جست‌وجوی استاد و پژوهشگر

• 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).

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.

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.

• Animal Behavior
• Bioacoustics
• Ecoacoustics
• +۳ مورد دیگر

Daria Valente is a Tenure-track Assistant Professor at the Department of Life Sciences and Systems Biology, University of Turin. Her research focuses on the evolution of animal and human behavior, with a specialization in primate bioacoustics, ecoacoustics, and vocal communication. Research Interests: Valente's work explores the complexity of primate vocalizations, emphasizing the indri species. She investigates topics such as vocal flexibility, categorical rhythms, nonlinear acoustic phenomena, and the interplay between genetic distance and song dissimilarity. Her studies often integrate fieldwork in Madagascar with advanced sound analysis techniques. Teaching: She offers courses in bioacoustic analysis, scientific communication, and ethological methods, reflecting her expertise in modern behavioral research tools. Publication Trends: Valente's research output spans evolutionary biology, acoustic monitoring, and interdisciplinary approaches to primate behavior. Her recent work includes collaborations on global citizenship education through ecological research.

• Computer vision and pattern recognition
• Digital image processing
• Medical image processing
• +۴ مورد دیگر

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.

Bartolomeo Montrucchioمشاهده پروفایل

استاد

• Computer Vision
• Quantum Computing
• Image Processing
• +۵ مورد دیگر

Bartolomeo Montrucchio is a Full Professor of Information Processing Systems (ING-INF/05) at the Department of Control and Computer Engineering (DAUIN) of the Polytechnic University of Turin. He is a member of the Interdepartmental Center Photonext - PoliTo Interdepartmental Center on Applied Photonics and serves as deputy director at the Interuniversity Center of Regional Interest for the Training of Secondary School Teachers (CIFIS) since July 2012. Additionally, he has held an adjunct professor position at the University of Illinois at Chicago during July 2008. Professor Montrucchio's research spans several cutting-edge areas with a primary focus on quantum computing, computer vision, and sensor networks. His work encompasses image processing, scientific visualization, parallel and distributed systems, and wireless sensor networks. He actively contributes to European research initiatives including the EQUO (European QUantum ecOsystems) project as Scientific Responsible. His research bridges theoretical computer science with practical applications across multiple industries. His publication record shows a strong trajectory toward quantum technologies, with numerous recent publications focusing on quantum machine learning, quantum algorithms for financial applications, and quantum applications in cybersecurity. His work demonstrates increasing emphasis on practical implementations of quantum computing in real-world scenarios, particularly in industrial settings and telecommunications. Best student paper award at BIOSIGNAL2002, conferred by EURASIP, Italy (2002) Associate Editor of IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019-present) Professor Montrucchio actively supervises numerous PhD students working on quantum computing applications across various domains including finance, cybersecurity, traffic optimization, and industrial use cases. His teaching portfolio includes courses on Quantum Computing, Parallel and Distributed Computing, and Image Processing and Computer Vision across multiple degree programs including Computer Engineering, Biomedical Engineering, and Quantum Engineering. He leads multiple research projects funded by both competitive calls and commercial contracts, with a significant focus on quantum technologies since 2019. His patent portfolio includes several inventions related to tire manufacturing processes and visual rehabilitation for telemedicine.

Roy Cerquetiمشاهده پروفایل

استاد

• Quantitative Methods for Economics and Finance
• Data Science
• Complex Networks Analysis
• +۵ مورد دیگر

Roy Cerqueti is Full Professor of Statistics at Sapienza University of Rome where he serves as Director of the Doctoral School in Economics and Social Sciences. He is affiliated with the Department of Social and Economic Sciences and maintains a research affiliation with GRANEM at the University of Angers, France. Previously, he held a part-time professorship at London South Bank University, UK. His research focuses on quantitative methods for economics and finance, data science, and complexity theory. His work spans complex networks analysis, statistical modeling, and applications to financial systems, social dynamics, and policy evaluation. Recent publications demonstrate expertise in Benford's Law applications, cryptocurrency market analysis, social media analytics, and systemic risk assessment in financial networks. Cerqueti's publication record shows strong trends in network science applications to economic systems, with increasing focus on sustainability, social media analytics, and financial market stability. His 2024-2025 work integrates advanced statistical techniques including Random Matrix Theory, fuzzy clustering methods, and multilayer network approaches to analyze complex economic phenomena. He has served as a review panel expert for COST Action proposals (2017-2019) under appointment by the Italian Ministry of Education, University and Research. Cerqueti holds multiple editorial positions and regularly serves as referee for scientific journals and institutions. His teaching portfolio includes courses in International Statistics and Quantitative Methods for Economics at the undergraduate level. As Director of the Doctoral School, he oversees advanced research training in Economics and Social Sciences. He has delivered invited seminars and courses at prestigious international universities, demonstrating global academic engagement. Cerqueti leads research projects including "Analysis of rankings and preference data: new methods and applications" and has participated in numerous national and international research initiatives as Principal Investigator. His lab work focuses on network-based approaches to economic and social systems, with particular emphasis on computational methods for complex data analysis.

Josef Kittlerمشاهده پروفایل

استاد

• Pattern Recognition
• Machine Learning
• Biometrics
• +۶ مورد دیگر

Professor Josef Kittler is a distinguished academic at the University of Surrey, where he serves as Professor and heads the Department of Electronics within the Faculty of Engineering and Physical Sciences. He founded the Centre for Vision, Speech and Signal Processing (CVSSP), which has grown into a major research hub with over 100 researchers. Kittler joined the university in 1986 after five years at EPSRC Rutherford Appleton Laboratory and prior research fellowships at Cambridge, Oxford, Southampton, and ENST Paris. His research spans Pattern Recognition, Machine Learning, Biometrics, Signal Processing, and Cognitive Vision, with significant contributions to Deep Learning and Ambient Assisted Living. His prolific output includes over 700 scientific publications and the seminal textbook Pattern Recognition: A Statistical Approach (Prentice Hall). As Series Editor of Springer Lecture Notes in Computer Science, he shapes discourse across computer vision and signal processing disciplines. Professor Kittler's exceptional contributions are recognized through: KS Fu Prize (2006) for outstanding pattern recognition research Honorary Doctorates from University of Lappeenranta (1999) and Czech Technical University (2007) IET Faraday Medal (2008) EURASIP Fellow designation (2009) His leadership extends to serving as President of the International Association for Pattern Recognition (1994-1996) and co-founding OmniPerception Ltd., demonstrating sustained impact across academia and industry through the CVSSP research ecosystem.

• Biomedical Engineering
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Donato Cascio is an Associate Professor in the Department of Physics and Chemistry - Emilio Segrè at the University of Palermo. His research focuses on biomedical engineering, machine learning applications in healthcare, and medical imaging analysis. He teaches physics to students in Dentistry, Veterinary Medicine, and Medicine, with office hours available via Teams. Current position: Associate Professor at University of Palermo (PHYS-06/A) Teaching: Physics for Dentistry, Veterinary Medicine, and Medicine students Research: Machine learning in diagnostics, medical imaging, AI-driven healthcare solutions Dr. Cascio's work spans biomedical applications of artificial intelligence, including AI-assisted cancer diagnosis, neonatal health prediction models, and automated immunofluorescence image analysis. His recent publications demonstrate a strong focus on machine learning for medical decision support and clinical imaging enhancement. His machine learning research trends include applications for breast cancer classification, stem cell donation awareness, and neonatal outcomes prediction. While no specific scientific awards are mentioned, his work has contributed to projects like AIDA (Auto Immunity: Diagnosis Assisted by Computer) and MAGIC-5 mammographic database initiatives.