جستجوی استاد راهنمای دانشگاهی

• Statistical Inference
• Applied Statistics
• Regression Graphical Models
• +۳ مورد دیگر

Giovanni M. Marchetti is a Professor in the Department of Statistics, Computer Science, Applications "G. Parenti" at the University of Florence, Italy. His office is located at viale Morgagni, 59, 51134 Florence, with contact information including email giovanni.marchetti@disia.unifi.it and phone +39 055 275 1516. He teaches Statistics courses for Mathematics and Economics degree programs at the university. Professor Marchetti's primary research interests focus on Statistical Inference and Applied Statistics , with specific expertise in Regression Graphical Models and Multivariate Analysis . His recent research projects include Parameterizations of chain graphs, Graphical models under constraints, Inference in graphical models, and Palindromic binary distributions. His work bridges theoretical statistical developments with practical applications across various scientific domains, as evidenced by publications in journals ranging from mathematical statistics to ecological research. An analysis of Professor Marchetti's 15 most recent publications (2009-2017) reveals a consistent focus on graphical models and statistical theory. His research spans from foundational work on binary distributions and chain graphs to applications in biological networks and social sciences. The predominant themes include conditional independence structures, parameterizations of complex statistical models, and computational approaches for high-dimensional data analysis. His collaborative work, particularly with Nanny Wermuth, has produced significant contributions to the understanding of graphical model structures. Professor Marchetti has established significant collaborations, particularly with Nanny Wermuth, and his work appears in prestigious journals including The Annals of Statistics , Journal of Multivariate Analysis , and Electronic Journal of Statistics . His research contributes substantially to the theoretical foundations of graphical models while maintaining relevance to practical applications in diverse fields such as evolutionary biology, marine ecology, and social sciences.

Paola Leccaمشاهده پروفایل

استادیار

• Graph Theory
• Dynamical Networks
• Control Theory
• +۴ مورد دیگر

Paola Lecca is an Assistant Professor at the Free University of Bozen-Bolzano's Faculty of Engineering, specializing in theoretical and applied research on graph theory, dynamical networks, control theory, and causal inference. She is a Senior Professional Member of the Association for Computing Machinery and leads projects at the Smart Data Factory laboratory, focusing on technology transfer to industry. Her research integrates mathematical methods for complex systems in biological contexts, including systems biology, biophysics, and biochemistry. She develops computational approaches for network dynamics, simulation, and analysis, aiming to understand emergent properties in multi-agent systems. Courses taught include Mathematics and Statistics for Data Science and Preparatory Mathematics. She actively contributes to conferences and editorial boards, including Frontiers in Bioinformatics and Big Data Analysis for Medical Sciences.

Marco Frascaمشاهده پروفایل

دانشیار

• Artificial Intelligence in Biology & Medicine
• Deep Learning
• Deep Neural Network Compression
• +۷ مورد دیگر

Marco Frasca is an Associate Professor in the Department of Computer Science at the University of Milan. He is a member of the AnacletoLab (Computational Biology and Bioinformatics Lab) and holds a Ph.D. in Computer Science from the University of Milan. His research focuses on machine learning applications in bioinformatics, including artificial intelligence for medicine, deep learning, and neural network compression. He has held visiting positions at institutions such as the University of Toronto and Johannes Gutenberg University. Education: Ph.D. in Computer Science (2012), University of Milan M.Sc. in Computer Science (2005), University of Salerno Research Interests: Frasca's work bridges machine learning and computational biology, emphasizing methods for imbalanced data, hierarchical classification, and biomolecular network analysis. He develops algorithms for gene function prediction, disease-gene prioritization, and compressed deep learning models. His tools, such as COSNet and UNIPred-Web, are widely used in bioinformatics. Grants & Projects: Leader of the OPTIMA-NASH project (Pfizer-funded), integrating AI for NASH management PRIN Project 2017 (Italian Ministry of Education) on data structures and algorithms Labs & Teams: Frasca leads the AnacletoLab, focusing on AI-driven solutions for biology and medicine. The lab collaborates internationally on projects like CAFA3 and the EPIGEN flagship.

Matteo Reمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Matteo Re is an Associate Professor at the Department of Computer Science, University of Milan. His research focuses on Machine Learning, Computational Biology, and Bioinformatics, with applications in genomics, drug network analysis, and semi-supervised learning. He has contributed to developing tools like RANKS for node label ranking in biological networks and HyperSMURF for predicting genetic variants. His work integrates graph-based methods, ensemble learning, and network modeling to address challenges in disease association, drug repositioning, and biomolecular data analysis. Re’s research interests include semi-supervised node classification, imbalanced data handling, and functional gene prioritization. He has collaborated on projects involving clinical phenotype prediction using patient biomolecular networks and drug therapeutic categorization. His methods emphasize scalable learning, secondary memory usage, and multi-species data fusion. Awards and grants are not explicitly mentioned, but his extensive publication record reflects strong contributions to computational biology and machine learning.

Dario Malchiodiمشاهده پروفایل

دانشیار

• Machine Learning
• Fuzzy Set Induction
• Bioinformatics
• +۷ مورد دیگر

Dario Malchiodi is an Associate Professor at the Department of Computer Science, University of Milan. He holds visiting roles at Université de la Côte d'Azur and INRIA. His research focuses on machine learning, fuzzy set induction, bioinformatics, and data quality. He earned a PhD in Computational Mathematics (2000) and MSc in Computer Science (1997), both from the University of Milan. Key research interests include applications in veterinary science, forensics, and protein design. He has published over 100 peer-reviewed papers and led projects like the PRIN-funded 'Multicriteria Data Structures and Algorithms'. Awards: CSEDU 2018 Best Poster, Informatics Europe Best Practices in Education Teaching: Algorithms for Massive Datasets, Statistics, and courses at Université de la Côte d'Azur Roles: Member of Data Science Research Centre, CINI Big Data Lab His work bridges theoretical machine learning with practical applications in healthcare, archaeology, and cybersecurity. Ongoing projects explore large language models in protein engineering and federated learning security.

Francesco Cauteruccioمشاهده پروفایل

استادیار

• Social and Complex Network Analysis
• Data Mining and Data Science
• Internet of Things (IoT)
• +۳ مورد دیگر

Francesco Cauteruccio is a Tenure-Track Assistant Professor at the Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM) at the University of Salerno, Italy. He is affiliated with the CORE and S 3 labs, focusing on interdisciplinary research at the intersection of social networks, IoT, and hybrid AI models. Ph.D. in Mathematics and Computer Science, University of Calabria (thesis: "Generalizing Identity-Based String Similarity Metrics: Theory and Applications") Italian National Scientific Qualification (ASN) for Associate Professor in Computer Science and Computer Engineering Visiting researcher at University of Klagenfurt (AI & Cybersecurity) and University of Lyon 1 (CERMEP/CREATIS) His research spans: Social & Complex Networks: Modeling cross-blockchain ecosystems, Reddit community dynamics, and sentiment propagation IoT & Hybrid Intelligence: Scope formalization, anomaly detection in MIoT networks, and air quality prediction systems Logic Programming: Integrating Answer Set Programming with machine learning for medical informatics and pattern mining Algorithm Design: Multi-Parameterized Edit Distance (MPED) for bioinformatics and network analysis Recent publications address network theory applications, hybrid reasoning frameworks, and high-utility pattern mining with formal constraint specification. He serves as guest editor for journals like Big Data and Cognitive Computing and Frontiers in Medical Engineering , and organizes international workshops on hybrid intelligence (HYDRA, VIPERC). Academic distinctions: Italian National Scientific Qualification for Associate Professor positions (2024) Guest Editor for 12+ special issues in data science and IoT journals Program/General Chair for 8 international workshops (2022-2024) His teaching portfolio includes courses on algorithms, distributed systems, and data analytics at University of Salerno, Polytechnic University of Marche, and University of Calabria.

Clara Stegehuisمشاهده پروفایل

دانشیار

• Probability Theory
• Graph Theory
• Stochastic Networks
• +۱۱ مورد دیگر

Clara Stegehuis is an Associate Professor in the Mathematics of Operations Research group at the University of Twente . Her research sits at the intersection of probability theory, graph theory, and stochastic networks, with applications in network science, physics, and computer science. Education: PhD in Networks with Communities and Clustering from Eindhoven University of Technology (2019), M.S. and B.S. in Applied Mathematics from University of Twente Academic Positions: Associate Professor (2022–present) and Assistant Professor (2019–2021) at University of Twente Her research focuses on epidemic spreading/percolation processes on random graph models, network motif analysis, asymptotic network properties (degree-degree correlations, clustering), and geometric networks. She investigates how network structures influence robustness and connectivity patterns in complex systems like social media, internet infrastructure, and biological networks. Key grants include: NWO M-2 grant (2022) for chemical reaction network analysis NWO Veni grant (2020) for subgraph pattern research KWG PhD prize (2018) She actively contributes to academic service as: Board member of Netsci NL Board member of The Young Academy at University of Twente (2019) Organizer of YEP XV workshop

Marco Veneroniمشاهده پروفایل

دانشیار

• Partial differential equations
• Calculus of variations
• Liquid crystals
• +۱۸ مورد دیگر

Marco Veneroni is Associate Professor at the Department of Mathematics , University of Pavia . His research spans multiple fields including Partial Differential Equations , Calculus of Variations , and Optimal Transportation , with applications to materials science, biology, and engineering. Specializes in gradient flows and Wasserstein metrics for imaging and chemical processes Contributes to stochastic homogenization and elasto-plasticity models Research includes nematic liquid crystals , diblock copolymers , and cardiac electric field modeling Veneroni teaches courses like Mathematical Analysis and Advanced Mathematical Methods . He has supervised numerous students through tutoring programs and pre-course initiatives . His work involves collaborations across McGill University , TU Dortmund , and Università degli Studi di Pavia .

• Combinatorial Optimization
• Meta-heuristics
• Mixed Integer Programming
• +۷ مورد دیگر

Luigi De Giovanni is an Associate Professor of Operations Research at the Department of Mathematics, University of Padua, where he has been faculty since 2007 (promoted to Associate Professor in 2018). He holds the Italian academic qualification as Full Professor since 2020. His research focuses on Combinatorial Optimization, Meta-heuristics, Mixed Integer Programming, and their applications in Transportation and Logistics, Air traffic and Airport Optimization, Telecommunication Network Design, and Production Scheduling. His work bridges theoretical optimization methods with practical industrial applications across various sectors including transportation, manufacturing, and telecommunications. Professor De Giovanni has led and participated in several significant research projects including: European Research Project 'OptiFrame - An Optimization Framework for Trajectory Based Operations' (H2020-SESAR-2015-1, 2016-2018) PRIN Project 'Nonlinear and Combinatorial Aspects of Complex Networks' (2017-2020) European Research Project 'AAS - Integrated Airport Apron Safety Fleet Management' (2008-2011) European Research Project 'SCOOP - Sheet Cutting and Process Optimization for Furniture Enterprises' (2006-2008) His extensive publication record spans from 2002 to 2024, with numerous articles in high-impact journals such as Transportation Science, IEEE Access, and European Journal of Operational Research. Recent publications show a strong emphasis on data-driven approaches to air traffic management, vehicle routing and logistics optimization, and graph theory applications, demonstrating continued research productivity and relevance. Professor De Giovanni teaches courses in Operations Research, Methods and Models for Combinatorial Optimization, and Stochastic Optimization at the University of Padua. His office hours are on Thursdays from 10:30 am to 12:30 pm by appointment.

Michela Quadriniمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• RNA Secondary Structures
• +۳ مورد دیگر

Michela Quadrini is an Assistant Professor at the University of Camerino with expertise spanning Bioinformatics , Computational Biology , and Graph Neural Networks . Her academic work focuses on RNA structure analysis, human activity recognition, and formal methods for collective adaptive systems. Research Interests include: RNA pseudoknots comparison and classification Machine learning applications in biomedical signal processing Spatial logics for complex system modeling Protein-protein interaction site prediction Ontology-based frameworks for activity recognition Publications demonstrate methodological innovation across: RNA structure alignment and translation tools (TARNAS) Graph neural network programming languages (μG) Stress detection from wearable sensor data Formal verification of collective systems Immunoinformatics feature engineering Technical Expertise combines computational biology with advanced machine learning techniques, evidenced by contributions to: Topological data analysis for RNA structures Convolutional neural network architectures Semantic ontologies in mechatronic systems Integral equation numerical methods

• Combinatorial optimization
• Statistical physics
• Epidemic inference
• +۷ مورد دیگر

Alfredo Braunstein is an Associate Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino, and a member of SmartData@PoliTO laboratory. His research spans combinatorial optimization, statistical physics of complex systems, epidemic inference, and computational biology . He leads the Simultaneous Inference from Multiscale Biological Data (SIMBAD) project (2023-2027) and previously led Statistical Inference via Belief Propagation for Epidemics (SIBYL) (2015-2017). He has supervised multiple PhD students in Physics of Complex Systems . Research Interests : Statistical physics, machine learning, network science, epidemic modeling, and optimization. Key Collaborations : Collegio Carlo Alberto, Human Genetics Foundation, and interdisciplinary teams. His recent publications focus on epidemic risk estimation , network dismantling , and message-passing algorithms . He received the La Ricerca dei Talenti award from Fondazione CRT (2015).

• Artificial Intelligence
• Machine Learning
• Neural-Symbolic AI
• +۵ مورد دیگر

Marco Gori is a Full Professor at the Department of Information Engineering and Mathematical Sciences , University of Siena. His research spans Artificial Intelligence , Machine Learning , and Neural-Symbolic AI , with significant contributions to Educational Technology and Knowledge Graphs . He teaches Algorithms and Data Structures for undergraduate Management Engineering and Fundamentals of Machine Learning for graduate Artificial Intelligence and Automation Engineering . His recent work integrates Graph Neural Networks and State-Space Models for tasks like educational quiz generation and interoperable supply chain traceability. His publications highlight trends in Hybrid AI , focusing on interpretable models, cross-language educational tools, and temporal sequence processing. Articles like Grounding Methods for Neural-Symbolic AI and Interpretable-by-design Link Prediction underscore his emphasis on explainability and knowledge integration in AI systems.

• Bioinformatics
• Algorithmic Biology
• Natural Computing
• +۴ مورد دیگر

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.

• Bioinformatics
• Computational Biology
• Graph Algorithms for Biological Networks
• +۷ مورد دیگر

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.

Rossana Mastrandreaمشاهده پروفایل

استادیار

• Complex Networks
• Health Economics
• Environmental Economics
• +۳ مورد دیگر

Rossana Mastrandrea is an Assistant Professor at the IMT School for Advanced Studies Lucca, affiliated with the AXES Research Unit. Her work integrates complex network analysis with interdisciplinary research in Economics, Environmental Science, and Behavioral Studies. She holds a PhD in Economics from the Sant’Anna School of Advanced Studies (Pisa) and has conducted research at the Lorenz Institute of Theoretical Physics (Leiden, Netherlands) and the INET project on Macroeconomics Networks. Her research focuses on theoretical and empirical analysis of complex networks in economic, social, and biological systems. Current projects include studying pharmaceutical innovation through patent citation networks (PRIN project on mRNA vaccines), pro-environmental behavior using evolutionary game theory, and carbon emissions via network approaches. She also explores brain connectivity dynamics and global trade networks using statistical physics and agent-based modeling. Key themes in her work include network reconstruction, motif analysis, and the application of optimal transport methods. Her recent studies address topics like cooperation dynamics in structured populations, coalition formation in litigation, and the role of information load in functional brain connectivity. Rossana coordinates the PRIN project analyzing public and private roles in pharmaceutical innovations and has contributed to high-impact studies on environmental performance metrics, global value chains, and schizophrenia-related brain network alterations.