/12th-seminar-of-the-d2-seminar-series-florence-center-for-data-science/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2021/05/Sfondo-D2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220211T143000
DTEND;TZID=Europe/Rome:20220211T160000
DTSTAMP:20260505T103807
CREATED:20220121T093555Z
LAST-MODIFIED:20220204T105043Z
UID:3874-1644589800-1644595200@datascience.unifi.it
SUMMARY:11th Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:The Florence Center for Data Science is happy to present the eleventh Seminar of the “D2 Seminar Series” launched by the FDS. The Seminar will be held online Friday 11th of February 2021\, from 2.30-4.00 pm. \nThe seminar will be held by Fabio Corradi and Michela Baccini from the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence. \nRegister in advance for this webinar:\nhttps://us02web.zoom.us/webinar/register/WN_KFoLkeSfT3-kzWLK2mwHPA \nAfter registering\, you will receive a confirmation email containing information about joining the webinar. \n——————— \nSpeaker: Fabio Corradi – Department of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence\nTitle: Learning the two parameters of the Poisson-Dirichlet distribution with a forensic application \nAbstract: This contribution is motivated by the rare type match problem\, a relevant forensic issue. There\, difficulties arise to evaluate the likelihood ratio comparing the defense and the prosecution hypotheses since the specific matching characteristic from the suspect and the crime scene is not in the reference database. A recently proposed solution approximates the likelihood ratio by plugging in the parameters MLE of a Poisson Dirichlet distribution\, a\nBayesian nonparametric prior modeling probability mass function showing a power-law behavior in the infinite dimensional space. We instead consider how to learn the parameters of a Posson-Dirichlet and we propose two sampling schemes: Monte Carlo Markov Chain and Approximate Bayesian Computation. We demonstrate that the previously employed plug-in solution produces a systematic bias that Bayesian inference avoids entirely. Finally\, we evaluate the method using a database of Y-chromosome haplotypes. \nSpeaker: Michela Baccini – Department of Statistics\, Computer Science\, Applications “G. Parenti”\, University of Florence\nTitle: Combining and comparing regional epidemic dynamics in Italy: Bayesian meta-analysis of compartmental models and model assessment via Global Sensitivity Analysis \nAbstract: During autumn 2020\, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number R0(t)\, and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID19-related deaths\, fixing at values from literature Infection Fatality Rate (IFR) and infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then\, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R0(t) curves were similar in the Northern and Central regions\, while a less peaked curve was estimated for the South. The maximum R0(t) ranged from 2.61 (North) to 2.15 (South) with an increase following school reopening and a decline at the end of October. Average temperature\, urbanization\, characteristics of family medicine and health care system\, economic dynamism\, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R0(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results\, but without compromising between-region comparisons.
URL:https://datascience.unifi.it/index.php/event/11th-seminar-of-the-d2-seminar-series-florence-center-for-data-science/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2021/05/Sfondo-D2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20220201T163000
DTEND;TZID=Europe/Rome:20220201T183000
DTSTAMP:20260505T103807
CREATED:20220126T145329Z
LAST-MODIFIED:20220126T145620Z
UID:3887-1643733000-1643740200@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\, 1st of February 2022 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/
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
END:VCALENDAR