BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Florence data science - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Florence data science
X-ORIGINAL-URL:https://datascience.unifi.it
X-WR-CALDESC:Events for Florence data science
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Rome
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20221030T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20231029T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20270328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20271031T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20260227T140000
DTEND;TZID=Europe/Rome:20260227T150000
DTSTAMP:20260501T170233
CREATED:20260218T112159Z
LAST-MODIFIED:20260218T133616Z
UID:6553-1772200800-1772204400@datascience.unifi.it
SUMMARY:Seminar: Babak Shahbaba
DESCRIPTION:On-site-only seminar: Friday\, February 27th 2026\, from 2.00 – 3.00 PM \nTitle: Latent Representation Learning of Brain Dynamics and Structure in Goal-Directed Behavior\nSpeaker: Babak Shahbaba (University of California\, Irvine)\n  \nLocation: Room 205 – Viale Morgagni 59\nABSTRACT \n\n\nThe ability to plan actions in order to achieve specific goals is essential for navigating daily life\, from decisions about basic needs (e.g.\, food and medical care) to long-term objectives (e.g.\, financial planning\, education\, and preventive healthcare). Understanding how the healthy brain supports planning can help clarify how this capacity breaks down in a range of cognitive disorders\, including age-related cognitive decline\, Alzheimer’s disease\, and addiction. In this talk\, we present several new statistical machine learning methods designed to identify the algorithmic representations and structures underlying goal-directed decisions—the latent neural trajectories over time that give rise to particular choices. We begin by identifying patterns of ensemble neural activity over time using a latent representation learning approach. To address the challenge of integrating information across heterogeneous subjects\, we introduce a new method\, Integrated Latent Alignment (ILA)\, which combines deep representation learning with optimal transport (OT) theory. We then turn to the problem of characterizing structural connectivity among neurons to identify the activity patterns that generate these neural trajectories. To this end\, we propose a new Graph Neural Network (GNN) model that leverages graph topology by representing brain structure as a complex network of interconnected units. The model consists of two components: an estimation component that captures latent relationships between neural features and trial outcomes\, and an interpretation component that identifies an influential and compact subgraph by selecting a subset of node features that play a central role in the model’s predictions. \n\nBIO \nBabak Shahbaba is a Professor of Statistics at UC Irvine and a Fellow of the American Statistical Association. Before joining UC Irvine\, he was a Postdoctoral Fellow at Stanford University and received his PhD from the University of Toronto under the supervision of Radford Neal. \nShahbaba’s research focuses on Bayesian inference and statistical machine learning\, with applications to data-intensive biomedical problems. His work spans a broad range of areas\, including statistical methodologies (e.g.\, Bayesian nonparametrics\, stochastic process modeling\, data integration\, and deep learning)\, computational techniques (e.g.\, scalable MCMC)\, and a variety of applied and collaborative projects in neuroscience\, genomics\, and the health sciences.
URL:https://datascience.unifi.it/index.php/event/seminar-babak-shahbaba/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2026/02/Babak-e1771413780464.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20260130T110000
DTEND;TZID=Europe/Rome:20260130T120000
DTSTAMP:20260501T170233
CREATED:20260124T120528Z
LAST-MODIFIED:20260124T120528Z
UID:6454-1769770800-1769774400@datascience.unifi.it
SUMMARY:Seminar: Peter McCullagh
DESCRIPTION:On-site-only seminar: Friday\, January 30th 2026\, from 11.00 – 12.00 AM \nTitle: Statistics of speciation and the evolution of reproductive isolation\nSpeaker: Peter McCullagh (University of Chicago)\n  \nLocation: Room 205 – Viale Morgagni 59\nABSTRACT \n\n\nThis talk is concerned with statistical models for the analysis of an experiment in evolutionary biology. The experiment described by Villa et al. (PNAS 2019) is concerned with adaptive evolution of an organism in response to selective pressure. The catchy title “Rapid experimental evolution of reproductive isolation from a single natural population” implies that evolution is observed on short time scales on the order of 50 generations. I will describe the experimental design\, the host-parasite system\, the randomization scheme and the sampling protocol. One flaw of the statistical model leading to the advertised conclusion is that it contradicts the randomization scheme. An alternative model (due to Cavalli-Sforza & Edwards (1967)) using Brownian motion for the temporal evolution of a quantitative trait is shown to conform to the randomization scheme. It is also a substantially better fit to the data. Unfortunately\, the alternative analysis shows that\, while adaptive evolution is indeed possible\, the authors’ data provides zero evidence in support of that conclusion. \n\nBIO \nPeter McCullagh is a Northern Irish–born statistician and John D. MacArthur Distinguished Service Professor Emeritus in the Department of Statistics at the University of Chicago. He completed his Ph.D. at Imperial College London under the supervision of Anthony Atkinson and David Cox and has made foundational contributions to statistical theory and methods\, including work on generalized linear models and tensor methods in statistics. \nHe is co-author\, with John Nelder\, of the influential text Generalized Linear Models\, widely used across disciplines\, and his research spans areas such as quasi-likelihood\, analysis of ordinal data\, and the mathematical foundations of statistical models. \nProfessor McCullagh has received numerous honours\, including the COPSS Presidents’ Award (1990)\, the Royal Statistical Society’s Guy Medal in Bronze and Silver (1983 and 2005)\, and election as a Fellow of the Royal Society and the American Academy of Arts and Sciences.
URL:https://datascience.unifi.it/index.php/event/seminar-peter-mccullagh/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2026/01/Pic_PMc-e1769255857389.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20250701T140000
DTEND;TZID=Europe/Rome:20250701T150000
DTSTAMP:20260501T170233
CREATED:20250606T150159Z
LAST-MODIFIED:20260124T111409Z
UID:6178-1751378400-1751382000@datascience.unifi.it
SUMMARY:Webinar: Mohammad Jafari Jozani
DESCRIPTION:On-site and online seminar: Tuesday\, July 1st 2025 from 2.00 – 3.00 PM \nTitle: Rethinking Support in SVMs: An Elite-Driven Approach to Classification\nSpeaker: Mohammad Jafari Jozani (University of Manitoba)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \n\n\nSupport Vector Machines (SVMs) are a cornerstone of classification methodology\, where decision boundaries are shaped by support vectors determined via a chosen loss function. However\, different loss functions yield different sets of support vectors\, resulting in variable classification outcomes. This conventional dependence on a single loss function often obscures broader structural insights—particularly the presence of observations that consistently act as support vectors across multiple SVM configurations. These persistently influential points point to a new paradigm in classifier design. \nWe propose Elite-Driven Support Vector Machines (EDSVM)\, a novel framework that enhances classification performance by identifying and amplifying the role of these elite observations. These elites are data points that recur as support vectors across a range of loss functions and decision boundaries. To harness their importance\, we design new classification-calibrated loss functions that embed elite-weighted influence directly into the training process. \nThrough rigorous theoretical development and extensive empirical evaluation—spanning both synthetic and real-world datasets—we show that EDSVM outperforms classical SVMs in linear and nonlinear settings. This work advances the foundations of SVM methodology and offers practical tools for high-stakes\, data-sensitive applications.
URL:https://datascience.unifi.it/index.php/event/webinar-mohammad-jafari-jozani/
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20250407T143000
DTEND;TZID=Europe/Rome:20250407T153000
DTSTAMP:20260501T170233
CREATED:20250304T112838Z
LAST-MODIFIED:20250304T113609Z
UID:6104-1744036200-1744039800@datascience.unifi.it
SUMMARY:Webinar: Tom Bartlett
DESCRIPTION:On-site and online seminar: Monday\, April 7th 2025 from 2.30 – 3.30 PM \nTitle: Using stochastic network theory to inform unsupervised learning from single-cell genomic count data\nSpeaker: Tom Bartlett (UCL)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \n\nImportant tasks in the study of genomic data include the identification of groups of similar cells (for example by clustering)\, and visualisation of data summaries (for example by dimensional reduction). In this talk\, I will present a novel view of these tasks in the context of single-cell genomic data. To do so\, I propose modelling the observed count-matrices of genomic data by representing these measurements as a bipartite network with multi-edges. Starting with this first-principles network model of the raw data\, I will show improvements in clustering single cells via a suitably-identified d-dimensional Laplacian Eigenspace (LE) using a Gaussian mixture model (GMM-LE)\, and apply UMAP to non-linearly project the LE to two dimensions for visualisation (UMAP-LE). From this first-principles viewpoint\, the LE representation of the data-points estimates transformed latent positions (of genes and cells)\, under a latent position statistical model of nodes in a bipartite stochastic network. By applying this proposed methodology to data from three recent genomics studies in different biological contexts\, I will show how clusters of cells independently learned by this proposed methodology are found to correspond to cells expressing specific marker genes that were independently defined by domain experts\, with an accuracy that is competitive with the industry-standard for these data. I will then show how this novel view of these data can provide unique insights\, leading to the identification of a LE breast-cancer biomarker that significantly predicts long-term patient survival outcome in two independent validation cohorts with data from 1904 and 1091 individuals.
URL:https://datascience.unifi.it/index.php/event/webinar-tom-bartlett/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20241015T143000
DTEND;TZID=Europe/Rome:20241015T153000
DTSTAMP:20260501T170233
CREATED:20240925T154739Z
LAST-MODIFIED:20240925T154739Z
UID:6004-1729002600-1729006200@datascience.unifi.it
SUMMARY:Webinar: Antonella Falini
DESCRIPTION:On-site and online seminar: Tuesday\, 15th October 2024 from 2.30 – 3.30 PM \nTitle: Spline Quasi-Interpolation as an effective tool to enhance clustering performance\nSpeaker: Antonella Falini (Università degli studi di Bari)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \n\nIn this seminar spline quasi-interpolation is presented as a valid tool to enhance the performance of unsupervised-learning based approaches in the context of clustering. Due to its local nature\, quasi-interpolation has a reduced computational cost compared to global methods (like interpolation); moreover\, data collected from realistic scenarios are usually affected by errors\, therefore\, global methods might be too stringent\, beside suffering from overfitting. As a first application\, quasi-interpolation is adopted to derive a smoothing model that can be combined with common clustering techniques for the task of anomaly detection in time-series data. Secondly\, in the context of model based clustering\, quasi-interpolation can be used to construct an estimation of the monovariate empirical marginal densities which underlie suitable multivariate distributions of the given data. The adopted quasi-interpolant operator is proved to be a uniformly consistent estimator of the sought density which outperforms classical approaches. In particular\, copulas will be employed to construct the final clustering model. For both the applications\, numerical tests will be shown on artificial and real datasets. \n\n\nBIO \n\nAntonella Falini is currently a researcher at the Computer Science Department\, University of Bari Aldo Moro and her research activities focus on Big Data Analytics and numerical methods for artificial intelligence and machine learning. She got her PhD in 2016 with a Marie-Curie fellowship at the Institute of Applied Geometry in Linz\, Austria. Then she won two INdAM fellowships in numerical analysis under the supervision of professor Carlotta Giannelli at the University of Firenze and since 2019 she is working as research assistant at the University of Bari with funding projects PON AIM and FAIR.
URL:https://datascience.unifi.it/index.php/event/webinar-antonella-falini/
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2024/09/AntonellaFalini.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20240524T120000
DTEND;TZID=Europe/Rome:20240524T130000
DTSTAMP:20260501T170233
CREATED:20240424T103114Z
LAST-MODIFIED:20240429T150953Z
UID:5808-1716552000-1716555600@datascience.unifi.it
SUMMARY:Webinar: Marco Scutari
DESCRIPTION:On-site and online seminar: Friday\, 24th May 2024 from 12.00 – 1.00 PM \nTitle: Causal Modelling in Space and Time\nSpeaker: Marco Scutari (Dalle Molle Institute)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59 (jointly organized with DiSIA)\nPlease register here to participate online : link \nABSTRACT \n\n\nThe assumption that data are independent and identically distributed samples from a single underlying population is pervasive in statistical modelling. However\, most data do not satisfy this assumption. Regression models have been extended to deal with structured data collected over time\, spaces\, and different populations. But what about causal network models\, which are built on regression? In this talk\, we will discuss how to produce causal models that can answer crucial causal questions in environmental sciences\, epidemiology and other challenging domains that produce data with complex structures. \n\n\n\nBIO \n\nMarco Scutari is a Senior Researcher at Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)\, Switzerland. He has held positions in statistics\, statistical genetics and machine learning in the UK and Switzerland since completing his PhD in Statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data\, as well as statistical computing and software engineering.
URL:https://datascience.unifi.it/index.php/event/webinar-marco-scutari/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2024/04/scutari.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20240510T140000
DTEND;TZID=Europe/Rome:20240510T150000
DTSTAMP:20260501T170233
CREATED:20240410T094813Z
LAST-MODIFIED:20240410T094813Z
UID:5789-1715349600-1715353200@datascience.unifi.it
SUMMARY:Webinar: Corrado Di Guilmi
DESCRIPTION:On-site and online seminar: Friday\, 10th May 2024 from 2.00 – 3.00 PM \nTitle: Does the supply network shape the firm size distribution? The Japanese case\nSpeaker: Corrado Di Guilmi (University of Florence)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \n\nThe paper presents an investigation on how the upward transmission of demand shocks in the Japanese supply network influences the growth rates of firms and\, consequently\, shapes their size distribution. Through an empirical analysis\, analytical decomposition of the growth rates’ volatility\, and numerical simulations\, we obtain several original results. We find that the Japanese supply network has a bow-tie structure in which firms located in the upstream layers display a larger volatility in their growth rates. As a result\, the Gibrat’s law breaks down for upstream firms whereas downstream firms are more likely to be located in the power law tail of the size distribution. This pattern is determined by the amplification of demand shocks hitting downstream firms\, and the magnitude of this amplification depends on the network structure and on the relative market power of downstream firms. Finally\, we observe that in an almost complete network\, in which there are no upstream or downstream firms\, the power-law tail in firm size distribution disappears. An important implication of our results is that aggregate demand shocks can affect the economy both directly\, through the reduction in output for downstream firms\, and also indirectly by shaping the firm size distribution.\n\n\nBIO \n\nCorrado Di Guilmi is Associate Professor at the University of Florence. He is co-director of the program in Behavioral Macroeconomics and Complexity at the Centre of Applied Macroeconomic Analysis at the Australian National University and Research Fellow at the Center for Computational Social Science\, Kobe University.\nHis research mainly focuses on computational\, labor\, and financial macroeconomics.\n\n 
URL:https://datascience.unifi.it/index.php/event/webinar-corrado-di-guilmi/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2024/03/Di-Guilmi.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20240328T120000
DTEND;TZID=Europe/Rome:20240328T130000
DTSTAMP:20260501T170233
CREATED:20240304T095341Z
LAST-MODIFIED:20240322T125012Z
UID:5773-1711627200-1711630800@datascience.unifi.it
SUMMARY:InterConnect webinar: Anna Monreale
DESCRIPTION:On-site and online seminar:  28th March 2024 from 12.00 – 13.00 AM \nTitle: Interplay between Privacy and Explainable AI\nSpeaker: Anna Monreale (Università di Pisa)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online: link \nABSTRACT\nIn recent years we are witnessing the diffusion of AI systems based on powerful machine learning models which find application in many critical contexts such as medicine\, financial market\, credit scoring\, etc. In such contexts\, it is particularly important to design Trustworthy AI systems while guaranteeing the interpretability of their decisional reasoning\, and privacy protection and awareness. In this talk\, we will explore the possible relationships between these two relevant ethical values to take into consideration in Trustworthy AI. We will answer research questions such as: how explainability may help privacy awareness? Can explanations jeopardize individual privacy protection?\n\n\nBIO\nAnna Monreale is an associate professor at the Computer Science Department of the University of Pisa and a member of the Knowledge Discovery and Data Mining Laboratory (KDD-Lab)\, a joint research group with the Information Science and Technology Institute of the National Research Council in Pisa. She has been a visiting student at Department of Computer Science of the Stevens Institute of Technology (Hoboken\, New Jersey\, USA) (2010). Her research interests include big data analytics\, social networks and the privacy issues raising in mining these kinds of social and human sensitive data. In particular\, she is interested in the evaluation of privacy risks during analytical processes and in the design of privacy-by-design technologies in the era of big data. She earned her Ph.D. in computer science from the University of Pisa in June 2011 and her dissertation was about privacy-by-design in data mining.\n 
URL:https://datascience.unifi.it/index.php/event/interconnect-webinar-anna-monreale/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20240315T110000
DTEND;TZID=Europe/Rome:20240315T120000
DTSTAMP:20260501T170233
CREATED:20240216T090248Z
LAST-MODIFIED:20240318T171158Z
UID:5751-1710500400-1710504000@datascience.unifi.it
SUMMARY:InterConnect webinar: Silvia Liverani
DESCRIPTION:On-site and online seminar: Friday\, 15th March 2024 from 11.00 – 12.00 AM \nTitle: Bayesian modelling for spatially misaligned health areal data\nSpeaker: Silvia Liverani (Queen Mary University of London)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \n  \nABSTRACT \n\n\nThe objective of disease mapping is to model data aggregated at the areal level. In some contexts\, however\, (e.g. residential histories\, general practitioner catchment areas) when data is arising from a variety of sources\, not necessarily at the same spatial scale\, it is possible to specify spatial random effects\, or covariate effects\, at the areal level\, by using a multiple membership principle. In this talk I will investigate the theoretical underpinnings of these application of the multiple membership principle to the CAR prior\, in particular with regard to parameterisation\, properness and identifiability\, and I will  present the results of an application of the multiple membership model to diabetes prevalence data in South London\, together with strategic implications for public health considerations.\n\n\n\nBIO \n\nSilvia Liverani is Reader in Statistics\, and Head of the Research Centre in Probability\, Statistics and Data Science in the School of Mathematical Sciences at Queen Mary University of London\, UK. Her research interests are in Bayesian statistics\, and in particular Dirichlet proces mixture models\, clustering and spatio-temporal modelling\, with application in epidemiology and biodiversity.
URL:https://datascience.unifi.it/index.php/event/interconnect-webinar-silvia-liverani/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2024/02/Silvia-Liverani-pic-2021-1-1.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20240223T120000
DTEND;TZID=Europe/Rome:20240223T130000
DTSTAMP:20260501T170233
CREATED:20240210T145549Z
LAST-MODIFIED:20240216T100930Z
UID:5746-1708689600-1708693200@datascience.unifi.it
SUMMARY:InterConnect webinar: Francesco Lagona
DESCRIPTION:On-site and online seminar: Friday\, 23rd February 2024 from 12.00 – 13.00 PM \nTitle: Nonhomogeneous hidden semi-Markov models for environmental toroidal data\nSpeaker: Francesco Lagona (University of Roma Tre)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \n  \nABSTRACT \n\nA novel hidden semi-Markov model is proposed to segment bivariate time series of wind and wave directions according to a finite number of latent regimes and\, simultaneously\, estimate the influence of time-varying covariates on the process’ survival under each regime. The model integrates survival analysis and directional statistics by postulating a mixture of toroidal densities\, whose parameters depend on the evolution of a semi-Markov chain\, which is in turn modulated by time-varying covariates through a proportional hazards assumption. Parameter estimates are obtained using an EM algorithm that relies on an efficient augmentation of the latent process. Fitted on a time series of wind and wave directions recorded in the Adriatic sea\, the model offers a clear-cut description of sea state dynamics in terms of latent regimes and captures the influence of time-varying weather conditions on the duration of such regimes.\n\n\n\nBIO \n\nFrancesco Lagona is professor of statistics at the University of Roma Tre\, where he teaches statistical modelling and environmental statistics. He is the coordinator of GRASPA (www.graspa.org)\, the most important research network of Italian environmental statisticians\, active since 1995 and a standing group of the Italian Statistical Society for Environmental Statistics\, sustainability and territorial safety since May 2013.  \nFrancesco’s research activity focuses on new models for the analysis of complex multivariate data that are dependent across time and space. Principal proposals are in the area multivariate hidden Markov models\, an extension of multivariate finite mixture models to both the spatial and the temporal setting. Within this context\, he has developed new computational methods that allow for efficient parameter estimation and model selection. Such proposals have been applied to address classification issues with applications in legislative production\, sea waves and currents\, environmental pollution\, wildfire occurrences\, cause-specific mortality\, cardiovascular measurements and biological image analysis.
URL:https://datascience.unifi.it/index.php/event/interconnect-webinar-francesco-lagona/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2024/02/Lagona.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20240112T143000
DTEND;TZID=Europe/Rome:20240112T153000
DTSTAMP:20260501T170233
CREATED:20240106T185936Z
LAST-MODIFIED:20240209T153229Z
UID:5730-1705069800-1705073400@datascience.unifi.it
SUMMARY:InterConnect webinar: Lorenzo Tamellini
DESCRIPTION:On-site and online seminar: Friday 12th January 2024 from 2.30 – 3.30 PM \nTitle: A multi-fidelity method for uncertainty quantification in engineering problems\nSpeaker: Lorenzo Tamellini (CNR-IMATI pavia)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \n  \nABSTRACT \n\nComputer simulations\, which are nowadays a fundamental tool in every field of science and engineering\, need to be fed with parameters such as physical coefficients\, initial states\, geometries\, etc. This information is however often plagued by uncertainty: values might be e.g. known only up to measurement errors\,  or be intrinsically random quantities (such as winds or rainfalls). Uncertainty Quantification (UQ) is a research field devoted to dealing efficiently with uncertainty in computations.\nUQ techniques typically require running simulations for several (carefully chosen) values of the uncertain input parameters (modeled as random variables/fields)\, and computing statistics of the outputs of the simulations (mean\, variance\, higher order moments\, pdf\, failure probabilities)\, to provide decision-makers with quantitative information about the reliability of the predictions. Since each simulation run typically requires solving one or more Partial Differential Equations (PDE)\, which can be a very expensive operation\,\nit is easy to see how these techniques can quickly become very computationally demanding.\nIn recent years\, multi-fidelity approaches have been devised to lessen the computational burden: these techniques explore the bulk of the variability of the outputs of the simulation by\nmeans of low-fidelity/low-cost solvers of the underlying PDEs\, and then correct the results by running a limited number of high-fidelity/high-cost solvers. They also provide the user a so-called “surrogate-model” of the system response\, that can be used to approximate the outputs of the system without actually running any further simulation.\nIn this talk we illustrate a multi-fidelity method (the so-called multi-index stochastic collocation method) and its application to a couple of engineering problems. If time allows\, we will also briefly touch the issue of coming upwith good probability distributions for the uncertain parameters\, e.g. by Bayesian inversion techniques.\nReferences:\n\n1) C. Piazzola\, L. Tamellini\,\nThe Sparse Grids Matlab Kit – a Matlab Implementation of Sparse Grids for High-Dimensional Function Approximation and Uncertainty Quantification\,\nACM Transactions on Mathematical Software\, 2023\n2) C. Piazzola\, L. Tamellini\, R. Pellegrini\, R. Broglia\, A. Serani\, and M. Diez. \n\nComparing Multi-Index Stochastic Collocation and Multi-Fidelity Stochastic Radial Basis Functions for Forward\nUncertainty Quantification of Ship Resistance. Engineering with Computers\, 2022\n3) M. Chiappetta\, C. Piazzola\, L. Tamellini\, A. Reali\, F. Auricchio\, M. Carraturo\nData-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing arXiv:2311.03823 \n\n\n\nBIO \nLorenzo Tamellini is a senior researcher (primo ricercatore) at the Institute for Applied Mathematics and Information Technologies “Enrico Magenes”\,\na research institute of the Italian National Research Council (CNR-IMATI) in Pavia.\nHe was post-doc for 3 years at Ecole Polytechnique Fédérale de Lausanne (EPFL) with prof. Fabio Nobile\,\nand PhD student at Politecnico di Milano (MOX lab\, Department of Mathematics)\,\nwhere he got a PhD in Mathematical Models and Methods for Engineering in March 2012\, again under the supervision of prof. Nobile.
URL:https://datascience.unifi.it/index.php/event/interconnect-webinar-lorenzo-tamellini/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2024/01/lorenzo_tamellini.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20231124T160000
DTEND;TZID=Europe/Rome:20231124T170000
DTSTAMP:20260501T170233
CREATED:20231019T174413Z
LAST-MODIFIED:20231019T174543Z
UID:5700-1700841600-1700845200@datascience.unifi.it
SUMMARY:InterConnect webinar: Flavio Soares Correa da Silva
DESCRIPTION:  \nOn-site and online seminar: Friday 24th November 2023 from 4.00 – 5.00 PM \nTitle: General Artificial Intelligence can be OK\, Artificial General Intelligence cannot\nSpeaker: Flavio Soares Correa da Silva (University of São Paulo – Brazil)\nLocation: Aula 205 (ex 32) – Viale Morgagni 59\nPlease register here to participate online : link \nABSTRACT \nMachine Learning (ML) results have been propelled forward in recent years\, attracting a lot of attention from the general press and large corporations. ML is one subfield belonging to the broader scientific and technological initiative named Artificial Intelligence (AI) in the mid-1950s. In this presentation\, we shall go briefly across the history and foundations of AI\, in order to (1) appreciate from where it came and how\, to some extent\, it has preserved methodological coherence since its infancy\, (2) attempt to interpret recent results from a broader perspective\, and (3) formulate possible future steps that can ensure that the relevance of AI continues to grow and be judged as positive from both scientific and technological perspectives. Along the way we shall dispel some myths about the practicality of pursuing the development of an Artificial General Intelligence. \n  \nWebpage \n 
URL:https://datascience.unifi.it/index.php/event/interconnect-webinar-flavio-soares-correa-da-silva/
LOCATION:DiSIA\, viale Morgagni 59\, Viale Morgagni 59\, Firenze\, Italy
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://datascience.unifi.it/wp-content/uploads/2023/10/152_l_res_bw.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20231110T160000
DTEND;TZID=Europe/Rome:20231110T170000
DTSTAMP:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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:20260501T170233
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
END:VCALENDAR