Seminari - Dipartimento Informatica validi dal 19.11.2025 al 19.11.2026. https://www.di.univr.it/?ent=seminario&rss=0 https://www.di.univr.it/?ent=seminario&rss=0&id=6817 Relatore: Anna Bassi; Provenienza: DecisionBrain; Data inizio: 2025-11-24; Ora inizio: 16.30; Note orario: Sala Verde; Referente interno: Marco Caliari; Riassunto: Questo incontro esplora l#39;applicazione pratica della Ricerca Operativa (OR) e delle Advanced Analytics, le discipline che utilizzano modelli matematici per prendere decisioni ottimali in scenari complessi. DecisionBrain sviluppa queste soluzioni per aiutare le aziende a raggiungere i loro obiettivi operativi. Durante la presentazione, vedremo come i concetti di OR vengono applicati per ottimizzare la Workforce & Maintenance e la Manufacturing, Supply Chain & Logistics. Esploreremo i ruoli professionali in un team di progetto, come l#39;Optimization Developer e il Business Analyst, e vedremo come la metodologia Agile faciliti lo sviluppo di soluzioni efficienti attraverso cicli di rilascio brevi. Questo egrave; un invito a scoprire come il tuo background in Matematica puograve; trasformare problemi complessi in soluzioni vincenti. L#39;incontro egrave; pensato per le studentesse e gli studenti del corso di laurea in Matematica Applicata e aperto a tutti e a tutte. Mon, 24 Nov 2025 16:30:00 +0100 https://www.di.univr.it/?ent=seminario&rss=0&id=6817 https://www.di.univr.it/?ent=seminario&rss=0&id=6733 Relatore: Giada Fiandaca; Provenienza: INRIA, Marseille; Data inizio: 2025-11-27; Ora inizio: 10.30; Note orario: Aula T.05; Referente interno: Giacomo Albi; Riassunto: Cancer remains a leading cause of mortality worldwide. Despite decades of research, controlling or eradicating advanced forms of the disease continues to be a challenge. Mathematical modeling offers a powerful tool for cancer research, enabling the exploration of complex processes and rdquo;what-ifrdquo; scenarios that may be inaccessible through traditional experimental or clinical methods. This mini-course will in- troduce participants to key mathematical modeling techniques used in cancer studies, emphasizing the multi-scale nature of cancer progression. We will explore models that simulate treatment effects across various biological levelsmdash;from intracellular interactions to tissue and whole-body responses, showing how mathematical models can provide quantitative insights, guide experimental design, and help distin- guish competing hypotheses, ultimately supporting the development of innovative cancer therapies. 27 November T.05 10:30 -12:30 28 November Aula C 10:30 -12:30 (TBC) 28 November Aula I 13:30 -15:30 01 December Aula M 8:30-12:30 03 December Alfa 13:30 -15:30 (TBC) 04 December Aula T.05 10:30 - 15:30 Contact: Giandomenico Orlandi/ Giacomo Albi. Thu, 27 Nov 2025 10:30:00 +0100 https://www.di.univr.it/?ent=seminario&rss=0&id=6733 https://www.di.univr.it/?ent=seminario&rss=0&id=6815 Relatore: Matteo Spadetto; Provenienza: LS2N, University of Nantes; Data inizio: 2025-12-01; Ora inizio: 10.30; Note orario: (see after abstract for timetable and venue); Referente interno: Peter Michael Schuster; Riassunto: Category theory offers a powerful and unifying way to look at mathematics and theoretical computer science. It provides a common language to describe structures and relationships across different areas, from algebra and topology to logic and computation. This course gives an introduction to category theory for master students, combining basic concepts with their (logical) applications. The minicourse (1 ECTS) will consist of an introduction to category theory (6 hours, the first part) followed by complementary sessions on categorical logic (the second part). In the first part, we will cover the main ideas of this subject: Categories, functors, and natural transformations Universal properties and limits Adjunctions In the second part, we will explore how logic can be interpreted within categories (focusing on the fragment of regular logic and on regular categories): Internal logic and categorical semantics Regular categories and regular theories Completeness This minicourse aims to build intuition through examples and exercises. By the end, students will have a solid grasp of the categorical way of thinking (and how it connects to logical systems and their semantics). Schedule Monday, 01/12/2025 ndash; 10:30ndash;12:30 ndash; Aula T.02a Tuesday, 02/12/2025 ndash; 08:30ndash;10:30 ndash; Aula T.02b Wednesday, 03/12/2025 ndash; 08:30ndash;11:30 ndash; Aula T.04 Friday, 05/12/2025 ndash; 16:30ndash;18:30 ndash; Aula T.02b Venue Department of Computer Science University of Verona Strada le Grazie 15 37134 Verona Lecturer#39;s web site First contact . Mon, 1 Dec 2025 10:30:00 +0100 https://www.di.univr.it/?ent=seminario&rss=0&id=6815 https://www.di.univr.it/?ent=seminario&rss=0&id=6802 Relatore: Riccardo Sartea; Provenienza: Amazon; Data inizio: 2025-12-10; Referente interno: Alberto Castellini; Riassunto: Mini-course (6 hours) on quot;Applied Large Language Models: Hands-onquot; within the course Natural Language Processing in the Master Programme in Artificial Intelligence A.Y. 2025 - 2026 Schedule: - 10/12: 8.30-10.30 Aula T02.a - 11/12: 12.30-14.30 Aula I (di Imola) - 12/12: 8.30-10.30 Aula T02.b Contact: Alberto Castellini (alberto.castellini@univr.it). Wed, 10 Dec 2025 00:00:00 +0100 https://www.di.univr.it/?ent=seminario&rss=0&id=6802 https://www.di.univr.it/?ent=seminario&rss=0&id=6726 Relatore: Dante Kalise; Provenienza: Imperial College London; Data inizio: 2025-12-15; Ora inizio: 14.30; Note orario: da definire; Referente interno: Giacomo Albi; Riassunto: This course introduces the fundamental ideas and computational methods behind optimal control and data-driven modelling.In this short course, we will study how to incorporate elements of machine learning into optimal control design. The course will focus on fundamentals on optimal control: dynamic optimization, linear-quadratic control, dynamic programming and Pontryagin#39;s maximum principle. Nonlinear optimal control. Approximation methods in high dimensions are also discussed such as polynomial approximation, deep neural networks. Optimization techniques: LASSO regression, stochastic gradient descent, training neural networks. Finally, combining the first two parts, we will study the construction of data-driven schemes for the approximation of high-dimensional nonlinear control laws. . Mon, 15 Dec 2025 14:30:00 +0100 https://www.di.univr.it/?ent=seminario&rss=0&id=6726 https://www.di.univr.it/?ent=seminario&rss=0&id=6734 Relatore: Marcello Carioni; Provenienza: University of Twente; Data inizio: 2025-12-16; Ora inizio: 8.30; Note orario: Aula G; Referente interno: Giacomo Albi; Riassunto: Optimal Transport (OT) is a mathematical theory introduced by Gaspard Monge in 1781 to study the optimal allocation of resources and goods. Its original formulation, known as the Monge formulation, aims at finding the best way to transport a probability distribution in $\mathcal{P}(\mathbb{R}^n)$ to another by minimizing a transport cost computed with respect to a given cost function $c$. Mathematically, this consists in finding a map $T : \mathbb{R}^n \to \mathbb{R}^n$ that minimizes the cost of moving mass from the first measure to the second one. This formulation is highly flexible, as it encompasses discrete, semi-discrete, and continuous settings, and the cost $c$ can be chosen to enforce specific properties, such as restricting transport to certain regions or promoting mass concentration. Due to its flexibility and mathematical rigor, the theory experienced substantial development in the 20th century and gained relevance in fields such as economics, urban planning, image processing, and biology. More recently---especially in the last decade---optimal transport has become a powerful tool in machine learning. Many modern algorithms rely on estimating distances between data distributions efficiently and accurately, and OT provides a natural framework for comparing such distributions by quantifying the cost of transporting one into the other. This perspective, combined with fast numerical methods for computing OT (notably entropic regularization and the Sinkhorn algorithm), has made OT central in the design of new generative models, in solving inverse problems, and in improving the robustness of neural networks. In this series of lectures, we will cover: (1) an introduction to the classical formulations of optimal transport and the core theoretical results; (2) entropic regularization and the Sinkhorn algorithm for efficient computation of regularized OT; and (3) connections between OT and machine learning, with a focus on adversarial generative models based on optimal transport (such as WGAN and WAE). Time permitting, we will also discuss OT-based approaches to inverse problems. Schedule: Tue 16/12 Aula G, 8:30-10:30 Wed 17/12 Aula Alfa, 13:30-15:30 Thu 18/12 Aula T.05, 10:30-12:30 Contact: Giandomenico Orlandi/ Giacomo Albi. Tue, 16 Dec 2025 08:30:00 +0100 https://www.di.univr.it/?ent=seminario&rss=0&id=6734