Florence data science

Webinar: Marco Scutari
2024-05-24 12:00 - 13:00 (Europe/Rome)
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: 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.

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.

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: 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: 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: 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: 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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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: 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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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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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

DiSIA Seminar
2023-06-19 14:00 | 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 Seminar
2023-06-08 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Laura D’Angelo (Dipartimento di Economia, Metodi Quantitativi e Strategie d’Impresa, Università Milano Bicocca)
Title: Modeling grouped data via finite nested mixture models: an application to calcium imaging data
Abstract:
Recent 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.

DiSIA Seminar
2023-06-05 14:00 - 15:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Matteo Mio – CNRS, ENS-Lyon
Title: An introduction to Quantitative Algebras
Abstract:
Equational 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.
[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

DiSIA Double Welcome Seminar - Fiammetta Menchetti & Marta Pittavino
2023-06-01 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Fiammetta Menchetti
Title: From high school creativity to cultural heritage conservation: a journey in causal inference
Abstract:
In this talk, I will provide an overview of my research activity in causal inference for time series data. Starting with
C-ARIMA and Bayesian multivariate structural time series models that were part of my PhD thesis, I’ll then give
you a glimpse into my recent collaborations, including a randomized control trial to assess the impact of FABLAB’s
courses on the creativity of Italian high-school students and a machine learning method for counterfactual forecasting
for short panels in the absence of controls. The talk will then focus on the PNRR research activity, which aims
to study the evolution of the web cracks on Brunelleschi’s Santa Maria del Fiore Dome as part of an ongoing and
fascinating project on cultural heritage conservation

Speaker: Marta Pittavino
Title: A tale on statistical methods, and their applications, developed around Europe
Abstract:

In this talk, I will present some statistical methods that I exploited for my research. I will begin by presenting
the Additive Bayesian Network (ABN) multivariate methodology, a data-driven technique particularly suitable for
inter-dependent data. I will show two applications of ABN in the veterinary epidemiology field. Then, I will
move to the illustration of a Bayesian hierarchical model applied to nutritional epidemiology. Specifically, this
model relates a measurement error model with a disease model via an exposure model. Afterwards, I will provide
examples of quantile regression and forecasting techniques applied to a specific philanthropic-social dataset on
charitable deductions for tax incentives for the Canton of Geneva population. Last, but not least, I will conclude
this statistical modelling journey by introducing the current demographic project for the “Forecasting kinship
networks and kinless individuals” and show the first preliminary results of kinless of countries around Europe.

Young researcher Seminar – Florence Center for Data Science
2023-05-22 11:00 - 12:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Welcome another seminar of the “Young Researchers Seminar Series“!
The Seminar will be held both on-site and online Monday 22th May 2023 at 11:00 AM.

Our guest will be Matt DosSantos DiSorbo from Harvard Business School.

The seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).
The seminar will be available also online. Please register here to participate online:

https://us02web.zoom.us/webinar/register/WN_4-1IeZgrS6WpY-En-JKoZw

Title: Starting Strong: The Impact of Early Interventions on Employee Outcomes
Abstract:
In 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.

DiSIA Seminar
2023-05-16 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Rosario Barone – University of Rome “Tor Vergata”
Title: Bayesian time-interaction point process
Abstract:
In 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.
REFERENCES 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.

Seminar of the "Special Guest Seminar Series" - Björn Bornkamp
2023-05-11 16:30 - 17:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
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!
The Seminar will be held on-site and online Thursday 11th May 2023 from 4.30 – 5.30 PM
Our guest will be Björn Bornkamp – Statistical Methodologist at Novartis.
The seminar will be held in Aula 205 (ex 32) – Viale Morgagni 59.
The Seminar will be available also online.
Please register here to participate online: https://us02web.zoom.us/webinar/register/WN_RgnfqAVYSJaA5JYRePVNUw
Title: Estimand and analysis strategies for recurrent event endpoints in the presence of a terminal event
Abstract:
Recurrent 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.

DiSIA seminar
2023-04-20 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Maurizio Pisati and Mario Lucchini (Università di Milano-Bicocca)
Title: L’indagine ITA.LI quantitativa: Disegno di campionamento, pesi campionari e varianza degli stimatori
Abstract:
Lo 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.
The event is organized in collaboration with UPS – Population and Society Unit.

DiSIA "Welcome" Seminar - Daniele Castellana, Andrea Marino, Francesco Tiezzi
2023-04-17 12:00 - 13:30 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Daniele Castellana
Title: Machine Learning in Structured Domains
Abstract:
Machine 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.

Speaker: Andrea Marino
Title: Algorithms for the Analysis of (Temporal) Graphs
Abstract:
In 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.

Speaker: Francesco Tiezzi
Title: A Tale on Domain-Specific System Engineering: the Case of Multi-Robot Systems
Abstract:
The 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.

DiSIA Seminar
2023-04-04 15:00 - 16:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Andrea Meilan Vila (Department of Statistics, Universidad Carlos III de Madrid)
Title: Kernel regression estimation for a circular response and different types of covariates
Abstract:
The 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.

DiSIA Seminar
2023-03-30 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Andrea Sottosanti (Università di Padova)
Title: Co-clustering Models for Spatial Transcriptomics
Abstract:
Spatial 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.

During 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.
By 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.

Economics Lecture by Nobel Prize 2021: Guido W. Imbens "Combining Experimental and Observational Data"
2023-03-28 11:30 - 13:00 | Auditorium A – Viale Morgagni 40, Auditorium A- Polo Didattico, Viale Morgani, 40 - Università degli Studi di Firenze, Firenze, Italy
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.

The 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

The event does not require prior registration.

Title: Combining Experimental and Observational Data

Presented by Guido W. Imbens (Stanford University), Nobel Prize in Economics 2021

Abstract:
In the social sciences there has been an increase in interest in randomized experiments to estimate causal effects, partly
because 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.
In 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

DiSIA - ARTES4.0 Webinar
2023-03-27 16:00 - 17:00 | Online
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.
Webinar DISIA#3: Explainable Artificial Intelligences;
Title: Counterfactual Explanations of (some) Machine Learning Models
Speaker: Prof. FABRIZIO SILVESTRI,
Language: Italian

For those interested in participating, please register at the link: https://lp.artes4.it/it/ webinar-27-marzo-2023

Webinar contents:
Start 16.00: Introduction
16.05 – Talk
16.45- Discussion, questions with participants
17.00 – Conclusion

The 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.

Abstract : 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.
Brief 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.

DiSIA Seminar
2023-03-23 12:00 - 13:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Sirio Legramanti (University of Bergamo)
Title: Concentration of discrepancy-based ABC via Rademacher complexity
Abstract:
There has been increasing interest on summary-free versions of approximate Bayesian computation (ABC), which replace distances among summaries with discrepancies between the whole empirical distributions of the observed data and the synthetic samples generated under the proposed parameter values. The success of these solutions has motivated theoretical studies on the concentration properties of the induced posteriors. However, current results are often specific to the selected discrepancy, and mostly rely on existence arguments which are typically difficult to verify and provide bounds not readily interpretable. We address these issues via a novel bridge between the concept of Rademacher complexity and recent concentration theory for discrepancy-based ABC. This perspective yields a unified and interpretable theoretical framework that relates the concentration of ABC posteriors to the behavior of the Rademacher complexity associated to the chosen discrepancy in the broad class of integral probability semimetrics. This class extends summary-based ABC, and includes the widely-implemented Wasserstein distance and maximum mean discrepancy (MMD), which admit interpretable bounds for the corresponding Rademacher complexity along with constructive sufficient conditions for the existence of such bounds. Therefore, this unique bridge crucially contributes towards an improved understanding of ABC, as further clarified through a focus of this theory on the MMD setting and via an illustrative simulation.

(Joint work with Daniele Durante and Pierre Alquier), Link: https://arxiv.org/abs/2206.06991

DiSIA - StEering - itENBIS Seminar
2023-03-20 15:30 - 17:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
The Department of Statistics, Computer Science, Applications DiSIA is happy to invite you to a seminar organized with the StEering – Statistics for Engineering: design, quality and reliability and itENBIS the Italian group of the European Network for Business and Industrial Statistics
Speaker: G. Geoffrey Vining (Virginia Tech, USA)
Title: Shewhart and Profile Monitoring for Industry 4.0
Abstract:
An important current area within statistical process control/monitoring is profile monitoring, which assumes that the underlying profile of the data over time is some linear, nonlinear, or nonparametric model. Let y be the characteristic of interest, and let f(y; θ, x) be the underlying model, where x is the p x 1 vector of variables that explain the behavior of y over time and θ is an unknown vector of model parameters relating to y. The standard approach taken by the profile monitoring community uses the following algorithm:
(1) Estimate θ for each individual value of y. Let $hat{theta}_{i}$ be the resulting vector of estimates associated with each individual $y_{i}$. Let $hat{theta}_{avg}$ be the average value of the $hat{theta}_{i}$s.
(2) Estimate the variance of $hat{theta}_{i}$ by computing estimates of every variance and covariance involving the components of x.
(3) Construct control limits using some variation of Hotelling’s $T^{2}$ statistic.

This approach historically assumes that p is very small. There are many serious issues from a linear-models perspective to this approach, not the least of which is an unnecessary need to estimate $p+binom{p}{2}$ variance components, which typically require much larger sample sizes to estimate than averages.
Industry 4.0 increases the number of sensors that can provide huge amounts of information in real time. As a result, there are opportunities to align data on those variables known to impact a critical quality characteristic to improve the monitoring of that characteristic. The case study that underlies this talk has very good information on 40 variables known to impact the performance of the critical quality characteristic. The current profile monitoring approach requires the estimation of 780 variances/covariances, which is completely unrealistic.
This talk outlines how to incorporate the extra information efficiently and effectively. Ironically, this approach has its origins in Shewhart’s original ideas underlying control charts. Seeing the connection is important for advances in statistical process monitoring within Industry 4.0.
The seminar will be held in DiSIA ‘s meeting room 205, and also online.
In order to get the link for the webinar you should register by 20 March at noon – sending an email to centro.steering@disia.unifi.it with the subject: “Webinar-Vining”.

Seminar of the “D2 Seminar Series” – Florence Center for Data Science
2023-03-17 14:30 - 16:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Welcome to another seminar of the D2 Seminar Series of the Florence Center for Data Science!

We’re happy to host Alberto Cassese and Chiara Bocci from the Department of Statistics, Computer Science, Applications “G. Parenti” of the University of Florence.
The Seminar will be held both on-site and online Friday 17th of March 2023, from 2.30-4 pm.

The seminar will be held in Aula 205 (ex 32) (DISIA – Viale Morgagni 59).
The Seminar will be available also online. Please register here to participate online:
https://us02web.zoom.us/webinar/register/WN_DIlCeuHERia0L7SLrmuWGQ

Speaker: Alberto Cassese
Title: "Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data"
Abstract: A Bayesian hierarchical mixture regression model is developed for studying the association between a multivariate response, measured as counts on a set of features, and a set of covariates. We have available RNASeq and DNA methylation data on breast cancer patients at different stages of the disease. We account for heterogeneity and over-dispersion of count data by considering a mixture of negative binomial distributions and incorporate the covariates into the model via a linear modeling construction on the mean components. Our modeling construction employs selection techniques allowing the identification of a small subset of features that best discriminate the samples, simultaneously selecting a set of covariates associated to each feature. Additionally, it incorporates known dependencies into the feature selection process via Markov random field priors. On simulated data, we show how incorporating existing information via the prior model can improve the accuracy of feature selection. In the case study, we incorporate knowledge on relationships among genes via a gene network, extracted from the KEGG database. Our data analysis identifies genes that are discriminatory of cancer stages and simultaneously selects significant associations between those genes and DNA methylation sites. A biological interpretation of our findings reveals several biomarkers that can help to understand the effect of DNA methylation on gene expression transcription across cancer stages.

Speaker: Chiara Bocci
Title: "Sampling design for large-scale geospatial phenomena using remote sensing data"

Abstract: In many fields of application, it is common to be interested in spatially-related phenomena and, in particular, to deal with attributes that, being defined on continuous spatial domains, are observed on a fine grid. For such kind of data, selecting the units spatially well spread over the study area allows to collect more information and consequently provides a better estimation of the population parameters. Moreover, technological advances have led to a growing availability of ready-to-use low-cost spatial data, like remote sensing data, which can be used as auxiliary information in the sampling design development process in addition to the units' spatial location.
Several sampling methods in literature simultaneously implement the selection of well-spread samples and the use of auxiliary variables in the selection process. These designs implicitly assume that, besides having a spatial pattern, the study variable shows a relationship with some auxiliary variables. However, this relationship is never known exactly, and for large-scale phenomena, it is often unrealistic to assume a unique relationship that holds everywhere. Therefore, we propose a two-step sampling design to identify when and how it is useful to exploit the auxiliary information, in addition to the units' spatial location, in the second step of the sampling selection process. We evaluate the performance of our proposal through Monte Carlo experiments in two simulation studies: one on pseudo-real datasets and one on synthetic datasets.
This talk is based on joint work with Saverio Francini and Emilia Rocco.

DiSIA Seminar
2023-03-16 12:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Chen-Hao Hsu (University of Bamberg)
Title: How women’s employment instability affects birth transitions? The moderating role of family policies in 27 European Countries
Abstract:
Why women in some countries are more likely than others to postpone childbirth when facing employment instability? This study uses 2010-2019 EU-SILC panel data to explore whether women’s unemployment and temporary employment affect their first- and second-birth transitions and how such patterns differ across 27 European countries. Results show that while unemployment and temporary employment generally delay women’s first- and second-birth transition, such effects vary across European countries and depend on the levels of family policy provisions. More generous family cash benefits may mitigate the negative effects of women’s unemployment on the first birth and temporary employment on the second birth transitions. On the other hand, the effect of women’s employment instability depends less on the length of paid maternity/parental leaves. Most strikingly, higher childcare coverage rates are associated with more negative effects of women’s temporary employment on the first birth and unemployment on the second birth transitions.

DiSIA Seminar
2023-03-06 11:00 - 12:00 | DiSIA, viale Morgagni 59, Viale Morgagni 59, Firenze, Italy
Speaker: Nicola Prezza (Università Ca’ Foscari, Venezia)
Title: A Theory of (co-lex) Ordered Regular Languages

Abstract: NFAs are inherently unordered objects, but they represent regular languages on which one can very naturally define a total order: for example, the co-lexicographic order in which words are compared alphabetically from right to left. In this talk I will show that interesting things happen when one tries to map this total order to the states of an accepting NFA for the language: the resulting order of the states is a partial pre-order whose width p turns out to be an important parameter for NFAs and regular languages. For example, take the classic powerset determinization algorithm for converting an NFA of size n into an equivalent DFA: while a straightforward analysis shows that the size of the resulting DFA is at most 2^n, we prove that it is actually at most (n-p+1)*2^p. This implies that PSPACE-complete problems such as NFA equivalence or universality are actually easy on NFAs of small width p (the case p=1 – total order – is particularly interesting). Another implication of this theory is that we can compress NFAs to just O(log p) bits per transition while supporting fast membership queries in the substring closure of the language.

Biosketch: Nicola Prezza is an Associate professor at Ca’ Foscari University of Venice, Italy. He received a PhD in Computer Science from the University of Udine in 2017 with a thesis on dynamic compressed data structures. After that, he worked as post-doc researcher at the universities of Pisa and Copenhagen (DTU) and as Assistant professor at LUISS (Rome). His current research is focused on the relations existing between data structures, data compression, and regular languages. In 2018, he received the “Best Italian Young Researcher in Theoretical Computer Science” award from the Italian chapter of EATCS. In 2021, he won an ERC starting grant on the topic of regular language compression and indexing.
