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Edmondo TrentinView profile

Associate Professor

• Artificial Intelligence
• Neural Networks
• Pattern Recognition
• +5 more

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

• Bioengineering
• Artificial Intelligence
• Explainable AI
• +7 more

Gloria Menegaz is a Full Professor of Bioengineering at the Department of Engineering for Innovation Medicine, University of Verona, where she directs the BraiNAVLab. She has been serving in this position since 2021, following her appointment as Full Professor in Computer Science in 2017. Previously, she held Associate Professor and Assistant Professor positions at the University of Verona, University of Siena, and University of Fribourg (Switzerland). Her research spans multiple interdisciplinary fields including bioengineering, artificial intelligence, machine learning, and neuroimaging. She specializes in explainable AI methods applied to biomedical data, with particular focus on brain connectivity analysis using multi-modal neuroimaging data (fMRI, EEG) integrated with genetic information. Her work has resulted in over 160 publications in prestigious journals including IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging, and Frontiers in Neuroscience. Her recent research trends emphasize the development of explainable AI frameworks for clinical applications, particularly in neurological disorder diagnosis and treatment planning. She has pioneered approaches for integrating structural and functional brain connectivity data with genetic markers to identify numerical biomarkers for various neurological conditions. Senior Member of IEEE (2017) Chair of Steering Committee of IEEE Transactions on Medical Imaging Member of IEEE-SPS Challenges and Data Collection Technical Committee Vice-Chair of IEEE Bioimaging and Signal Processing Committee Recipient of Rita Levi Montalcini grant Throughout her career, Professor Menegaz has secured over 1 million euros in research funding from international institutions including the European Commission (H2020, FP7), European Science Foundation, MIUR (PRIN, SIR, FIRB), and ANR (France). She maintains extensive international collaborations with Georgia Tech University, INRIA (France), EPFL (Switzerland), Queen Mary University, and University College London. Her laboratory, BraiNAVLab, focuses on developing advanced computational methods for brain imaging analysis and navigation.

• Bioinformatics
• Algorithmic Biology
• Natural Computing
• +4 more

Simone Avesani serves as a Researcher in the Department of Computer Science at the University of Verona's School of Science and Engineering. His academic sector is INFO-01/A - Informatics, where he contributes to the university's research infrastructure through specialized computational approaches to biological data. His research spans Bioinformatics, Algorithmic Biology, and Natural Computing with focus on developing graph and string algorithms for systems biology, advanced data structures for sequence analysis, distance measures for biological sequences, and machine learning applications in biomedical contexts. Avesani is actively involved in the Algorithmic Bioinformatics and Natural Computing research group where he applies theoretical methods to model biological information processes, as well as the InfOmics laboratory which specializes in efficient biomedical data analysis. Currently leading the project 'Defining Clinical and Molecular Phenotypes of Multi-Drug Resistance in difficult to treat Rheumatoid Arthritis - MDR-RA' (started January 1, 2025), his work develops novel methods for mining biological networks, integrating heterogeneous data, analyzing omics, reconstructing pangenomes, and patient classification using interdisciplinary approaches from machine learning, data science, mathematics, and graph theory. He is affiliated with the INFOMICS laboratory among several specialized research facilities including IRIS, ISLa, and STARS laboratories within the department.

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +3 more

Pietro Lovato serves as a Temporary Professor in the Department of Computer Science within the School of Science and Engineering at the University of Verona. His academic work focuses on Information Processing Systems (ING-INF/05), with active teaching responsibilities across multiple degree programs including Human Centered Medical System Engineering, Computer Science and Engineering, and Bioinformatics. Dr. Lovato's research centers on advancing beyond traditional text representation models, particularly through his BeBoW (Beyond the Bag of Words) project that examines structural and statistical perspectives in information processing. His work spans artificial intelligence, machine learning, pattern recognition, and information retrieval systems with applications in bioinformatics and medical text analysis. His research demonstrates a consistent focus on developing more sophisticated models that capture contextual and structural information beyond simple word frequency approaches. His publication record from 2010-2019 shows a progression from traditional information retrieval techniques toward more complex neural network architectures and structural analysis methods. This evolution reflects broader trends in the field moving from statistical models to deep learning approaches while maintaining a focus on practical applications in specialized domains. Dr. Lovato contributes to several research laboratories at the university including the Networked Embedded Systems (NES) Laboratory and the ALTAIR Laboratory, where his work intersects with electronic systems design and parallel computing. His teaching portfolio includes both theoretical coursework and laboratory components, indicating a hands-on approach to student education in artificial intelligence and machine learning.

Giulia De PasqualeView profile

Assistant Professor

• Control Systems
• Social Network Analysis
• Optimization Algorithms
• +3 more

Giulia De Pasquale is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology, specializing in Control Systems. Her research focuses on social network interventions, optimization algorithms, and fairness in AI systems. She leads the Control Systems Group, addressing challenges in distributed control, hypergraph reconstruction, and ethical algorithm design. Her work integrates machine learning, network science, and control theory to develop fair and safe recommendation systems. Recent projects include hypergradient-based optimization for social network interventions and optimal transport approaches to fairness in influence maximization. She actively collaborates with institutions like Santa Fe Institute and UC San Diego. Giulia organizes interdisciplinary workshops on algorithmic fairness and has presented at NeurIPS, CDC, and NetSci. She advises PhD students like Alessandro Casu and hosts visiting scholars such as Camilla Quaresmini. Her research has been published in prestigious venues including Nature Communications and ICML.

• Semantic Web
• Cultural Heritage Modeling
• Knowledge Graphs
• +3 more

Alessandro Adamou is a Digital Humanities Scientist at the Bibliotheca Hertziana - Max Planck Institute for Art History. His expertise spans Semantic Web technologies, knowledge graph development, and intangible cultural heritage modeling. He leads the 'Linked Data Infrastructure for Cognitive Computing' project and has held research positions at Insight SFI Research Centre, The Open University, and Italy's National Research Council. Research Focus: Develops computational methods for representing fuzzy cultural concepts, optimizing ontology networks, and integrating linked data in humanities research. Applications include modeling musical traditions, reading experiences, and art historical evidence. Professional Service: Co-chairs the WHiSe workshop series on data science for humanities and serves on the Apache Stanbol project management committee.

Marco FumeroView profile

Research Fellow

• Geometric Deep Learning
• Geometry Processing
• Representation Learning
• +2 more

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.

• Bioinformatics
• Computational Biology
• Graph Algorithms for Biological Networks
• +7 more

Rosalba Giugno is a Full Professor of Informatics at the University of Verona's Department of Computer Science. She serves as Principal Investigator of the InfOmics Laboratory and is the reference person for the Master in Medical Bioinformatics program. Previously, from 2017 to 2022, she directed the Infolife laboratory, which comprised 35 research groups from various Italian universities. Her research focuses on algorithmic bioinformatics and computational biology, specifically developing graph algorithms for biological networks, integrating and analyzing biomolecular data, and modeling biological systems for personalized medicine. She leads a research group consisting of 5 PhD students and multiple master's thesis students. Her work bridges theoretical computer science with practical biomedical applications, utilizing approaches from machine learning, data science, mathematics, and graph theory. Professor Giugno has authored 130 scientific publications, with 70 appearing in international journals. Her recent work shows a strong trend toward applying advanced computational methods to solve complex problems in personalized medicine, particularly in patient stratification using multi-omics data, drug repurposing, and combination therapy prediction. She has secured funding for numerous national and European research projects, with recent initiatives focusing on multi-drug resistance in rheumatoid arthritis and personalized prostate cancer evaluation. She serves as an editor for the journal Information Systems and participates in scientific committees for international conferences and schools. Her leadership extends to university governance through roles on the Computer Science Teaching Committee and Department Council. Professor Giugno teaches courses in the Master's program in Medical Bioinformatics and the PhD program in Computer Science, including 'Analisi di dati Multi-omics da single-cell' and 'Programming for bioinformatics.' She has consistently taught these courses from 2016 through 2025, demonstrating commitment to training the next generation of bioinformatics specialists. The InfOmics Laboratory under her direction develops computational methods for biomedical data analysis, with applications spanning genomics, patient classification, and therapeutic optimization. The lab maintains strong connections with both academic and industrial partners through collaborative research projects.

• Temporal Data Mining
• Biomedical Decision Support Systems
• Temporal Logics
• +5 more

Pietro Sala is an Associate Professor at the University of Verona , Department of Computer Science. His research intersects Temporal Data Mining , Biomedical Decision Support Systems , and Formal Verification of temporal logic specifications. Key Research Areas: Interval Temporal Logic and Model Checking Temporal BPMN for healthcare processes Data fusion in clinical environments Algorithm design for repetitive task automation Notable Projects: Dioxin impact analysis on cardiovascular development Open-source clinical data warehousing (DW-SAN) Real-time system verification frameworks Teaching: Lecturer for Biomedical Decision Support Systems (Master's) Instructor for Data Mining and Software Engineering courses The articles highlight his work in temporal logic verification (2025), clinical process modeling (2024-2025), and data integration for NLP pipelines (2023). His publications span theoretical computer science conferences (LICS, ICALP) and healthcare informatics journals (ICHI, CBM).

Stefano PelusoView profile

Associate Professor

• Bayesian Statistics
• Network Analysis
• Spatio-temporal Modeling
• +3 more

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.

• Mathematical Physics
• Network Theory
• Kinetic Theory
• +4 more

Jonathan Franceschi serves as a Visiting Professor in the Department of Mathematics at the University of Pavia, maintaining an office in room C11. His academic profile is anchored in the Mathematical Physics research group, with active contributions to interdisciplinary mathematical modeling. His research spans Mathematical Physics, Network Theory, Kinetic Theory, and Fractional Calculus, with pronounced applications in social dynamics and public health. Key focus areas include epidemic modeling, opinion polarization mechanisms, and fake news diffusion—particularly examining Italian vaccination hesitancy during the Covid-19 pandemic. His methodological toolkit integrates kinetic equations, graph theory, and optimal control frameworks for complex networked systems. Analysis of his 15 publications (2019–2025) reveals a cohesive trajectory: early work established foundations in fractional calculus and numerical methods, while recent research pivots toward data-driven social modeling. Dominant themes include consensus optimization with condensation phenomena, graphon-based opinion dynamics, and uncertainty quantification in epidemic control—all demonstrating rigorous mathematical formalism applied to real-world sociotechnical challenges. Franceschi actively supports academic community initiatives including Five o'clock TeX (offering LaTeX courses via Zoom), The break bridge (curating educational handouts), and the CompMat poster template project. His digital scholarship footprint is visible through arXiv, ResearchGate, ORCID, and Google Scholar profiles.

• Optimal Transportation
• Geometric Measure Theory
• Calculus of Variations
• +3 more

Eugene Stepanov is a Full Professor at the St. Petersburg Branch of the Steklov Research Institute of Mathematics of the Russian Academy of Sciences. His research focuses on geometric measure theory, optimal transportation, and calculus of variations. He has contributed extensively to studies on metric measure spaces, isoperimetric problems, and differential equations. Key research interests include the structure of metric cycles, branched transportation networks, and the application of geometric integration techniques to nonsmooth systems. His work bridges pure mathematics with applied areas such as control theory and stochastic analysis. Stepanov has authored 56+ publications, with recent trends emphasizing multidimensional scaling, fractal geometry, and constructive controllability. His articles frequently explore the intersection of geometry and optimization, addressing problems like Steiner tree configurations and functional quantization. He has participated in conferences on optimal transport, geometric analysis, and measure theory, often contributing to seminar discussions on topics ranging from nonholonomic systems to invariant measures. His research also involves collaborations with international scholars, evident in co-authored works on eigenvalue problems, self-contracted curves, and operator theory. Despite no listed awards, his prolific publication record highlights sustained contributions to foundational mathematical theories.

Anisa RulaView profile

Assistant Professor

• Data Management
• Knowledge Graphs
• Data Quality Assessment
• +4 more

Anisa Rula is an Assistant Professor in Computer Science at the Department of Information Engineering, University of Brescia since 2021. Previously, she was a researcher at the University of Bonn's SDA group (2017–2019). She holds a PhD in Computer Science from the University of Milano-Bicocca (2014). Her research focuses on data management and exploration, particularly in data quality assessment for Information Systems and Knowledge Graphs. Key interests include semantic data enrichment, large datasets, and knowledge extraction. Education: PhD in Computer Science, University of Milano-Bicocca (2014) Prior academic and research roles at University of Bonn (2017–2019) Research Interests: Data Quality Assessment for Knowledge Graphs and Information Systems Large-Scale Dataset Management & Exploration Procedural Knowledge Extraction from Text Ontology-Driven Semantic Enrichment Human-in-the-Loop Data Annotation Systems Publications reflect her expertise in Knowledge Graph lifecycle management, quality assessment tools like KGHeartBeat, and LLM applications for schema mining and data discovery. Her work emphasizes practical implementations such as MantisTable for semantic table interpretation and K-hub ontology for industrial knowledge extraction. Awards: None explicitly mentioned in text. Advising & Grants: No student advisees listed. Active in developing open-source tools and collaborative frameworks for data quality and semantic web applications. Labs/Teams: Involved in Knowledge Graph validation and quality initiatives, including contributions to the EU Business Graph platform and cross-institutional workshops on Linked Data Quality.

CLAUDIA DIAMANTINIView profile

Professor

• Data Science
• Process Mining
• Machine Learning
• +5 more

Prof. Claudia Diamantini is a Full Professor at the Department of Information Engineering (D.I.I.) of the Polytechnic University of Marche, located in Ancona, Italy. She specializes in data science, process mining, machine learning, and semantic technologies with a focus on applications in sustainability, education, and industrial systems. Her research explores topics such as knowledge graphs, predictive modeling for environmental metrics, and educational data analysis using AI techniques. Prof. Diamantini's work integrates advanced computational methods with real-world challenges, including energy efficiency in building engineering, anomaly detection in business processes, and semantic-driven data lake analytics. She leads initiatives to enhance decision-making through data-driven approaches, such as optimizing KPI forecasting and improving public transport performance monitoring. Her scholarly contributions span over 50 articles in top-tier conferences and journals, emphasizing interdisciplinary applications of computer science. She collaborates on EU-funded projects and advises graduate students in engineering and computer science disciplines. Office hours are held weekly at the Faculty of Engineering.

• Biomedical Informatics
• Medical Informatics
• Healthcare Information Systems
• +4 more

Giovanni Dimauro is an Associate Professor in the Department of Computer Science at the University of Bari. His work focuses on biomedical informatics, ontology development, and machine learning applications in healthcare. He leads research in clinical text mining, healthcare knowledge graphs, and AI-driven medical decision support systems. Research interests include: biomedical natural language processing, ontology semantics for healthcare interoperability, and predictive modeling in clinical domains. His team develops tools for adverse event detection, social determinants extraction, and vaccine ontology alignment. Recent work emphasizes large language models for biomedical literature curation (RefAI), ensemble methods for knowledge extraction, and Med-BERT adaptations for disease prediction. Collaborations address clinical note analysis, drug repurposing via knowledge graphs, and interoperability frameworks like SDoHO ontology. His lab integrates deep learning, semantic web technologies, and clinical data standards to advance precision medicine and healthcare informatics.