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BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20231110T160000
DTEND;TZID=Europe/Rome:20231110T170000
DTSTAMP:20260505T194847
CREATED:20231019T172813Z
LAST-MODIFIED:20231019T173150Z
UID:5694-1699632000-1699635600@datascience.unifi.it
SUMMARY:InterConnect webinar: Qiwei Li
DESCRIPTION:On-site and online seminar: Friday 10th November 2023 from 4.00 – 5.00 PM \nTitle: When statistics meets AI: Bayesian modeling of spatial biomedical data\nSpeaker: Qiwei Li (University of Texas at Dallas)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \nStatistics relies more on human analyses with computer aids\, while AI relies more on computer algorithms with aids from humans. Nevertheless\, expanding the statistics concourse at each milestone provides new avenues for AI and creates new insides in statistics. This part incubates the findings initiated from either side of statistics or AI and benefits the other. In this talk\, I will demonstrate how the marriage between spatial statistics and AI leads to more explainable and predictable paths from raw spatial biomedical data to conclusions.  \nThe first part concerns the spatial modeling of AI-reconstructed pathology images. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at a large scale. The randomly distributed cells can be considered from a marked point process. I will present two novel Bayesian models for characterizing spatial correlations in a multi-type spatial point pattern. The new method provides a unique perspective for understanding the role of cell-cell interactions in cancer progression\, demonstrated through a lung cancer case study. \nThe second part concerns the spatial modeling of the emerging spatially resolved transcriptomics data. Recent technology breakthroughs in spatial molecular profiling have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario advances our understanding of molecular and cellular spatial organizations in tissues\, fueling the next generation of scientific discovery. I will focus on how to integrate information from AI tools into Bayesian models to address some key questions in this field\, such as spatial domain identification and gene expression reconstruction at the single-cell level. \n  \nWebpage \n 
URL:https://datascience.unifi.it/index.php/event/interconnect-webinar-qiwei-li/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2023/10/headshot.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20231106T170000
DTEND;TZID=Europe/Rome:20231106T180000
DTSTAMP:20260505T194847
CREATED:20231027T090014Z
LAST-MODIFIED:20231027T090014Z
UID:5716-1699290000-1699293600@datascience.unifi.it
SUMMARY:Online Open Day MD2SL 2023!
DESCRIPTION:The University of Florence and the IMT School for Advanced Studies Lucca invite you to the online Open Day of the Master’s program in Data Science and Statistical Learning (MD2SL). The event is scheduled for November 6\, 2023\, at 5:00 PM on Google Meet. We will begin with a brief presentation of the Master’s program\, followed by an open session to address any questions and concerns you may have.\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-md2sl-2023/
LOCATION:Online
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/10/SCHOLARSHIPS-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20231027T160000
DTEND;TZID=Europe/Rome:20231027T173000
DTSTAMP:20260505T194847
CREATED:20231002T125725Z
LAST-MODIFIED:20231008T121608Z
UID:5667-1698422400-1698427800@datascience.unifi.it
SUMMARY:InterConnect webinar: Eric Chi
DESCRIPTION:On-site and online seminar: Friday 27th October 2023 from 4.00 – 5.00 PM \nTitle: Proximal MCMC for Approximate Bayesian Inference of Constrained and Regularized Estimation\nSpeaker: Eric Chi (Rice University)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \nIn this talk I will introduce some extensions to the proximal Markov Chain Monte Carlo (Proximal MCMC) – a flexible and general Bayesian inference framework for constrained or regularized parametric estimation. The basic idea of Proximal MCMC is to approximate nonsmooth regularization terms via the Moreau-Yosida envelope. Initial proximal MCMC strategies\, however\, fixed nuisance and regularization parameters as constants\, and relied on the Langevin algorithm for the posterior sampling. We extend Proximal MCMC to the full Bayesian framework with modeling and data-adaptive estimation of all parameters including regularization parameters. More efficient sampling algorithms such as the Hamiltonian Monte Carlo are employed to scale Proximal MCMC to high-dimensional problems. Our proposed Proximal MCMC offers a versatile and modularized procedure for the inference of constrained and non-smooth problems that is mostly tuning parameter free. We illustrate its utility on various statistical estimation and machine learning tasks. \n  \nWebpage \n 
URL:https://datascience.unifi.it/index.php/event/5667/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2023/10/bio.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20231013T160000
DTEND;TZID=Europe/Rome:20231013T173000
DTSTAMP:20260505T194847
CREATED:20231002T124416Z
LAST-MODIFIED:20231008T121448Z
UID:5664-1697212800-1697218200@datascience.unifi.it
SUMMARY:InterConnect webinar: Daniel Kowal
DESCRIPTION:On-site and online seminar: Friday 13th October 2023 from 4.00 – 5.00 PM \nTitle: Nonparametric Copula Models for Multivariate\, Mixed\, and Missing Data\nSpeaker: Daniel Kowal  (Rice University)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \nModern datasets commonly feature both substantial missingness and many variables of mixed data types\, which present significant challenges for estimation and inference. Complete case analysis\, which proceeds using only the observations with fully-observed variables\, is often severely biased\, while model-based imputation of missing values is limited by the ability of the model to capture complex dependencies among (possibly many) variables of mixed data types. To address these challenges\, we develop a novel Bayesian mixture copula for joint and nonparametric modelling of multivariate count\, continuous\, ordinal\, and unordered categorical variables\, and deploy this model for inference\, prediction\, and imputation of missing data. Most uniquely\, we introduce a new and computationally efficient strategy for marginal distribution estimation that eliminates the need to specify any marginal models yet delivers posterior consistency for each marginal distribution and the copula parameters under missingness-at-random. Extensive simulation studies demonstrate exceptional modelling and imputation capabilities relative to competing methods\, especially with mixed data types\, complex missingness mechanisms\, and nonlinear dependencies. We conclude with a data analysis that highlights how improper treatment of missing data can distort a statistical analysis\, and how the proposed approach offers a resolution. \nBIO \nDr. Dan Kowal is the Dobelman Family Assistant Professor in the Department of Statistics at Rice University. His research interests include Bayesian models and algorithms for large and dependent data\, decision analysis for interpretable and actionable model summarization\, and synthesis and imputation of mixed data. Application areas include public health\, epidemiology and environmental justice\, physical activity data\, economics\, and finance. Dr. Kowal’s research has been recognized with a Young Investigator Award from the Army Research Office\, the inaugural Blackwell-Rosenbluth Award\, and multiple paper and presentation awards. He received his PhD from Cornell University. \nWeb page
URL:https://datascience.unifi.it/index.php/event/interconnect-webinar-daniel-kowal/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2023/10/170810_Dan_Kowal-0007-e1696767170345.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230906T120000
DTEND;TZID=Europe/Rome:20230906T140000
DTSTAMP:20260505T194847
CREATED:20230629T130503Z
LAST-MODIFIED:20231002T121655Z
UID:5589-1694001600-1694008800@datascience.unifi.it
SUMMARY:DiSIA Welcome Seminar Giammarco Alderotti\, Marco Cozzani\, Maria Veronica Dorgali
DESCRIPTION:Speaker: Giammarco Alderotti \nTitle: A journey\, just begun\, into family demography \nSpeaker: Marco Cozzani \nTitle: The heterogeneous consequences of adverse events \nSpeaker: Maria Veronica Dorgali \nTitle: Understanding health behaviours through the lens of psychology \n  \n 
URL:https://datascience.unifi.it/index.php/event/disia-welcome-seminar-giammarco-alderotti-marco-cozzani-maria-veronica-dorgali/
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:20230619T140000
DTEND;TZID=Europe/Rome:20230619T140000
DTSTAMP:20260505T194847
CREATED:20230518T081951Z
LAST-MODIFIED:20230518T081951Z
UID:5534-1687183200-1687183200@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: George Luta (Georgetown University) \nTitle: On Optimal Correlation-Based Prediction \nAbstract:\nWe consider the problem of obtaining predictors of a random variable by maximizing the correlation between the predictor and the predictand. For the case of Pearson’s correlation\, the class of such predictors is uncountably infinite and the least-squares predictor is a special element of that class. By constraining the means and the variances of the predictor and the predictand to be equal\, a natural requirement for some situations\, the unique predictor that is obtained has the maximum value of Lin’s concordance correlation coefficient (CCC) with the predictand among all predictors. Since the CCC measures the degree of agreement\, the new predictor is called the maximum agreement predictor. The two predictors are illustrated for three special distributions: the multivariate normal distribution; the exponential distribution\, conditional on covariates; and the Dirichlet distribution.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-17/
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:20230608T120000
DTEND;TZID=Europe/Rome:20230608T130000
DTSTAMP:20260505T194847
CREATED:20230518T081806Z
LAST-MODIFIED:20230518T081806Z
UID:5532-1686225600-1686229200@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Laura D’Angelo (Dipartimento di Economia\, Metodi Quantitativi e Strategie d’Impresa\, Università Milano Bicocca) \nTitle: Modeling grouped data via finite nested mixture models: an application to calcium imaging data \nAbstract: \nRecent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intracellular calcium signals. We propose a nested Bayesian finite mixture specification that allows estimating the underlying spiking activity and\, simultaneously\, reconstructing the distributions of the calcium transient spikes’ amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. We show that nested finite mixtures provide a valid alternative to priors based on infinite formulations and can even lead to better performances in some scenarios. We derive several prior properties and compare them with other well-known nonparametric nested models analytically and via simulation.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-16/
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:20230605T140000
DTEND;TZID=Europe/Rome:20230605T150000
DTSTAMP:20260505T194847
CREATED:20230518T081600Z
LAST-MODIFIED:20230518T081600Z
UID:5530-1685973600-1685977200@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Matteo Mio – CNRS\, ENS-Lyon \nTitle: An introduction to Quantitative Algebras \nAbstract: \nEquational reasoning and equational manipulations are widespread in all areas of computer science. Consider\, for example\, the optimisation steps performed by a compiler which replaces blocks of code with “equivalent”\, but more efficient\, blocks. In recent years it has become apparent that sometimes “approximate” equational reasoning techniques are useful and/or necessary. A block of code might be replaced by another block which is not truly equivalent but\, say\, equivalent 99% of the time (in a certain statistical sense). In this talk I will present the basic ideas of the mathematical framework of “Quantitative Algebras”\, recently proposed by Mardare et. al. in [1]\, aiming at formally developing some of the intuitions mentioned above. \n[1] Radu Mardare\, Prakash Panangaden\, and Gordon Plotkin. 2016. Quantitative Algebraic Reasoning. In Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science (LICS 2016). Association for Computing Machinery\, New York\, NY\, USA\, 700–709. https://doi.org/10.1145/2933575.2934518
URL:https://datascience.unifi.it/index.php/event/disia-seminar-15/
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:20230601T120000
DTEND;TZID=Europe/Rome:20230601T130000
DTSTAMP:20260505T194847
CREATED:20230427T134952Z
LAST-MODIFIED:20230518T151450Z
UID:5445-1685620800-1685624400@datascience.unifi.it
SUMMARY:DiSIA Double Welcome Seminar - Fiammetta Menchetti & Marta Pittavino
DESCRIPTION:Speaker: Fiammetta Menchetti \nTitle: From high school creativity to cultural heritage conservation: a journey in causal inference \nAbstract: \n In this talk\, I will provide an overview of my research activity in causal inference for time series data. Starting with\nC-ARIMA and Bayesian multivariate structural time series models that were part of my PhD thesis\, I’ll then give\nyou a glimpse into my recent collaborations\, including a randomized control trial to assess the impact of FABLAB’s\ncourses on the creativity of Italian high-school students and a machine learning method for counterfactual forecasting\nfor short panels in the absence of controls. The talk will then focus on the PNRR research activity\, which aims\nto study the evolution of the web cracks on Brunelleschi’s Santa Maria del Fiore Dome as part of an ongoing and\nfascinating project on cultural heritage conservation\n\n\n\n\n\nSpeaker: Marta Pittavino \nTitle: A tale on statistical methods\, and their applications\, developed around Europe \nAbstract: \n\n In this talk\, I will present some statistical methods that I exploited for my research. I will begin by presenting\nthe Additive Bayesian Network (ABN) multivariate methodology\, a data-driven technique particularly suitable for\ninter-dependent data. I will show two applications of ABN in the veterinary epidemiology field. Then\, I will\nmove to the illustration of a Bayesian hierarchical model applied to nutritional epidemiology. Specifically\, this\nmodel relates a measurement error model with a disease model via an exposure model. Afterwards\, I will provide\nexamples of quantile regression and forecasting techniques applied to a specific philanthropic-social dataset on\ncharitable deductions for tax incentives for the Canton of Geneva population. Last\, but not least\, I will conclude\nthis statistical modelling journey by introducing the current demographic project for the “Forecasting kinship\nnetworks and kinless individuals” and show the first preliminary results of kinless of countries around Europe.\n\n 
URL:https://datascience.unifi.it/index.php/event/disia-double-welcome-seminar-fiammetta-menchetti-marta-pittavino/
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:20230522T110000
DTEND;TZID=Europe/Rome:20230522T120000
DTSTAMP:20260505T194847
CREATED:20230509T163610Z
LAST-MODIFIED:20230515T091634Z
UID:5519-1684753200-1684756800@datascience.unifi.it
SUMMARY:Young researcher Seminar – Florence Center for Data Science
DESCRIPTION:Welcome another seminar of the “Young Researchers Seminar Series“! \nThe Seminar will be held both on-site and online Monday 22th May 2023 at 11:00 AM. \n\nOur guest will be Matt DosSantos DiSorbo from Harvard Business School.\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\n\nhttps://us02web.zoom.us/webinar/register/WN_4-1IeZgrS6WpY-En-JKoZw \n\nTitle: Starting Strong: The Impact of Early Interventions on Employee Outcomes \nAbstract: \nIn randomized experiments with insufficient covariates\, confounders can bias point estimates. We introduce rank estimates in factorial designs and discuss their relative robustness. We argue that\, in many applied settings\, identifying the top-ranked intervention is more critical than recovering exact point estimates. Using data from an experiment conducted at a large financial firm\, our method provides evidence that interventions in the first week of an internship have the largest sustained effect on intern rating. Further\, the data is suggestive that these early interventions have the greatest impact on interns eventually accepting an extended offer. This principle — intervene early — has important managerial implications.
URL:https://datascience.unifi.it/index.php/event/young-researcher-seminar-florence-center-for-data-science-2/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/03/QUADRATo-YR-Seminar-Series.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230516T120000
DTEND;TZID=Europe/Rome:20230516T130000
DTSTAMP:20260505T194847
CREATED:20230427T134705Z
LAST-MODIFIED:20230518T081613Z
UID:5443-1684238400-1684242000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Rosario Barone – University of Rome “Tor Vergata” \nTitle: Bayesian time-interaction point process \nAbstract: \nIn the temporal point process framework\, behavioral effects correspond to conditional dependence of event rates on times of previous events. This can result in self-exciting processes\, where the occurrence of an event at a time point increases the probability of occurrence of a later event\, or in self-correcting processes\, where the occurrence of an event at a time point decreases the probability of occurrence of a later event. Altieri et al. (2022) defined as time-interaction process a temporal point processes that is the combination of self-exciting and self-correcting point processes\, allowing each event to increase and/or decrease the likelihood of future ones. From the Bayesian perspective\, we generalize the existing model in several directions: we account for covariates and propose a nonparametric baseline\, which guarantees more flexibility and allows to control for heterogeneity. Also\, we let the model parameters be modulated by a discrete state continuous time latent Markov process. Posterior inference is performed via efficient Markov chain Monte Carlo (MCMC) sampling\, avoiding the implementation of discretization methods like Forward-Backward or Viterbi algorithm. Indeed\, by extending Hobolth and Stone (2009)\, we propose a data augmentation approach that allows to simulate the continuous time latent Markov trajectories. We present applications to simulated and terrorist attacks data. \nREFERENCES Altieri\, L.\, Farcomeni\, A.\, and Fegatelli\, D. A. (2022). Continuous timeinteraction processes for population size estimation\, with an application to drug dealing in Italy. Biometrics. Hobolth\, A. and Stone\, E. A. (2009). Simulation from endpoint-conditioned\, continuous-time Markov chains on a finite state space\, with applications to molecular evolution. The Annals of Applied Statistics\, 3(3): 1204-1231.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-14/
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:20230511T163000
DTEND;TZID=Europe/Rome:20230511T173000
DTSTAMP:20260505T194847
CREATED:20230331T150829Z
LAST-MODIFIED:20230502T095309Z
UID:5312-1683822600-1683826200@datascience.unifi.it
SUMMARY:Seminar of the "Special Guest Seminar Series" - Björn Bornkamp
DESCRIPTION:The Florence Center for Data Science and the Department of Statistics\, Computer Science\, Application “G. Parenti” DiSIA are happy to invite you to the joint event: a new Special Guest Seminar! \nThe Seminar will be held on-site and online Thursday 11th May 2023 from 4.30 – 5.30 PM \nOur guest will be Björn Bornkamp – Statistical Methodologist at Novartis. \nThe seminar will be held in Aula 205 (ex 32) – Viale Morgagni 59. \nThe Seminar will be available also online. \nPlease register here to participate online: https://us02web.zoom.us/webinar/register/WN_RgnfqAVYSJaA5JYRePVNUw \nTitle: Estimand and analysis strategies for recurrent event endpoints in the presence of a terminal event \nAbstract: \nRecurrent event endpoints are commonly used in clinical drug development. One example is the number of recurrent heart failure hospitalizations\, which is used in the context of clinical trials in the chronic heart failure (CHF) indication. A challenge in this context is that patients with CHF are at an increased risk of dying. For patients who died\, further heart failure hospitalizations can no longer be observed. As a treatment may affect both mortality and the number of hospitalizations\, a naive comparison of the number of hospitalizations across treatment arms can be misleading even in a randomized clinical trial. An investigational treatment may\, for example\, reduce mortality compared to a control\, but this may lead to more observed hospitalizations if severely ill patients with high risk of repeated hospitalizations die earlier under the control treatment. In this talk we will review this issue and different estimand and analysis strategies. We will then describe a Bayesian modelling strategy to target a principal stratum estimand in detail. The model relies on joint modelling of the recurrent event and death processes with a frailty term accounting for within-subject correlation. The analysis is illustrated in the context of a recent randomized clinical trial in the CHF indication. \n 
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-special-guest-seminar-series-bjorn-bornkamp/
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:20230420T120000
DTEND;TZID=Europe/Rome:20230420T130000
DTSTAMP:20260505T194847
CREATED:20230331T143356Z
LAST-MODIFIED:20230403T152226Z
UID:5302-1681992000-1681995600@datascience.unifi.it
SUMMARY:DiSIA seminar
DESCRIPTION:Speaker: Maurizio Pisati and Mario Lucchini (Università di Milano-Bicocca) \nTitle: L’indagine ITA.LI quantitativa: Disegno di campionamento\, pesi campionari e varianza degli stimatori \nAbstract: \nLo scopo di questo seminario è illustrare alcuni aspetti essenziali della prima rilevazione dell’indagine ITA.LI quantitativa. In primo luogo verrà descritto il disegno di campionamento\, soffermandosi sulle caratteristiche di ciascuno stadio. Successivamente sarà presentata la procedura di costruzione dei pesi campionari\, con particolare attenzione agli aggiustamenti per la non risposta e alla calibrazione post-stratificazione. L’ultima parte sarà dedicata all’incertezza degli stimatori in una prospettiva design-based. \nThe event is organized in collaboration with UPS – Population and Society Unit.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-13/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230417T120000
DTEND;TZID=Europe/Rome:20230417T133000
DTSTAMP:20260505T194847
CREATED:20230331T143027Z
LAST-MODIFIED:20230331T143027Z
UID:5300-1681732800-1681738200@datascience.unifi.it
SUMMARY:DiSIA "Welcome" Seminar - Daniele Castellana\, Andrea Marino\, Francesco Tiezzi
DESCRIPTION:Speaker: Daniele Castellana \nTitle: Machine Learning in Structured Domains \nAbstract: \nMachine Learning (ML) for structured data aims to build ML models which can handle structured data (e.g. sequence\, trees\, and graphs). In this setting\, common ML approaches for flat data (i.e. vectors) cannot be applied directly. In this seminar\, we show the challenges of learning from structured data and discuss how to overcome them. We introduce a tensor framework for learning from trees highlighting how tensors naturally arise in this context and the role of tensor decompositions to reduce the computational complexity of such approach. Interestingly\, this framework can be applied to both probabilistic and neural models for trees. Lastly\, we talk about learning from graphs. In particular\, we introduce a probabilistic model for graph based on a Bayesian Non-Parametric technique showing its effectiveness in inferring suitable hyper-parameters. \n  \n\nSpeaker: Andrea Marino \nTitle: Algorithms for the Analysis of (Temporal) Graphs \nAbstract: \nIn a society in which we are almost always “connected”\, the capability of acquiring different information has become a source of huge amounts of data to be elaborated and analysed. Graphs are data structures that allow modeling the relationships in these data\, effectively representing millions of nodes and billions of connections between nodes as edges. For example\, a social network can be seen as a graph whose nodes are the users and whose edges correspond to the friendships between them. In such a context\, discovering the most important or influential users\, which communities they belong to\, and which is the structure of their society can be translated into discovering suitable patterns on graphs. Sometimes in some scenarios additional constraints must be considered: for instance\, in temporal graphs\, connections are available only at prescribed times\, in the same way as connections of a public transportation system are available according to a time schedule. In temporal graphs\, walks and paths make sense only if the legs of the trip are time consistent\, i.e. the departure of a leg is after the arrival of the previous one\, and structural patterns must take into account this constraint. Our work is devoted to understand whether such patterns can be computed efficiently\, i.e. in time polynomial wrt the size of the graph\, hopefully linearly if the considered graphs are big. During the talk we will see a high level overview of our contributions in the field. \n\nSpeaker: Francesco Tiezzi \nTitle: A Tale on Domain-Specific System Engineering: the Case of Multi-Robot Systems \nAbstract: \nThe first part of the seminar introduces challenges and methodologies concerning the (possibly formal) engineering of domain-specific distributed systems. The second part focuses on a specific case: the development of software for robotics applications involving multiple heterogeneous robots. Programming such distributed software requires coordinating multiple cooperating tasks to avoid unsatisfactory emerging behaviors. It is then presented a programming language devised to face this challenge. The language supports an approach for programming multi-robot systems at a high abstraction level\, allowing developers to focus on the system behavior\, achieving readable\, reusable\, and maintainable code.
URL:https://datascience.unifi.it/index.php/event/disia-welcome-seminar-daniele-castellana-andrea-marino-francesco-tiezzi/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230404T150000
DTEND;TZID=Europe/Rome:20230404T160000
DTSTAMP:20260505T194847
CREATED:20230323T151141Z
LAST-MODIFIED:20230323T151141Z
UID:5293-1680620400-1680624000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Andrea Meilan Vila (Department of Statistics\, Universidad Carlos III de Madrid) \nTitle: Kernel regression estimation for a circular response and different types of covariates  \nAbstract: \nThe analysis of a variable of interest that depends on other variable(s) is a typical issue appearing in many practical problems. Regression analysis provides the statistical tools to address this type of problem. This topic has been deeply studied\, especially when the variables in the study are of Euclidean type. However\, there are situations where the data present certain kinds of complexities\, for example\, the involved variables are of circular or functional type\, and the classical regression procedures designed for Euclidean data may not be appropriate. In these scenarios\, these techniques would have to be conveniently modified to provide useful results. Moreover\, it might occur that the variables of interest can present a certain type of dependence. For example\, they can be spatially correlated\, where observations that are close in space tend to be more similar than observations that are far apart. This work aims to design and study new approaches to deal with regression function estimation for models with a circular response and different types of covariates. For an R^d-valued covariate\, nonparametric proposals to estimate the circular regression function are provided and studied\, under the assumption of independence and also for spatially correlated errors. These estimators are also adapted for regression models with a functional covariate. In the above-mentioned frameworks\, the asymptotic bias and variance of the proposed estimators are calculated. Some guidelines for their practical implementation are provided\, checking their sample performance through simulations. Finally\, the behavior of the estimators is also illustrated with real data sets.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-12/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230330T120000
DTEND;TZID=Europe/Rome:20230330T130000
DTSTAMP:20260505T194847
CREATED:20230309T102113Z
LAST-MODIFIED:20230309T102208Z
UID:5232-1680177600-1680181200@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Andrea Sottosanti (Università di Padova) \nTitle: Co-clustering Models for Spatial Transcriptomics \nAbstract: \nSpatial transcriptomics is a cutting-edge technology that\, differently from traditional transcriptomic methods\, allows researchers to recover the spatial organization of cells within tissues and to map where genes are expressed in space. By examining previously hidden spatial patterns of gene expression\, researchers can identify distinct cell types and study the interactions between cells in different tissue regions\, leading to a deeper understanding of several key biological mechanisms\, such as cell-cell communication or tumour-microenvironment interaction. \n\nDuring this talk\, we will be presenting novel statistical tools that exploit the previously unavailable spatial information in transcriptomics to coherently group cells and genes. First\, we will introduce SpaRTaCo\, a new model that clusters the gene expression profiles according to a partition of the tissue. This is accomplished by performing a co-clustering\, that is\, a simultaneous clustering of the genes using their expression across the tissue\, and of the image areas using the gene expression in the locations where the RNA is collected. Then\, we will show how to use SpaRTaCo when a previous annotation of the cell types is available\, incorporating biological knowledge into the statistical analysis. Last\, we will discuss a new modelling solution that exploits recent advances in sparse Bayesian estimation of covariance matrices to reconstruct the spatial covariance of the data in a sparse and flexible manner\, significantly reducing the computational cost of the model estimation.\nBy applying these tools to tissue samples processed with recent spatial transcriptomic technologies\, we can gain interesting and promising biological insights. The models are in fact able to reveal the presence of specific variation patterns in some restricted areas of the tissue that cannot be directly uncovered using other methods in the literature\, and\, inside each image cluster\, they can detect genes that carry out specific and relevant biological functions.\n 
URL:https://datascience.unifi.it/index.php/event/disia-seminar-11/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230328T113000
DTEND;TZID=Europe/Rome:20230328T130000
DTSTAMP:20260505T194847
CREATED:20230306T115050Z
LAST-MODIFIED:20230306T115715Z
UID:5194-1680003000-1680008400@datascience.unifi.it
SUMMARY:Economics Lecture by Nobel Prize 2021: Guido W. Imbens "Combining Experimental and Observational Data"
DESCRIPTION:The Florence Center for Data Science and the Department of Statistics\, Computer Science\, Application “G. Parenti” DiSIA in a joint event with the European University Institute EUI are happy to invite you to the Economics Lecture by Nobel Prize 2021: Guido W. Imbens.\n\n\nThe event will take place Tuesday 28th March 2023  from 11:30 to 12:45 at Auditorium A – Polo Didattico\, Viale Morgani\,40 – Università degli Studi di Firenze\n  \nThe event does not require prior registration. \n\n\n\nTitle: Combining Experimental and Observational Data \n\n\nPresented by Guido W. Imbens (Stanford University)\, Nobel Prize in Economics 2021\n\n\nAbstract:\nIn the social sciences there has been an increase in interest in randomized experiments to estimate causal effects\, partly\nbecause their internal validity tends to be high\, but they are often small and contain information on only a few variables. At the same time\, as part of the big data revolution\, large\, detailed\, and representative\, administrative data sets have become more widely available. However\, the credibility of estimates of causal effects based on such data sets alone can be low.\nIn this paper\, we develop statistical methods for systematically combining experimental and observational data to improve the credibility of estimates of the causal effects. We focus on a setting with a binary treatment where we are interested in the effect on a primary outcome that we only observe in the observational sample. Both the observational and experimental samples contain data about a treatment\, observable individual characteristics\, and a secondary (often short term) outcome. To estimate the effect of a treatment on the primary outcome\, while accounting for the potential confounding in the observational sample\, we propose a method that makes use of estimates of the relationship between the treatment and the secondary outcome from the experimental sample. We interpret differences in the estimated causal effects on the secondary outcome between the two samples as evidence of unobserved confounders in the observational sample\, and develop control function methods for using those differences to adjust the estimates of the treatment effects on the primary outcome. We illustrate these ideas by combining data on class size and third grade test scores from the Project STAR experiment with observational data on class size and both third and eighth grade test scores from the New York school system.Co-author: Susan Athey and Raj Chetty
URL:https://datascience.unifi.it/index.php/event/economics-lecture-by-nobel-prize-2021-guido-w-imbens-combining-experimental-and-observational-data/
LOCATION:Auditorium A – Viale Morgagni 40\, Auditorium A- Polo Didattico\, Viale Morgani\, 40 - Università degli Studi di Firenze\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/03/Flyer-Imbens-28th-Mar-short-e1678115649434.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230327T160000
DTEND;TZID=Europe/Rome:20230327T170000
DTSTAMP:20260505T194847
CREATED:20230314T113703Z
LAST-MODIFIED:20230314T113945Z
UID:5247-1679932800-1679936400@datascience.unifi.it
SUMMARY:DiSIA - ARTES4.0 Webinar
DESCRIPTION:The Department of Statistics\, Computer Science\, Applications DiSIA as part of the activities of the UNIFI Macronode in the ARTES4.0 Competence Center organizes the following webinar scheduled for March 27\, 2023 at 4 pm. \nWebinar DISIA#3: Explainable Artificial Intelligences;\nTitle: Counterfactual Explanations of (some) Machine Learning Models\nSpeaker: Prof. FABRIZIO SILVESTRI\,\nLanguage: Italian\n\n\nFor those interested in participating\, please  register at the link:  https://lp.artes4.it/it/ webinar-27-marzo-2023\n\n\nWebinar contents:\nStart 16.00: Introduction\n16.05 – Talk\n16.45- Discussion\, questions with participants\n17.00 – Conclusion\n\nThe third webinar promoted by DISIA focuses on Machine Learning (ML)\, in the context of Explicable Artificial Intelligence understood as a set of methodologies that allow a human to understand and trust the results produced by an ML algorithm. Such methodologies are used to describe the AI ​​model in terms of expected impact and potential bias in the decision-making processes in which an ML algorithm is integrated. These aspects are crucial to ensure that decision-making processes are impartial\, transparent and accurate.\n\nAbstract : In this presentation we will review some of the most recent contributions in the field of Explainable Artificial Intelligence (XAI) and\, in particular\, those of counterfactual (CF) explanations. We will start by reviewing our first contribution to the field and show some recent papers on CF explanations of graph neural network models. Guided by the interest of generalizing the CF explainability of ML models as much as possible\, we propose a solution based on reinforcement learning that treats the model as a black box that we cannot access directly.\nBrief Biography of Fabrizio Silvestri: Fabrizio Silvestri is a full professor at the DIAG of the Sapienza University of Rome. His research interests concern artificial intelligence and in particular machine learning applied to web search and natural language processing problems. He is the author of more than 150 articles in international journals and conference proceedings. He holds nine industrial patents. At Facebook AI\, Fabrizio Silvestri led research groups to develop artificial intelligence techniques to combat malicious actors who use the Facebook platform for malicious purposes (hate speech\, disinformation\, terrorism\, etc.). Fabrizio Silvestri holds a PhD in computer science from the University of Pisa.
URL:https://datascience.unifi.it/index.php/event/disia-artes4-0-webinar/
LOCATION:Online
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/03/Logo-Webinar-ARTES-Disia.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230323T120000
DTEND;TZID=Europe/Rome:20230323T130000
DTSTAMP:20260505T194847
CREATED:20230220T170558Z
LAST-MODIFIED:20230220T170826Z
UID:5162-1679572800-1679576400@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Sirio Legramanti (University of Bergamo) \nTitle: Concentration of discrepancy-based ABC via Rademacher complexity \nAbstract: \nThere has been increasing interest on summary-free versions of approximate Bayesian computation (ABC)\, which replace distances among summaries with discrepancies between the whole empirical distributions of the observed data and the synthetic samples generated under the proposed parameter values. The success of these solutions has motivated theoretical studies on the concentration properties of the induced posteriors. However\, current results are often specific to the selected discrepancy\, and mostly rely on existence arguments which are typically difficult to verify and provide bounds not readily interpretable. We address these issues via a novel bridge between the concept of Rademacher complexity and recent concentration theory for discrepancy-based ABC. This perspective yields a unified and interpretable theoretical framework that relates the concentration of ABC posteriors to the behavior of the Rademacher complexity associated to the chosen discrepancy in the broad class of integral probability semimetrics. This class extends summary-based ABC\, and includes the widely-implemented Wasserstein distance and maximum mean discrepancy (MMD)\, which admit interpretable bounds for the corresponding Rademacher complexity along with constructive sufficient conditions for the existence of such bounds. Therefore\, this unique bridge crucially contributes towards an improved understanding of ABC\, as further clarified through a focus of this theory on the MMD setting and via an illustrative simulation.\n\n\n(Joint work with Daniele Durante and Pierre Alquier)\, Link: https://arxiv.org/abs/2206.06991
URL:https://datascience.unifi.it/index.php/event/disia-seminar-9/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230320T153000
DTEND;TZID=Europe/Rome:20230320T170000
DTSTAMP:20260505T194847
CREATED:20230314T115331Z
LAST-MODIFIED:20230314T115331Z
UID:5253-1679326200-1679331600@datascience.unifi.it
SUMMARY:DiSIA - StEering - itENBIS Seminar
DESCRIPTION:The Department of Statistics\, Computer Science\, Applications DiSIA is happy to invite you to a seminar organized with the StEering – Statistics for Engineering: design\, quality and reliability and itENBIS the Italian group of the European Network for Business and Industrial Statistics \nSpeaker: G. Geoffrey Vining (Virginia Tech\, USA) \nTitle: Shewhart and Profile Monitoring for Industry 4.0 \nAbstract: \nAn important current area within statistical process control/monitoring is profile monitoring\, which assumes that the underlying profile of the data over time is some linear\, nonlinear\, or nonparametric model.  Let y be the characteristic of interest\, and let f(y; θ\, x)  be the underlying model\, where x is the p x 1 vector of variables that explain the behavior of y over time and θ is an unknown vector of model parameters relating  to y.  The standard approach taken by the profile monitoring community uses the following algorithm: \n(1)  Estimate θ for each individual value of y.  Let $\hat{\theta}_{i}$ be the resulting vector of estimates associated with each individual $y_{i}$.  Let $\hat{\theta}_{avg}$ be the average value of the $\hat{\theta}_{i}$s. \n(2)  Estimate the variance of $\hat{\theta}_{i}$ by computing estimates of every variance and covariance involving the components of x. \n(3)  Construct control limits using some variation of Hotelling’s $T^{2}$ statistic. \n\nThis approach historically assumes that p is very small.  There are many serious issues from a linear-models perspective to this approach\, not the least of which is an unnecessary need to estimate $p+\binom{p}{2}$ variance components\, which typically require much larger sample sizes to estimate than averages. \nIndustry 4.0 increases the number of sensors that can provide huge amounts of information in real time.  As a result\, there are opportunities to align data on those variables known to impact a critical quality characteristic to improve the monitoring of that characteristic.  The case study that underlies this talk has very good information on 40 variables known to impact the performance of the critical quality characteristic.  The current profile monitoring approach requires the estimation of 780 variances/covariances\, which is completely unrealistic. \nThis talk outlines how to incorporate the extra information efficiently and effectively.  Ironically\, this approach has its origins in Shewhart’s original ideas underlying control charts.  Seeing the connection is important for advances in statistical process monitoring within Industry 4.0. \nThe seminar will be held in DiSIA ‘s meeting room 205\, and also online. \nIn order to get the link for the webinar you should register by 20 March at noon – sending an email to centro.steering@disia.unifi.it with the subject: “Webinar-Vining”.
URL:https://datascience.unifi.it/index.php/event/disia-steering-itenbis-seminar/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230317T143000
DTEND;TZID=Europe/Rome:20230317T160000
DTSTAMP:20260505T194847
CREATED:20230124T145611Z
LAST-MODIFIED:20230316T083129Z
UID:5001-1679063400-1679068800@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome to another seminar of the D2 Seminar Series of the Florence Center for Data Science!\n\nWe’re happy to host Alberto Cassese and Chiara Bocci from the Department of Statistics\, Computer Science\, Applications “G. Parenti” of the University of Florence. \nThe Seminar will be held both on-site and online Friday 17th of March 2023\, from 2.30-4 pm.\n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe Seminar will be available also online. Please register here to participate online:\nhttps://us02web.zoom.us/webinar/register/WN_DIlCeuHERia0L7SLrmuWGQ\n\nSpeaker: Alberto Cassese\nTitle: "Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data"\nAbstract: A Bayesian hierarchical mixture regression model is developed for studying the association between a multivariate response\, measured as counts on a set of features\, and a set of covariates. We have available RNASeq and DNA methylation data on breast cancer patients at different stages of the disease. We account for heterogeneity and over-dispersion of count data by considering a mixture of negative binomial distributions and incorporate the covariates into the model via a linear modeling construction on the mean components. Our modeling construction employs selection techniques allowing the identification of a small subset of features that best discriminate the samples\, simultaneously selecting a set of covariates associated to each feature. Additionally\, it incorporates known dependencies into the feature selection process via Markov random field priors. On simulated data\, we show how incorporating existing information via the prior model can improve the accuracy of feature selection. In the case study\, we incorporate knowledge on relationships among genes via a gene network\, extracted from the KEGG database. Our data analysis identifies genes that are discriminatory of cancer stages and simultaneously selects significant associations between those genes and DNA methylation sites. A biological interpretation of our findings reveals several biomarkers that can help to understand the effect of DNA methylation on gene expression transcription across cancer stages.\n\nSpeaker: Chiara Bocci\nTitle: "Sampling design for large-scale geospatial phenomena using remote sensing data" \n\nAbstract: In many fields of application\, it is common to be interested in spatially-related phenomena and\, in particular\, to deal with attributes that\, being defined on continuous spatial domains\, are observed on a fine grid. For such kind of data\, selecting the units spatially well spread over the study area allows to collect more information and consequently provides a better estimation of the population parameters. Moreover\, technological advances have led to a growing availability of ready-to-use low-cost spatial data\, like remote sensing data\, which can be used as auxiliary information in the sampling design development process in addition to the units' spatial location.\nSeveral sampling methods in literature simultaneously implement the selection of well-spread samples and the use of auxiliary variables in the selection process. These designs implicitly assume that\, besides having a spatial pattern\, the study variable shows a relationship with some auxiliary variables. However\, this relationship is never known exactly\, and for large-scale phenomena\, it is often unrealistic to assume a unique relationship that holds everywhere. Therefore\, we propose a two-step sampling design to identify when and how it is useful to exploit the auxiliary information\, in addition to the units' spatial location\, in the second step of the sampling selection process. We evaluate the performance of our proposal through Monte Carlo experiments in two simulation studies: one on pseudo-real datasets and one on synthetic datasets. \nThis talk is based on joint work with Saverio Francini and Emilia Rocco.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-8/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230316T120000
DTEND;TZID=Europe/Rome:20230316T120000
DTSTAMP:20260505T194847
CREATED:20230214T122203Z
LAST-MODIFIED:20230214T122234Z
UID:5093-1678968000-1678968000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Chen-Hao Hsu (University of Bamberg) \nTitle: How women’s employment instability affects birth transitions? The moderating role of family policies in 27 European Countries \nAbstract: \nWhy women in some countries are more likely than others to postpone childbirth when facing employment instability? This study uses 2010-2019 EU-SILC panel data to explore whether women’s unemployment and temporary employment affect their first- and second-birth transitions and how such patterns differ across 27 European countries. Results show that while unemployment and temporary employment generally delay women’s first- and second-birth transition\, such effects vary across European countries and depend on the levels of family policy provisions. More generous family cash benefits may mitigate the negative effects of women’s unemployment on the first birth and temporary employment on the second birth transitions. On the other hand\, the effect of women’s employment instability depends less on the length of paid maternity/parental leaves. Most strikingly\, higher childcare coverage rates are associated with more negative effects of women’s temporary employment on the first birth and unemployment on the second birth transitions.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-8/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230306T110000
DTEND;TZID=Europe/Rome:20230306T120000
DTSTAMP:20260505T194847
CREATED:20230227T110459Z
LAST-MODIFIED:20230227T110459Z
UID:5178-1678100400-1678104000@datascience.unifi.it
SUMMARY:DiSIA Seminar
DESCRIPTION:Speaker: Nicola Prezza (Università Ca’ Foscari\, Venezia) \nTitle: A Theory of (co-lex) Ordered Regular Languages\n\nAbstract: NFAs are inherently unordered objects\, but they represent regular languages on which one can very naturally define a total order: for example\, the co-lexicographic order in which words are compared alphabetically from right to left. In this talk I will show that interesting things happen when one tries to map this total order to the states of an accepting NFA for the language: the resulting order of the states is a partial pre-order whose width p turns out to be an important parameter for NFAs and regular languages. For example\, take the classic powerset determinization algorithm for converting an NFA of size n into an equivalent DFA: while a straightforward analysis shows that the size of the resulting DFA is at most 2^n\, we prove that it is actually at most (n-p+1)*2^p. This implies that PSPACE-complete problems such as NFA equivalence or universality are actually easy on NFAs of small width p (the case p=1 – total order – is particularly interesting). Another implication of this theory is that we can compress NFAs to just O(log p) bits per transition while supporting fast membership queries in the substring closure of the language.\n\nBiosketch: Nicola Prezza is an Associate professor at Ca’ Foscari University of Venice\, Italy. He received a PhD in Computer Science from the University of Udine in 2017 with a thesis on dynamic compressed data structures. After that\, he worked as post-doc researcher at the universities of Pisa and Copenhagen (DTU) and as Assistant professor at LUISS (Rome). His current research is focused on the relations existing between data structures\, data compression\, and regular languages. In 2018\, he received the “Best Italian Young Researcher in Theoretical Computer Science” award from the Italian chapter of EATCS. In 2021\, he won an ERC starting grant on the topic of regular language compression and indexing.
URL:https://datascience.unifi.it/index.php/event/disia-seminar-10/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2023/02/logo-DISIA_ECCELLENZa-e1675944756959.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230303T143000
DTEND;TZID=Europe/Rome:20230303T160000
DTSTAMP:20260505T194847
CREATED:20230119T092627Z
LAST-MODIFIED:20230127T123533Z
UID:4989-1677853800-1677859200@datascience.unifi.it
SUMMARY:Seminar of the “D2 Seminar Series” – Florence Center for Data Science
DESCRIPTION:Welcome to another seminar of the D2 Seminar Series of the Florence Center for Data Science! \nWe’re happy to host Augusto Cerqua and Marco Letta from the Department of Social Sciences and Economics of Sapienza University of Rome. \nThe Seminar will be held both on-site and online Friday 3rd of March 2023\, from 2.30-4 pm.\n\nThe seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).\nThe Seminar will be available also online. Please register here to participate online:\n\n\nhttps://us02web.zoom.us/webinar/register/WN_GprXsGF7Ti6sQ8uyNfqKpQ\n\nSpeakers: Augusto Cerqua and Marco Letta\nTitle: "Losing control (group)? The Machine Learning Control Method for counterfactual forecasting"\nAbstract: The standard way of estimating treatment effects relies on the availability of a similar group of untreated units. Without it\, the most widespread counterfactual methodologies cannot be applied. We tackle this limitation by presenting the Machine Learning Control Method (MLCM)\, a new causal inference technique for aggregate data based on counterfactual forecasting via machine learning. The MLCM is suitable for the estimation of individual\, average\, and conditional average treatment effects in evaluation settings with short panels and no controls. The method is formalized within the Rubin’s Potential Outcomes Model and comes with a full set of diagnostic\, performance\, and placebo tests. We illustrate our methodology with an empirical application on the short-run impacts of the COVID-19 crisis on income inequality in Italy\, which reveals a striking heterogeneity in the inequality effects of the pandemic across the Italian local labor markets.
URL:https://datascience.unifi.it/index.php/event/seminar-of-the-d2-seminar-series-florence-center-for-data-science-7/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://datascience.unifi.it/wp-content/uploads/2022/04/SPecial-Guest-Seminar-Series-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230228T143000
DTEND;TZID=Europe/Rome:20230228T153000
DTSTAMP:20260505T194847
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:20260505T194847
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:20260505T194847
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:20260505T194847
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
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BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20230207T100000
DTEND;TZID=Europe/Rome:20230207T110000
DTSTAMP:20260505T194847
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:20260505T194847
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
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END:VCALENDAR