Seminari - Dipartimento Informatica validi dal 29.04.2026 al 29.04.2027. https://www.di.univr.it/?ent=seminario&rss=0 https://www.di.univr.it/?ent=seminario&rss=0&id=6998 Relatore: Gregorio Pellegrini; Provenienza: Generali Italia; Data inizio: 2026-04-29; Ora inizio: 17.30; Note orario: Sala Verde; Referente interno: Marco Caliari; Riassunto: Partendo dai primi incontri con la matematica fino allrsquo;esperienza nel settore assicurativo, la presentazione propone una riflessione sul valore formativo e pratico di questa disciplina. La matematica emerge come linguaggio per interpretare lrsquo;incertezza, valutare il rischio e costruire strategie sostenibili nel tempo. Un racconto che unisce teoria, pratica e competenze trasversali, pensato per chi sta immaginando il proprio futuro professionale. Wed, 29 Apr 2026 17:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=6998 https://www.di.univr.it/?ent=seminario&rss=0&id=6994 Relatore: Michele Ginesi; Provenienza: Istituto Europeo di Oncologia; Data inizio: 2026-05-04; Ora inizio: 17.30; Note orario: Sala Verde; Referente interno: Marco Caliari; Riassunto: La modellistica matematica egrave; la disciplina che si occupa dellrsquo;uso di vari strumenti matematici per descrivere fenomeni e fare predizioni. Un esempio si trova nellrsquo;uso di sistemi di Equazioni Differenziali Ordinarie (ODE) nella modellazione di fenomeni biologici. Nel laboratorio ldquo;Endocytosis, Signalling and Cancerrdquo; presso lrsquo; Istituto Europeo di Oncologia (IEO) stiamo sviluppando un modello matematico di questo tipo per descrivere lrsquo;Epidermal Growth Factor Receptor (EGFR), un recettore cellulare molto studiato in quanto numerosi tipi di cancro sono collegati a mutazioni nella sua espressione. Lo sviluppo di un modello come questo presenta diverse sfide: la scelta delle dinamiche biochimiche da descrivere, la traduzione di ipotesi biologiche in leggi matematiche, la scelta di risolutori numerici appropriati per risolvere lrsquo;ODE risultante, lrsquo;uso di specifici algoritmi di ottimizzazione per inferire i parametri dai dati, e lrsquo;analisi di identificabilitagrave; di questi parametri. In questo incontro verranno presentate le basi della modellistica matematica tramite ODE, per poi focalizzarsi sul modello EGFR e le sfide che pone. Mon, 4 May 2026 17:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=6994 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 https://www.di.univr.it/?ent=seminario&rss=0&id=7009 Relatore: Claudia Totzeck; Provenienza: University of Wuppertal; Data inizio: 2026-05-18; Ora inizio: 9.30; Referente interno: Giacomo Albi; Riassunto: Socio-economic systems are complex, adaptive, and driven by nonlinear interactions among heterogeneous agents. This minicourse provides a concise introduction to mathematical frameworks for their modeling, analysis, and control, combining tools from dynamical systems, networks, optimization, and data-driven methods. Emphasis is placed on the interplay between theoretical structure and empirical information, including stability and sensitivity analysis, parameter estimation, and the role of feedback in shaping collective dynamics. The course also addresses decision-making and intervention strategies through optimal control perspectives, highlighting how policies can influence system behavior in constrained and uncertain environments. Applications to domains such as markets, epidemics, and resource allocation illustrate how quantitative models can support understanding, prediction, and governance of socio-economic phenomena. SCHEDULE: 18/05. TBA 19/05. TBA 20/05. TBA Contacts: Giacomo Albi (giacomo.albi@univr.it) . Mon, 18 May 2026 09:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=7009 https://www.di.univr.it/?ent=seminario&rss=0&id=6999 Relatore: Loris Bazzani; Provenienza: Università degli Studi di Verona; Data inizio: 2026-05-19; Ora inizio: 16.30; Note orario: Aula D - CV1 (presenza ed on line); Referente interno: Alessandro Farinelli; Riassunto: Abstract : The prevailing AI paradigm is approaching a critical juncture where brute-force scaling of general-purpose models yields diminishing returns. As training costs escalate into the hundreds of millions, the industry remains challenged by frozen models that lack adaptability and struggle to deal with the long-tail complexities of real-world applications. This talk proposes a transition toward Adaptive and Collaborative Multimodal Intelligence: systems natively designed to be adaptable in a lightweight manner to environments where data is scarce and restricted and to interact with humans. We will explore three fundamental pillars necessary to bridge the gap between foundational research and industrial applications: Controllable Multimodal Data Generation & Privacy:active generation to deal with the long tail of rare events and privacy-restricted domains. Multimodal Adaptation & Specialization:leveraging adaptation techniques to customize models into domain-specific vertical experts. Human-AI Co-Design:integrating multimodal signals (language, gestures, and spatial clicks) as primary algorithmic constraints to facilitate collaboration between human and AI. The presentation will broadly review my research of the past few years across academia and industry and defines future directions. I will zoom in on one of my recent works to demonstrate the value of the aforementioned pillars: quot;Interactive Episodic Memory with User Feedbackquot; (CVPR 2026), which illustrates how integrating interactive memory allows models to better collaborate with humans. Bio : Loris Bazzani is an AI Research Leader with over 15 years of experience, spanning classical computer vision and machine learning to todayrsquo;s foundation and multimodal generative models. He is currently an adjunctprofessor at the University at Verona. In his previous role as Principal Scientist at Amazon (where he spent almost a decade), he led core research and product efforts across Prime Video, Alexa, and shopping, co-developing architectures for video understanding, vision-language representation, Large Multimodal Models, and diffusion models. His work powered features such as live sports highlights, virtual try-on, interactive product recommendations, and shopping assistants, reaching millions of users and delivering significant business impact. Loris obtained his Ph.D. in Computer Science from the University of Verona (Italy) in 2012, supervised by Prof. Vittorio Murino and Prof. Marco Cristani. He held postdoctoral positions at Dartmouth College with Prof. Lorenzo Torresani, and at the Italian Institute of Technology with Prof. Vittorio Murino. His research has been published in top-tier venues including CVPR, ICCV, ECCV, and ICML, with 50+ publications and patents: https://lorisbaz.github.io/ Link: https://univr.zoom.us/j/81959130316 Meeting ID: 819 5913 0316 . Tue, 19 May 2026 16:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=6999