Seminari - Dipartimento Informatica validi dal 14.05.2026 al 14.05.2027. https://www.di.univr.it/?ent=seminario&rss=0 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: 12.30; 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: 19/05. 10:30 - 12:30 Aula I 20/05. 12:30 - 14:30 Aula L 21/05. 10:30 - 12:30 Aula H Contacts: Giacomo Albi (giacomo.albi@univr.it) . Mon, 18 May 2026 12:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=7009 https://www.di.univr.it/?ent=seminario&rss=0&id=7034 Relatore: Ahmed Eldawy; Provenienza: University of California Riverside (USA); Data inizio: 2026-05-19; Ora inizio: 10.30; Note orario: Sala Verde (presenza ed on line); Referente interno: Sara Migliorini; Riassunto: Abstract : Maps are powerful, but making sense of them has traditionally required specialized expertise in GIS software, complex query formulation, and significant manual effort. Advances in large language models (LLMs) and generative AI are beginning to change this dynamic, opening new ways of working with spatial data that are far more intuitive. Instead of relying on specialized tools, users can now describe the data they need or write complex geographic questions, and intelligent systems can translate those intentions into concrete results. This talk will highlight recent progress in three key directions: generating realistic spatial datasets from textual descriptions, answering complex questions that combine spatial reasoning with external knowledge, and automatically creating styles that make map visualizations easier to comprehend. Taken together, these advances illustrate a new paradigm where geospatial data can be explored and understood through a natural and accessible interface. Bio : Ahmed Eldawy is an Associate Professor of Computer Science at the University of California Riverside. His research interests lie in the broad area of databases with a focus on big data management and spatial data processing. Ahmed led the research and development in many open source projects for big spatial data exploration and visualization including UCR-Star, an interactive repository for geospatial data with nearly four terabytes of publicly available data. He is a recipient of the highly prestigious NSF CAREER award, the 10-year Influential Paper Award in ICDE 2025, and the Best Application Paper award in SIGSPATIAL 2025. His work is supported by the National Science Foundation (NSF) and the US Department of Agriculture (USDA). Join Zoom Meeting https://univr.zoom.us/j/82394316049?pwd=m8YLEScwOqjdXN299Xilaj4555CZJw.1 Meeting ID: 823 9431 6049 Passcode: 871667 . Tue, 19 May 2026 10:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=7034 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: Sala Verde (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 https://www.di.univr.it/?ent=seminario&rss=0&id=7028 Relatore: Alexander Viguerie; Provenienza: Universita' di Urbino; Data inizio: 2026-05-25; Ora inizio: 12.30; Note orario: Aula H; Referente interno: Giacomo Albi; Riassunto: This topic concerns the development and application of mathematical, numerical, and data-driven methods for modelling the spread of infectious diseases, simulating epidemic scenarios, and supporting surveillance systems. It combines compartmental models, agent-based simulations, numerical methods for differential equations, statistical inference, machine learning, and real-time epidemiological data analysis to improve outbreak detection, forecasting, and public-health decision-making. SCHEDULE: 25/05 Aula H 12:30-14:30 26/05 Aula T.03 11:30 - 13:30 27/05 (2 hours) TBA . Mon, 25 May 2026 12:30:00 +0200 https://www.di.univr.it/?ent=seminario&rss=0&id=7028