Seminari - Dipartimento Informatica validi dal 06.04.2026 al 06.04.2027. https://www.di.univr.it/?ent=seminario&rss=0 https://www.di.univr.it/?ent=seminario&rss=0&id=6915 Relatore: Leonard P. Bos; Provenienza: University of Calgary; Data inizio: 2026-04-20; Ora inizio: 12.30; Note orario: Aula H; Referente interno: Giacomo Albi; Riassunto: Neural Nets generate outputs according to a specific recipe, i.e., they form a certain family of (vector valued) functions, determined by a typically large number of parameters (the weights). Training a Neural Net means to adjust the parameters to produce a desired output, i.e., find a good approximation to a given output function from the family of functions produced by the Net. In this course we will explore, in relation to classical approximation by polynomials and splines, how good an approximation can be so obtained. The course will be completely self contained. Schedule: (TBA). Mon, 20 Apr 2026 12:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=6915 https://www.di.univr.it/?ent=seminario&rss=0&id=6975 Relatore: Luciano Cascione; Provenienza: Institute of Oncology Research (IOR) - Bellinzona (SUI); Data inizio: 2026-04-20; Ora inizio: 15.30; Note orario: Sala Verde (solo presenza); Referente interno: Rosalba Giugno; Riassunto: Abstract : Recent advances in high-throughput sequencing have generated unprecedented volumes of transcriptomic data, offering new opportunities to study cancer as a complex, data-driven system. However, translating these data into predictive and interpretable models remains a major computational challenge. In this talk, I will present our ongoing efforts to develop computational frameworks for the analysis of bulk and single-cell RNA sequencing data, with a focus on B-cell malignancies. I will discuss key challenges, including high dimensionality, data heterogeneity, and integration across datasets, and present preliminary results that highlight emerging gene programs and potential clinical associations. Finally, I will outline future directions toward building predictive models of tumor behavior and enabling more precise, data-driven approaches to cancer research. Short CV: Luciano Cascione is a bioinformatician, head of the Bioinformatics Core Unit at the Institute of Oncology Research (IOR) in Bellinzona (Switzerland), and Group Leader at the Swiss Institute of Bioinformatics (SIB). His research focuses on computational analysis of transcriptomic data to investigate cancer biology, with a particular interest in B-cell malignancies. He works on integrating bulk and single-cell RNA sequencing data to identify gene regulatory programs and clinically relevant biomarkers. He has developed computational tools for RNA analysis, including methods for circular RNA characterization and transcriptomic profiling. His current work aims at building predictive and interpretable models to better understand tumor heterogeneity and support precision medicine. . Mon, 20 Apr 2026 15:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=6975 https://www.di.univr.it/?ent=seminario&rss=0&id=6963 Relatore: Pierluigi Graziani; Provenienza: (University of Urbino Carlo Bo, Department of Pure and Applied Sciences); Data inizio: 2026-04-21; Ora inizio: 10.00; Note orario: Sala Verde (solo presenza); Referente interno: Peter Michael Schuster; Riassunto: Abstract: Automated Theorem Proving (ATP) and Automated Theorem Finding (ATF) are well-established areas of mathematical research, rich in methods, results, and open problems. Among the questions that remain especially significant today are how to measure the simplicity of a proof, how to assess its readability, and how to evaluate the interestingness of a theorem. Each of these questions matters in its own right. In this talk, however, I will consider them specifically in relation to automated theorem proving and theorem finding in geometry. I will suggest that these three questions can be viewed as closely connected, and that, when approached from this perspective, they offer a fruitful way of understanding both the process and the products of automated reasoning in geometry. ---. Tue, 21 Apr 2026 10:00:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=6963 https://www.di.univr.it/?ent=seminario&rss=0&id=6967 Relatore: Leonardo Colombo; Provenienza: Centre for Automation and Robotics CSIC-UPM; Data inizio: 2026-05-06; Ora inizio: 12.30; Note orario: See moodle for details; Referente interno: Nicola Sansonetto; Riassunto: This course provides an introduction to Gaussian Processes (GPs) and their role in modern learning-based control. The main goal is to present Gaussian Process regression as a flexible probabilistic framework for modeling unknown dynamics, quantifying uncertainty, and incorporating data-driven corrections into control design. After introducing the foundations of Gaussian Process regression, kernels, hyperparameter learning, and prediction with uncertainty estimates, the course will focus on applications motivated by geometric mechanics and control. In particular, we will discuss how Gaussian Processes can be used to learn and approximate dynamics in mechanical systems of Lagrangian type, where structure, physical interpretability, and control relevance play a central role. We will then illustrate these ideas in applications to quadrotor systems, emphasizing residual dynamics learning, model improvement, and uncertainty-aware control. Finally, the course will address the learning of nonholonomic systems, highlighting both the opportunities and the challenges that arise when data-driven methods are combined with kinematic and dynamic constraints. The course is intended to build a bridge between machine learning and control theory, showing how Gaussian Processes can serve as a rigorous and practical tool for modeling, prediction, and control of complex dynamical systems. Schedule Thursday 07/05/2026 10:30-12:30 Room H Friday 08/05/2026 11:00-13:00 Room M Monday 11/05/2026 12:30-14:30 Room H Thursday 14/05/2026 10:30-12:30 Room H. Wed, 6 May 2026 12:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=6967