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: 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.
