Florence data science

DiSIA Seminar
2023-06-19 14:00 (Europe/Rome) | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: George Luta (Georgetown University)
Title: On Optimal Correlation-Based Prediction
Abstract:
We 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.

DiSIA Welcome Seminar Giammarco Alderotti, Marco Cozzani, Maria Veronica Dorgali
2023-09-06 12:00 - 14:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Giammarco Alderotti
Title: A journey, just begun, into family demography
Speaker: Marco Cozzani
Title: The heterogeneous consequences of adverse events
Speaker: Maria Veronica Dorgali
Title: Understanding health behaviours through the lens of psychology

InterConnect webinar: Daniel Kowal
2023-10-13 16:00 - 17:30
On-site and online seminar: Friday 13th October 2023 from 4.00 – 5.00 PM
Title: Nonparametric Copula Models for Multivariate, Mixed, and Missing Data
Speaker: Daniel Kowal (Rice University)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT
Modern 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.
BIO
Dr. 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.
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InterConnect webinar: Eric Chi
2023-10-27 16:00 - 17:30
On-site and online seminar: Friday 27th October 2023 from 4.00 – 5.00 PM
Title: Proximal MCMC for Approximate Bayesian Inference of Constrained and Regularized Estimation
Speaker: Eric Chi (Rice University)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT
In 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.

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Online Open Day MD2SL 2023!
2023-11-06 17:00 - 18:00 | Online
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.

If you are interested you can register for the event using this link.

InterConnect webinar: Qiwei Li
2023-11-10 16:00 - 17:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
On-site and online seminar: Friday 10th November 2023 from 4.00 – 5.00 PM
Title: When statistics meets AI: Bayesian modeling of spatial biomedical data
Speaker: Qiwei Li (University of Texas at Dallas)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT
Statistics 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.
The 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.
The 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.

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InterConnect webinar: Flavio Soares Correa da Silva
2023-11-24 16:00 - 17:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
On-site and online seminar: Friday 24th November 2023 from 4.00 – 5.00 PM
Title: General Artificial Intelligence can be OK, Artificial General Intelligence cannot
Speaker: Flavio Soares Correa da Silva (University of São Paulo – Brazil)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT
Machine 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.

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InterConnect webinar: Lorenzo Tamellini
2024-01-12 14:30 - 15:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
On-site and online seminar: Friday 12th January 2024 from 2.30 – 3.30 PM
Title: A multi-fidelity method for uncertainty quantification in engineering problems
Speaker: Lorenzo Tamellini (CNR-IMATI pavia)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link

ABSTRACT

Computer 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.
UQ 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,
it is easy to see how these techniques can quickly become very computationally demanding.
In 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
means 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.
In 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.
References:

1) C. Piazzola, L. Tamellini,
The Sparse Grids Matlab Kit – a Matlab Implementation of Sparse Grids for High-Dimensional Function Approximation and Uncertainty Quantification,
ACM Transactions on Mathematical Software, 2023
2) C. Piazzola, L. Tamellini, R. Pellegrini, R. Broglia, A. Serani, and M. Diez.

Comparing Multi-Index Stochastic Collocation and Multi-Fidelity Stochastic Radial Basis Functions for Forward
Uncertainty Quantification of Ship Resistance. Engineering with Computers, 2022
3) M. Chiappetta, C. Piazzola, L. Tamellini, A. Reali, F. Auricchio, M. Carraturo
Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing arXiv:2311.03823

BIO
Lorenzo Tamellini is a senior researcher (primo ricercatore) at the Institute for Applied Mathematics and Information Technologies “Enrico Magenes”,
a research institute of the Italian National Research Council (CNR-IMATI) in Pavia.
He was post-doc for 3 years at Ecole Polytechnique Fédérale de Lausanne (EPFL) with prof. Fabio Nobile,
and PhD student at Politecnico di Milano (MOX lab, Department of Mathematics),
where he got a PhD in Mathematical Models and Methods for Engineering in March 2012, again under the supervision of prof. Nobile.

InterConnect webinar: Francesco Lagona
2024-02-23 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
On-site and online seminar: Friday, 23rd February 2024 from 12.00 – 13.00 PM
Title: Nonhomogeneous hidden semi-Markov models for environmental toroidal data
Speaker: Francesco Lagona (University of Roma Tre)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link

ABSTRACT

A 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.

BIO

Francesco 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.
Francesco’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.

InterConnect webinar: Silvia Liverani
2024-03-15 11:00 - 12:00
On-site and online seminar: Friday, 15th March 2024 from 11.00 – 12.00 AM
Title: Bayesian modelling for spatially misaligned health areal data
Speaker: Silvia Liverani (Queen Mary University of London)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link

ABSTRACT

The 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.

BIO

Silvia 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.

InterConnect webinar: Anna Monreale
2024-03-28 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
On-site and online seminar: 28th March 2024 from 12.00 – 13.00 AM
Title: Interplay between Privacy and Explainable AI
Speaker: Anna Monreale (Università di Pisa)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online: link
ABSTRACT
In 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?

BIO
Anna 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.

Webinar: Corrado Di Guilmi
2024-05-10 14:00 - 15:00
On-site and online seminar: Friday, 10th May 2024 from 2.00 – 3.00 PM
Title: Does the supply network shape the firm size distribution? The Japanese case
Speaker: Corrado Di Guilmi (University of Florence)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT

The 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.

BIO

Corrado 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.
His research mainly focuses on computational, labor, and financial macroeconomics.

Webinar: Marco Scutari
2024-05-24 12:00 - 13:00
On-site and online seminar: Friday, 24th May 2024 from 12.00 – 1.00 PM
Title: Causal Modelling in Space and Time
Speaker: Marco Scutari (Dalle Molle Institute)
Location: Aula 205 (ex 32) – Viale Morgagni 59 (jointly organized with DiSIA)
Please register here to participate online : link
ABSTRACT

The 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.

BIO

Marco 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.

Webinar: Antonella Falini
2024-10-15 14:30 - 15:30
On-site and online seminar: Tuesday, 15th October 2024 from 2.30 – 3.30 PM
Title: Spline Quasi-Interpolation as an effective tool to enhance clustering performance
Speaker: Antonella Falini (Università degli studi di Bari)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT

In 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.

BIO

Antonella 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.

Webinar: Tom Bartlett
2025-04-07 14:30 - 15:30
On-site and online seminar: Monday, April 7th 2025 from 2.30 – 3.30 PM
Title: Using stochastic network theory to inform unsupervised learning from single-cell genomic count data
Speaker: Tom Bartlett (UCL)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT

Important 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.

Webinar: Mohammad Jafari Jozani
2025-07-01 14:00 - 15:00
On-site and online seminar: Tuesday, July 1st 2025 from 2.00 – 3.00 PM
Title: Rethinking Support in SVMs: An Elite-Driven Approach to Classification
Speaker: Mohammad Jafari Jozani (University of Manitoba)
Location: Aula 205 (ex 32) – Viale Morgagni 59
Please register here to participate online : link
ABSTRACT

Support 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.
We 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.
Through 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.
