Seminari - Dipartimento Computer Science validi dal 03.11.2025 al 03.11.2026. https://www.di.univr.it/?ent=seminario&lang=en&rss=0 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6741 Relatore: Christine Gaßner; Provenienza: University of Greifswald - GER; Data inizio: 2025-11-05; Ora inizio: 16.30; Note orario: Aula E - Ca' Vignal 1; Referente interno: Peter Michael Schuster; Riassunto: Abstract: The BSS-RAM model can be used for characterizing uniform algorithms within a mathematical framework. BSS RAMs over first-order structures are the result of a generalization of various types of abstract machines such as BSS machines and Turing machines. On the one hand, universal machines are not necessary for executing arbitrary programs since any machine has its own program. On the other hand, universal BSS RAMs and, in particular, universal non-deterministic BSS RAMs are helpful in defining complete problems for some classes in different hierarchies of decision problems. Here, we will discuss sufficient conditions for their existence. We will consider first-order structures that contain only a finite number of operations and relations, including or excluding the identity relation, and with or without constants. Christine Gaszlig;ner (University of Greifswald) Short CV: After graduating in mathematics in 1981, Christine Gaszlig;ner began to study mathematical logic and work in Guuml;nter Asser#39;s research group. In 1985, she received a doctorate degree for her thesis on the axiom of choice in second-order Henkin logic in Greifswald. Since 1993 she has been dealing with abstract computation over various structures. Among other things, she investigated the possibility of constructing structures with P = NP. She completed her habilitation thesis in 2013 and received the venia legendi for mathematics from the University of Greifswald and the facultas docendi for theoretical computer science. From 2015 to 2023, she was the head of the research group Theory of Computability over Algebraic Structures and responsible for the teaching modules in Mathematical Logic and Theoretical Computer Science at the Institute of Mathematics and Computer Science in Greifswald. Wed, 5 Nov 2025 16:30:00 +0100 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6741 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6779 Relatore: Davide Murari; Provenienza: University of Cambdrige (UK); Data inizio: 2025-11-10; Ora inizio: 8.30; Referente interno: Giacomo Albi; Riassunto: This seminar course develops the viewpoint that many modern neural networks can be viewed as dynamical systems. We begin with a compact review of the mathematical foundations of deep learning, covering the key approximation and stability results. We then show how neural networks can be interpreted as discrete or continuous dynamical systems, and why this is valuable. From this lens, we study Neural ODEs and continuous normalising flows (CNFs) for generative modelling, and introduce symplectic neural networks to discover and simulate Hamiltonian systems. Brief PyTorch demonstrations accompany the theory. Background: Students should be familiar with the basic notions in linear algebra and probability. Some exposure to numerical methods for ODEs (e.g., Runge-Kutta methods) is helpful but not required. No prior experience with neural networks or PyTorch is assumed; basic familiarity with Python is helpful. Schedule: - 10/11 Aula M 8:30-10:30 - 11/11 Aula Alfa 8:30-10:30 - 13/11 Aula T.05 10:30-12:30 Contacts: Nicola Sansonetto (nicola.sansonetto@univr.it) - Giacomo Albi (giacomo.albi@univr.it) . Mon, 10 Nov 2025 08:30:00 +0100 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6779 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6732 Relatore: David N. Raynolds; Provenienza: University of Warsaw; Data inizio: 2025-11-17; Ora inizio: 8.30; Referente interno: Giacomo Albi; Riassunto: Monday 17/11 8:30 Aula M. Contact: Giacomo Albi. Mon, 17 Nov 2025 08:30:00 +0100 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6732 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6735 Relatore: Giulia Cavicchioni; Provenienza: German Aerospace Center; Data inizio: 2025-11-18; Ora inizio: 10.30; Referente interno: Francesca Mantese; Riassunto: Beyond its role in information theory, coding theory plays a crucial role in cryptography, particularly in post-quantum cryptography. Traditional public-key cryptosystems rely on problems such as integer factorization and discrete logarithms on elliptic curves. While these problems are currently intractable for classical computers, Shorrsquo;s algorithm allows quantum computers to solve them in polynomial time. Post-quantum cryptography aims to create secure algorithms based on computationally hard problems that remain resistant to quantum attacks, with a focus on NP-complete problems. Code-based cryptography refers to any cryptographic system whose security relies on hard problems from algebraic coding theory. Classically, this problem consists of decoding a random linear code, which was proven to be NP-complete in 1978. That same year, McEliece introduced the first code-based cryptosystem. The core idea is to select a code with an underlying algebraic structure that enables efficient decoding, and then disguise it as a seemingly random. Encryp- tion works by encoding the message into a codeword and then intentionally adding errors. With the knowledge of the secret code, one can efficiently decode and recover the message, while an attacker is left with the challenge of decoding a random linear code. In this mini-course, we will introduce code-based cryptography, delving into the mathematical foundations of these systems. In particular, we will focus on the main approach to solving the decoding problem, that is, the information set decoding (ISD) algorithms. Lastly, we will discuss how to determine whether a code has a specific algebraic structure or if it is a random code. Shedule: 18/11 10:30-13:30, 19/11 12:30--14:30, 25/11 10:30-13:30, 26/11 12:30-14:30, 27/11 8:30-10:30 . Tue, 18 Nov 2025 10:30:00 +0100 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6735 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6733 Relatore: Giada Fiandaca; Provenienza: INRIA, Marseille; Data inizio: 2025-12-01; Ora inizio: 10.30; 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 -13:30 01 December Aula M 8:30-12:30 04 DecemberT.05 10:30 -12:30 Contact: Giandomenico Orlandi/ Giacomo Albi. Mon, 1 Dec 2025 10:30:00 +0100 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6733 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6734 Relatore: Marcello Carioni; Provenienza: University of Twente; Data inizio: 2025-12-14; Ora inizio: 8.30; Referente interno: Giacomo Albi; Riassunto: TBA Contact: Giandomenico Orlandi. Sun, 14 Dec 2025 08:30:00 +0100 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6734 https://www.di.univr.it/?ent=seminario&lang=en&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&lang=en&rss=0&id=6726