Seminari - Dipartimento Informatica validi dal 15.12.2025 al 15.12.2026. https://www.di.univr.it/?ent=seminario&rss=0 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: online; 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. The course will be held online at following link: https://univr.zoom.us/j/82606978180 Meeting ID: 826 0697 8180 SCHEDULE: Wed 17/12, 16:30-18:00, Fri 19/12 11:30-13:00, Mon 22/12 16:30-18:00 . 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 https://www.di.univr.it/?ent=seminario&rss=0&id=6856 Relatore: Erik Garrison; Provenienza: University of Tennessee; Data inizio: 2025-12-17; Ora inizio: 8.30; Note orario: Aula T06; Referente interno: Rosalba Giugno; Riassunto: Mini-course (6 hours) on quot;Algorithms to Build and Understand the Human Pangenomequot; within the course quot;Programming for Bioinformaticsquot; in the Master Programme in Medical Bioinformatics 17 Dicembre 2025 - Dalle 8:30 alle 11:30 - Aula T06 18 Dicembre 2025 - Dalle 10:30 alle 13:30 - Aula T06. Wed, 17 Dec 2025 08:30:00 +0100 https://www.di.univr.it/?ent=seminario&rss=0&id=6856