Monica Riccardo is a Researcher at the Department of Engineering and Architecture, University of Parma, where she actively contributes to research and teaching in computer graphics, robotics, and intelligent systems. She teaches core courses such as Computer Graphics and Object Oriented Programming in undergraduate programs including Computer, Electronic and Communications Engineering. Her research interests span Computer Graphics , Robotics , Artificial Intelligence , Machine Learning , Probabilistic Modeling , and Human-Computer Interaction . Her work integrates deep learning, sensor fusion, and motion planning for applications in virtual reality, soft robotics, and autonomous systems. The recent publications highlight a strong trend in intelligent robotic systems, particularly in probabilistic occupancy prediction, hybrid tracking, in-hand manipulation, and integrated task-motion planning. Her research bridges theoretical AI methods with real-world robotic applications, especially in perception, planning, and human-robot collaboration. Scientific Awards No awards mentioned in the provided text. Advising and Grants No formal students or advisees are listed. No grants or funding sources are mentioned. Labs and Teams While specific lab affiliations are not stated, her collaborative publications with researchers such as Aleotti J., Lodi Rizzini D., and Caselli Stefano suggest active participation in a robotics and intelligent systems research group at the University of Parma.
Patrizia Semeraro is a Full Professor at the Department of Mathematical Sciences "GL Lagrange" (DISMA), Polytechnic University of Turin. She is a member of the Interdepartmental Center R3C - Responsible Risk Resilience Center and the Master's and Continuing Education School. Her research and teaching are centered in mathematical finance, probability, and statistics, with applications in financial modeling and risk analysis. Polytechnic University of Turin, Department of Mathematical Sciences (Current) Her primary research interests include mathematical finance, stochastic processes, multivariate modeling, and statistical methods in economics and finance. She focuses on advanced topics such as multivariate Lévy models, tempered stable processes, subordination, and high-dimensional Bernoulli distributions. Her work bridges theoretical probability with practical applications in credit risk, derivative pricing, and financial engineering. Recent publications demonstrate a strong trend in developing and calibrating multivariate financial models, analyzing dependence structures, and applying probabilistic methods to real-world economic problems. Her work spans journals in operations research, statistics, probability, and financial economics, reflecting interdisciplinary expertise. Scientific recognitions include: Outstanding paper awarded by Emerald Literati Network, Italy (2016) Fellow - INDAM, Italy (2019–present) Fellow - AMASES, Italy (2017–present) She supervises PhD students including Chen Zhang, Alessandro Mutti, and Giovanni Amici. She has been involved in nationally funded research projects such as PRIN and SUP-CHAIN-DIS (2023–2025), focusing on supply chain disruptions and financial losses. She teaches in the Financial Engineering program and has previously taught courses such as Complements of Mathematics and Fundamentals of Physics and Mathematical Principles. She is a member of the Probability and Applications research group and contributes to the Mathematical Models in Finance research area at DISMA.
Edmondo Trentin is an Associate Professor of Artificial Intelligence at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He chairs the IAPR Technical Committee 3 (Neural Networks & Computational Intelligence) and serves as an Associate Editor for Neural Processing Letters . His research focuses on neural networks, probabilistic models, and their applications in bioinformatics, speech processing, and pattern recognition. Trentin has taught extensively at both undergraduate and graduate levels, including courses on Artificial Intelligence, Neural Networks, and Pattern Recognition. He has advised numerous PhD students and contributed to international conferences and workshops. His work integrates machine learning with statistical methods, emphasizing hybrid models for complex data analysis. Teaching Highlights: Artificial Intelligence (MSc. courses since 2001) Neural Networks for Statistical Pattern Recognition Machine Learning for Graphs Supervised and Unsupervised Learning Research Interests: Connectionist density estimation Probabilistic graphical models Hybrid ANN/HMM systems Bioinformatics applications Professional Activities: Former Vice-Chair of IAPR-TC3 IEEE Computational Intelligence Society Secretary (2005-2006) Organizer of international workshops on neural networks and pattern recognition
Marco Fumero is a PostDoctoral Researcher at the Institute of Science and Technology Austria (ISTA), where he conducts foundational research at the intersection of geometry and artificial intelligence. Previously, he completed his Ph.D. in Computer Science at Sapienza University of Rome as a core member of the GLADIA research group under Professor Emanuele Rodolà's supervision, establishing a trajectory bridging theoretical geometry with practical deep learning applications. Ph.D. in Computer Science, Sapienza University of Rome Dr. Fumero's research program centers on exploiting geometric structures to revolutionize artificial intelligence systems, with primary focus on geometric deep learning, geometry processing, and representation learning. He pioneers methodologies for analyzing neural network latent spaces through spectral geometry and dynamical systems theory, developing frameworks that enable cross-model communication and zero-shot transfer. His work systematically addresses challenges in representation alignment, latent space dynamics, and disentangled feature extraction, with direct applications in 3D shape analysis, multimodal learning, and quantum-inspired computing. This research demonstrates exceptional theoretical rigor while maintaining strong connections to real-world problems in computer vision and scientific computing. His publication record reveals a dominant trend toward unifying geometric principles with deep learning architectures, particularly through spectral methods and functional map theory. The 2024-2025 publications showcase a coherent evolution from foundational latent space analysis (e.g., attractor dynamics in autoencoders) to practical frameworks for cross-model communication (e.g., cycle-consistent merging and semantic alignment). Key thematic threads include zero-shot capability development, invariance exploitation, and the translation of classical geometry processing techniques into neural network contexts. These contributions have established new paradigms for latent space manipulation across computer vision, graphics, and multimodal AI. Spotlight presentation at ICLR 2024 for "From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication" Multiple papers accepted at NeurIPS 2024 including "Latent Functional Maps" and "C2M3" During his doctoral training at Sapienza, Dr. Fumero actively mentored junior researchers within the GLADIA group, contributing to the development of next-generation geometric AI specialists through collaborative projects and technical guidance. His research has been supported by institutional funding from Sapienza University and ISTA, with potential backing from European research initiatives targeting foundational AI advances. Current work focuses on scaling geometric deep learning frameworks to complex multimodal scenarios while maintaining theoretical guarantees. Dr. Fumero maintains strong ties to the GLADIA research group at Sapienza University of Rome, which specializes in geometric learning and data analysis. At ISTA, he operates within a highly collaborative interdisciplinary environment that emphasizes theoretical computer science and its applications, contributing to the institute's mission of advancing frontier research through mathematical rigor and computational innovation.
Stefano Peluso is an Associate Professor in the Department of Statistics and Quantitative Methods at the University of Milano-Bicocca. His academic appointments include: Current: Associate Professor, University of Milano-Bicocca Former: Assistant Professor, Catholic University of the Sacred Heart (2015-2020) Peluso's research centers on Bayesian statistics and network analysis with applications in epidemiology and biostatistics. He develops advanced spatio-temporal models for public health crises, specializing in dependency structures in mixed-type data and longitudinal networks. His methodological innovations bridge machine learning and statistical inference to address pandemic forecasting and cardiovascular event prediction, supporting evidence-based healthcare decision-making. His 2024 publications reveal a cohesive research trajectory focused on Bayesian network structures for dependency learning and spatio-temporal prediction. Five articles demonstrate consistent application of these methods to real-world health challenges—from COVID-19 forecasting using neural networks to cardiovascular event modeling—highlighting an interdisciplinary approach that transforms complex health data into actionable public health insights during emergencies. Peluso leads significant research initiatives funded by the Lombardy Region and Italian Ministry of University and Research, including the 'InPreSa' project for SARS-CoV-2 detection and Bayesian network models for pandemic risk forecasting. These projects reflect his commitment to deploying statistical science against societal threats, though no student advising details appear in the available records.
Luigi PORTINALE is a Full professor at the Department of Science and Technological Innovation , University of Eastern Piedmont Amedeo Avogadro. His research focuses on Artificial Intelligence , Probabilistic Graphical Models , and Case-Based Reasoning , with applications in healthcare and data-driven systems. He leads projects like QAIS (Quality and Artificial Intelligence Services) and WISDOM , addressing AI-driven solutions for chronic immune-mediated diseases and healthcare data integration. Education: Not explicitly detailed in text. Research interests include Bayesian Networks, Petri Nets, and fault-tree analysis, with a recent emphasis on AI in medical diagnostics and multi-label classification for healthcare analytics. His work contributes to UN Sustainable Development Goals related to health and innovation.
Santa Di Cataldo is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino. His research focuses on computer vision, pattern recognition, digital image processing, and medical image processing, with applications in industrial systems and AI for manufacturing. Scientific Branch: IINF-05/A - Information Processing Systems ERC Sectors: PE6_8 - Computer graphics, computer vision, multi media, computer games ERC Sectors: PE6_11 - Machine learning, statistical data processing His work includes developing AI-driven anomaly detection frameworks, physics-informed neural networks for additive manufacturing optimization, and neuro-symbolic approaches for Industry 4.0 applications. He supervises PhD students in Artificial Intelligence and Computer Engineering programs, collaborating on projects like BIG (Blue Is Green) and PNRR-Complementary Plan. Premio Donna Innovazione (2010) He leads courses such as Machine Learning in Applications and Applied AI and Machine Learning , while contributing to bioinformatics and robotics-related teaching. His research is supported by IAM@PoliTo and EDA groups, utilizing LADISPE laboratory facilities.
