جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• IoT Technologies
• Signal Processing
• +۲ مورد دیگر

Prof. Grazia Iadarola serves as a Researcher in the Department of Information Engineering at Università Politecnica delle Marche (UNIVPM) , Ancona, Italy. Her work spans biomedical engineering, IoT technologies, and advanced measurement systems. Research Focus : Biomedical signal processing, wearable sensors, and IoT-enabled health monitoring systems. Instrumentation : Development of measurement systems for UAVs, MV switchgears, and human physiology. Machine Learning : Applications in physical fatigue detection, fault diagnosis, and classification of biomedical signals. Email : g.iadarola@staff.univpm.it Recent Publication Trends : Over 20 publications since 2016 focus on IoT-based wearables , compressed sensing , drone monitoring , and health signal analysis . Key Collaborations : Contributions to conferences like EAI HealthyIoT and IEEE journals, with emphasis on sensor validation, signal reconstruction, and smart healthcare solutions.

Flavio Corradiniمشاهده پروفایل

استاد

• Blockchain
• Process Mining
• Business Process Modeling
• +۵ مورد دیگر

Flavio Corradini is a Full Professor at the Department of Computer Science, University of Camerino. His research focuses on blockchain, process mining, business process modeling, IoT systems, and digital twins. He has led projects like the SAFE platform for disaster response and ChorChain for blockchain-based process execution. His work bridges formal methods with practical applications in robotics, smart contracts, and environmental monitoring. Education details are not explicitly provided in the text, but his academic contributions span over a decade of publications in top journals and conferences. His research often combines theoretical rigor with real-world implementation, such as developing tools like BPMN Inspector and the μG graph neural network library. Research interests include: blockchain interoperability, process choreography, IoT system engineering, and formal verification of distributed systems. His work on digital twins integrates sensor data with simulation models for industrial and environmental applications. Recent articles highlight advancements in robotic process mining, smart contract analysis, and multimodal data clustering. He has contributed to frameworks like Tangramob for urban mobility simulation and FloWare for IoT application development.

• Statistics
• Decision Analysis
• Multivariate Analysis
• +۳ مورد دیگر

Antonio Lucadamo is an Associate Professor at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of the University of Sannio. His research focuses on statistical modeling, decision analysis, and multivariate methods with applications in diverse fields such as financial markets, cultural festivals, environmental science, and healthcare evaluation. Key Research Areas : Decision theory, ordinal data handling, AHP methodology, environmental quality assessment Recent Collaborations : Pietro Amenta, Ida Camminatiello, Gabriella Marcarelli, and Matteo Rossi Publication Trends : His work spans from foundational statistical techniques (e.g., co-inertia analysis, Taguchi index generalization) to applied studies in financial decision-making, festival loyalty models, and environmental quality assessment. Notable subfields include pairwise comparison matrices, OWA operators, and spectral soil analysis. Contact : antonio.lucadamo@unisannio.it

Roberto Marmoمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Social Media Mining
• +۴ مورد دیگر

Roberto Marmo is a Professor of Computer Science at the University of Pavia since the 2022/23 academic year. His research spans multiple domains including Artificial Intelligence , Computer Vision , and Social Media Mining . He earned his PhD in Information Engineering at Pavia's Computer Vision and Multimedia Lab after completing a Computer Science degree at the University of Salerno. Core Expertise: Data Science, AI Algorithms, Scientific Visualization Industry Applications: Anti-fraud Systems, Tourism Analytics, Transportation Sign Recognition His academic work demonstrates consistent interdisciplinary innovation: 2020: Encyclopedia contributions on Cybersecurity and Social Engineering 2017: Market analysis through social media mining 2010: Social network architecture research Key collaborations include: IBM's Web 2.0 Security Encyclopedia Italian Tourist Railways Association (application of IT to heritage tourism) Academic-Industry Partnerships in transportation sign recognition His technical contributions include: Neural network applications in geological analysis Operational risk visualization frameworks SOS gesture recognition systems e-learning performance dashboards Available for consulting and training services in AI, data analysis, and scientific visualization.

• Approximation Theory
• Numerical Analysis
• Machine Learning
• +۱۲ مورد دیگر

Emma Perracchione is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at Politecnico di Torino, where she conducts research at the intersection of approximation theory, machine learning, and scientific computing. Her work focuses on kernel-based methods, inverse problems, and applications in space weather and solar physics. She is actively involved in teaching and research leadership, including PhD supervision and national projects. PhD in Mathematics, University of Turin (2017, cum laude) M.Sc. and B.Sc. in Mathematics, University of Turin (2013, 2011) Her research interests center on approximation theory and its applications, particularly greedy methods and two-layered kernel machines used for feature reduction in geomagnetic storm forecasting and optimal sampling for solar nanosatellites. These efforts contribute significantly to advancements in inverse problems and scientific computing . Her expertise spans machine learning , imaging , and data-driven modeling , with applications in climate action and space weather. The most recent publications highlight a strong trend in developing and analyzing variably scaled kernels , feature selection via greedy algorithms, and machine learning applications in solar and astrophysical contexts. These works integrate numerical analysis with real-world data from solar wind and imaging instruments, demonstrating a blend of theoretical rigor and practical relevance. Scientific awards received include: GNCS Young Researchers Funding (2020) GNCS Young Researchers Funding (2016) "Luciana Picco Botta" Study Award (2015) COST Short Term Scientific Mission (STSM) grant (2015) Emma Perracchione supervises PhD student Matteo Trombini in the Mathematical Sciences program (40th cycle, 2025–ongoing) and leads the PRIN-funded project GOSSIP – Greedy Optimal Sampling for Solar Inverse Problems (2025–2027). She has taught various courses including Linear Algebra and Geometry , Numerical Methods and Scientific Computing , and advanced topics on Kernels for Machine Learning in aerospace, automotive, and computer science engineering programs. She is a member of the Space Weather Italian Community (SWICo) and the National Scientific Computing Group (GNCS-INdAM) , and serves as Guest Editor for Dolomites Research Notes on Approximation . She has also participated in organizing major conferences such as DWCAA24 and GIMC-SIMAI Young.

Filippo Bergamascoمشاهده پروفایل

دانشیار

• Computer Vision
• Oceanography
• Image Processing
• +۳ مورد دیگر

Filippo Bergamasco is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. He is a member of the Scientific Committee of the Center for Studies on Port Economics and Management (DAIS). His work bridges computer vision, environmental monitoring, and oceanography, with a focus on developing advanced imaging techniques for ocean wave analysis and stereo reconstruction. He leads projects like the WASS pipeline, an open-source tool for 3D stereo reconstruction of ocean waves, and collaborates on studies involving rogue wave dynamics, low-cost sensors, and Arctic sea-state analysis. His research leverages machine learning, deep learning, and real-time systems to address challenges in environmental and marine sciences. Research interests include computer vision applications in oceanography, stereo imaging systems, wave dynamics modeling, and environmental monitoring technologies. He has contributed to over 80 peer-reviewed publications, with recent works focusing on real-time wave tracking, 3D reconstruction, and the integration of physics-driven models with neural networks. His expertise spans interdisciplinary collaborations, including projects on fish oil's impact on wind waves and the development of robust camera calibration methods. He actively publishes in top journals like IEEE Transactions on Image Processing, Ocean Modelling, and Physical Review Letters, and engages in international conferences such as ECCV and ICPRAM.

• Complex Systems
• Network Theory
• Financial Networks
• +۴ مورد دیگر

Guido Caldarelli is a Full Professor of Theoretical Physics at Ca' Foscari University of Venice, leading roles in the Department of Molecular Sciences and Nanosystems and the European Center for Living Technology. His career includes positions at IMT Lucca, CNR, and international visiting roles. He specializes in complex systems, network theory, and financial networks, with interdisciplinary applications in urban planning and epidemiology. Caldarelli has published ~200 articles and three books, including influential works on scale-free networks. Recognitions include APS Fellowship (2020), Academia Europaea membership, and leadership roles in global scientific societies. His research integrates statistical physics with modern data-driven approaches, addressing challenges in finance, healthcare, and environmental science. Education : PhD in Condensed Matter Theory (SISSA, 1996), MPhil (1994), and Degree in Physics (Sapienza, 1992), all with top honors. Research Interests : Caldarelli’s work focuses on network theory’s applications to complex systems, including financial risk, urban topology, and biological networks. He develops methods to quantify systemic risk in financial systems and uses network science to analyze social media dynamics, election discourse, and ecological systems. His team employs machine learning and statistical physics to uncover hidden patterns in large datasets. Key Achievements : Over 16,000 citations, h-index 64 (Google Scholar). He founded the Italian Society of Statistical Physics and co-founded Catchy s.r.l. for AI solutions. His work on scale-free networks resolved debates about network structure validity, impacting fields from epidemiology to urban planning. Awards : APS Fellowship (2020), Academia Europaea (2020), Honorary ISI Fellow, and Network Science Society’s Outstanding Service Prize (2020).

Gianluca Lebaniمشاهده پروفایل

پژوهشگر

• Linguistics
• Computational Linguistics
• Dialectology
• +۶ مورد دیگر

Gianluca Lebani is a Researcher at Ca' Foscari University of Venice, affiliated with the Department of Comparative Linguistic and Cultural Studies and the Research Institute for Digital and Cultural Heritage. His roles include membership in the Management Committee of the University Scientific Instrumentation Service Center (CSA), the Board of Directors of the Bembolab Laboratory, and the Academic Senate. His research focuses on theoretical and comparative linguistics, dialectology, and computational linguistics. Key areas include variation in Italian and Italo-Romance dialects, semantic role analysis using transformer models, psycholinguistic investigations of grammatical gender, and historical document analysis. He also explores machine learning applications in language processing, such as baby language models (BAMBI) and Old Saxon poetry scansion algorithms. Recent work includes studies on possessive pronouns in dialects, indefiniteness in bilingual speakers, and the expression of semantic gender. His contributions span corpus-based methods, distributional semantics, and experimental psycholinguistics. Collaborations involve projects like VariOpInTA (Variation and Optionality in Italo-Romance) and computational frameworks like SYMPAThy for extracting word combinations. Lebani is active in academic service, contributing to institutional committees and editorial roles. His lab affiliations include Bembolab and the Digital and Cultural Heritage Institute. He regularly publishes in journals like RID, RIVISTA ITALIANA DI DIALETTOLOGIA and ISOGLOSS , and presents at conferences such as CLiC-it and QITL.

Claudio SILVESTRIمشاهده پروفایل

دانشیار

• Data Mining
• Privacy in Location-Based Services
• Spatio-Temporal Data Analysis
• +۳ مورد دیگر

Claudio SILVESTRI is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. He specializes in Computer Science (INFO-01/A), with a focus on data mining, privacy in location-based services, and spatio-temporal data analysis. His research integrates computer science with environmental and biomedical applications. Teaching Responsibilities include courses on Advanced Data Management (Computer Science) and Geographic Information Systems (Environmental Sciences) at the Master's level across multiple academic years. Research Interests span: Algorithms for privacy protection in location-based services Spatio-Temporal Data Warehouses and trajectory analysis Parallel computing on GPU and cloud platforms Applications in fisheries monitoring and diabetic kidney disease modeling Funding Projects include EU initiatives like H2020 (e.g., DC-ren for kidney disease research) and regional grants (e.g., ADMIN4D on Industry 4.0). Key collaborations involve researchers like Salvatore ORLANDO and Debora SLANZI. He is affiliated with the European Center for Living Technology (ECLT) and the Research Institute for Social Innovation. Office hours are held Wednesdays 2-4 PM by email appointment.

• Biometrics
• Fingerprint Recognition
• Security Systems
• +۳ مورد دیگر

Raffaele Cappelli is an Associate Professor at the Department of Computer Science and Engineering, University of Bologna. His research focuses on fingerprint biometrics, emphasizing anti-spoofing, segmentation, and synthetic data generation. He contributes to the Fingerprint Verification Competition (FVC) and has pioneered tools like SFinGe for synthetic fingerprint databases. His work addresses security vulnerabilities in biometric systems and forensic applications. Education and affiliations include roles at the University of Bologna, with expertise in computer science and engineering. His research integrates machine learning, pattern recognition, and hardware acceleration to enhance biometric system performance and reliability. Key areas of study include presentation attack detection, fingerprint orientation estimation, and large-scale database management. He has authored over 70 publications, with notable contributions to forensic science and secure authentication protocols.

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +۷ مورد دیگر

Stefano Teso is an Assistant Professor at the Interdepartmental Center for Mind/Brain Sciences (CIMeC) and the Department of Information Engineering and Computer Science, University of Trento. His research focuses on enhancing the reliability of machine learning models in high-stakes human-centric contexts through neurosymbolic integration, interactive learning, and constraint-based approaches. Master's in Computer Science, University of Trento Ph.D. in Information Engineering and Computer Science, University of Trento Teso's work combines conceptual modeling , novel neural architectures , and human-AI interaction via explanations and communication. He contributes to academic programs in Artificial Intelligence Systems (LM) and Data Science (LM) , teaching courses like Advanced Topics in Machine Learning and Machine Learning for NLP . He is affiliated with the Interdepartmental Center for Mind/Brain Sciences (CIMeC) , where he explores neurocognitive architectures and statistical relational learning frameworks.

• Internet of Things
• Body Sensor Networks
• Edge Computing
• +۴ مورد دیگر

G. Fortino is a Professor at the University of Calabria, Italy, with 643 publications, an h-index of 74, and over 21,592 citations. His research spans critical domains including: Internet of Things Body Sensor Networks Edge and Fog Computing Healthcare Applications Machine Learning and Deep Learning Security and Trust Frameworks His work establishes foundational architectures for healthcare industry 4.0 and IoT security, particularly through multi-sensor fusion techniques and edge-based processing. Recent publications demonstrate a pronounced shift toward medical applications, with deep learning models for blood pressure monitoring, brain tumor classification, and emotion recognition integrated with real-time sensor networks. Analysis of his 2017-2021 publications reveals dominant trends in human activity recognition (50% of recent work), security for distributed systems (30%), and medical diagnostics (20%), consistently leveraging machine learning for real-time processing in resource-constrained environments. His most influential papers address IoT security taxonomies and body sensor network fusion, with citation counts exceeding 500. No information regarding scientific awards, student advising, grants, or dedicated research laboratories was found in the provided materials.

Stefano Federico Tonellatoمشاهده پروفایل

دانشیار

• Statistical space-time models
• Statistical model choice
• Semiparametric models
• +۷ مورد دیگر

Stefano Federico Tonellato is an Associate Professor at the Department of Economics, Ca' Foscari University of Venice. His academic responsibilities include serving as Departmental Delegate for Information Technology Infrastructure. 1992: Degree in Economics, Ca' Foscari University of Venice 1993-1994: Visiting student, Department of Statistics, Warwick University (UK) 1996: PhD in Statistics, University of Padua His research focuses on statistical modeling for economic, financial, and environmental applications, with expertise in Bayesian nonparametric clustering, space-time models, semiparametric methods, and computational statistics. He has participated in EU research networks and regionally-funded health monitoring projects. Recent publications explore Bayesian clustering as community detection in financial data, health workforce forecasting, and spatio-temporal modeling. His work emphasizes predictive analytics and complex data structures. As a referee, he evaluates research projects for institutions like the University of Padua and contributes to journals including the Journal of the Royal Statistical Society and Computational Statistics and Data Analysis. He supervises theses in applied statistics for economics, finance, and environmental studies, requiring pre-discussion agreement on topics.

Mattia Stivalمشاهده پروفایل

پژوهشگر

• Health Inequalities
• Spatio-Temporal Modeling
• Sports Performance Analysis
• +۷ مورد دیگر

Mattia Stival is a Researcher at the Department of Economics , Ca' Foscari University of Venice , with a focus on social statistics (SSD: STAT-03/B). His work bridges Bayesian and computational statistics with applications in public health and sports science. Current statistician for the Planet4Health project Previously postdoctoral researcher for the Age-it project on multi-morbidity modeling PhD in Statistical Sciences (University of Padua, 2022) with thesis on Sports Performance Analysis with State Space Models Research Interests combine methodological innovation with real-world impact: Applied : Health inequalities, aging population dynamics, sport-for-health promotion, competitive sports analytics (talent identification, performance monitoring), diffusion models Methodological : Bayesian inference, computational statistics, spatio-temporal modeling, machine learning, Monte Carlo methods Publications demonstrate interdisciplinary trends across: Sports statistics (decathlon/heptathlon scoring, youth-to-elite transition) Bayesian spatio-temporal health modeling Missing data patterns in longitudinal athlete datasets Scientific Recognition : 2023 Honorable Mention for Best PhD Thesis in Applied Statistics by the Italian Statistical Society (SIS) Teaching includes statistics exercises for economics degrees and R coding in finance analytics. Office hours: Wednesday 10-12 by appointment.

Leonardo Impettمشاهده پروفایل

استادیار

• Digital Humanities
• Computational Humanities
• Computer Vision
• +۶ مورد دیگر

Leonardo Impett serves as Assistant Professor in Digital Humanities at the University of Cambridge's Faculty of English and Bye-Fellow in Digital Humanities at Selwyn College, Cambridge. He also leads a research group at the Bibliotheca Hertziana. Previously, he was Assistant Professor of Computer Science at Durham University and Digital Humanities Scientist at the Bibliotheca Hertziana - Max Planck Institute for Art History in Rome. His research uniquely bridges artificial intelligence and art history, focusing specifically on bias and visual ideology in AI systems from an art historical perspective. With a background in information engineering and machine learning, he combines technical expertise with deep humanities knowledge to develop novel approaches for analyzing visual culture through computational methods. His recent publications reveal a strong trend toward interdisciplinary work connecting computer vision with art historical analysis, particularly examining how AI systems interpret and potentially bias visual cultural heritage. His research spans technical AI development, art historical analysis, and critical examination of digital methodologies in cultural institutions. Digital Humanities Fellow at Villa I Tatti – Harvard University Center for Italian Renaissance Studies (2018) Scientific Assistant/Scientist at Bibliotheca Hertziana (2018-2020) PhD in Computer Vision for Art History at EPFL Impett actively collaborates with major cultural institutions including the Liverpool Biennial, Royal Opera House, and Whitney Museum of American Art, applying machine learning to practical challenges in arts and culture. His work demonstrates how technical AI research can be meaningfully integrated with humanities scholarship to produce insights valuable to both domains.