جستجوی استاد راهنمای دانشگاهی

Michele Loretiمشاهده پروفایل

استاد

• Formal methods
• Concurrency theory
• Distributed systems
• +۴ مورد دیگر

Michele Loreti is Full Professor in Computer Science at the University of Camerino within the School of Science and Technology and serves as Director of the School of Advanced Studies. His career includes roles as Research Associate at University of Firenze (2002-2017) and Visiting Professor at IMT Institute for Advanced Studies (2012-2016). He holds a PhD in Mathematical Logic and Theoretical Computer Science from University of Siena (2001) and a Computer Science degree from University of Rome 'La Sapienza' (1997). Research interests: Formal tools for concurrent/distributed systems Quantitative analysis of collective adaptive systems Spatio-temporal model checking Process calculi and modal logics Programming languages for network-aware applications Runtime verification and dynamic system adaptation Article trends: His work focuses on formal verification of cyber-physical systems, spatio-temporal properties, stochastic process calculi, attribute-based communication, and tools like CARMA and muG for collective system analysis. Editorial roles: Assistant editor for Elsevier Journal on Logical and Algebraic Methods in Programming and member of the Reproducibility Board for ACM Transactions on Modelling and Computer Simulation.

FRANCA STABLUMمشاهده پروفایل

استاد

• Cognitive Psychology
• Neuropsychology
• Cognitive Rehabilitation
• +۴ مورد دیگر

Prof. Franca Stablum is a Full Professor at the University of Padua. Her research focuses on cognitive psychology, neuropsychology, and rehabilitation, particularly in attention mechanisms, executive functions, and time perception across populations including older adults and neurological patients. She has pioneered computerized cognitive training tools and neurorehabilitation methodologies. Her work integrates experimental psychology, neuroimaging, and computational modeling. Key areas include: 1) Temporal processing disorders in neurological conditions (e.g., Parkinson's, MS); 2) Prospective memory deficits in brain injury patients; 3) Developmental time perception studies in children; 4) Digital interventions for cognitive aging. She has developed assessment tools like the 'Virtual Kitchen' for executive function evaluation and the SWIFT collaborative web-based training platform. Over 200+ publications span cognitive neuroscience, clinical neuropsychology, and rehabilitation engineering. Her research emphasizes translational approaches, bridging basic science with clinical applications. Active in international collaborations, she has contributed to standardization efforts in cognitive screening (e.g., Italian norms for Addenbrooke's Cognitive Examination).

Alessandro De Parisمشاهده پروفایل

دانشیار

• Mathematics
• Theoretical Computer Science
• Differential Geometry
• +۴ مورد دیگر

Alessandro De Paris is an Associate Professor in the scientific-disciplinary field MAT/03 - Geometry at Link Campus University in Rome. He transitioned from a 24-year permanent research career to his current role, focusing on tensor rank and combinatorial aspects with applications in theoretical computer science and mathematics. His research intersects algebraic geometry, differential geometry, and computational mathematics. His recent publications highlight expertise in Waring decompositions, tensor analysis, and polynomial complexity. Key contributions include determining sharp bounds for ternary algebraic forms and developing mathematical models for olfactory neuron responses. Notable works address Monge-Ampère equations and geometric invariants. Current research trends emphasize algebraic geometry, computational mathematics, and interdisciplinary neuroscience applications. Collaborative efforts include co-authoring a monograph on linear connections in differentiable vector bundles.

Enrico Corradiniمشاهده پروفایل

پژوهشگر

• Information Systems
• Cybersecurity
• Social Network Analysis
• +۵ مورد دیگر

Enrico Corradini is a Researcher at the Department of Information Engineering , School of Engineering, Università Politecnica delle Marche (UNIVPM), Italy. His work focuses on information systems, cybersecurity, and social network analysis using network theory and machine learning approaches. Academic Rank: Researcher Research Focus: Cybersecurity, Social Network Analysis, Machine Learning, Network Theory Contact: e.corradini@univpm.it His recent publications demonstrate expertise in anomaly detection for Internet of Everything ecosystems, multilayer network modeling, and social platform analysis. Office hours available every Tuesday 10:30-12:30 at UNIVPM campus.

Mirco Tribastoneمشاهده پروفایل

استاد

• Stochastic Processes
• Dynamical Systems
• Performance Engineering
• +۴ مورد دیگر

Dr. Mirco Tribastone is Full Professor of Computer Science at IMT School for Advanced Studies in Lucca, Italy, where he leads the SySMA research unit and serves as Deputy Rector. His research focuses on quantitative modeling of concurrent and distributed systems using stochastic processes and differential equations. Primary research streams: Algorithms for aggregation of dynamical systems Performance self-adaption of software systems Inference methods for probabilistic programs Formal methods for biological systems Professor Tribastone has supervised numerous PhD students and leads research projects on cyber-physical systems and adaptive software. His tool development includes ERODE for differential equation analysis and DiffLQN for layered queuing networks. Recent publications demonstrate advanced work in model reduction techniques for chemical reaction networks, optimal autoscaling for microservices, and novel approaches to quantum circuit verification.

Luigi Ceruloمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Genomics
• Machine Learning
• +۳ مورد دیگر

Luigi Cerulo is an Associate Professor in Computer Science at the Department of Science and Technology (DST) , University of Sannio in Benevento. He co-directs the bioinformatics research group with Francesco Napolitano, focusing on integrating computational methods into life sciences. His teaching includes advanced topics like Computational Genomics and Statistics and Bioinformatics for Master's programs in Biotechnology and Biology. Academic Rank: Associate Professor Department: Science and Technology, University of Sannio Email: lcerulo@unisannio.it Research Interests: Driven by cross-disciplinary innovation, his work bridges machine learning and bioinformatics. Key areas include reverse engineering gene regulatory networks using deep graph neural networks, predicting ligand-receptor interactions via graph autoencoders, and exploring non-coding RNA functions with self-supervised learning. His team also investigates drug synergy prediction and AI applications in biological data. Recent Publications: His 15 most recent works span 2022 to 2007, emphasizing machine learning in gene network reconstruction (Chang et al., 2020), deep learning for RNA classification (Noviello et al., 2020), and foundational contributions to software engineering tools (Canfora et al., 2007-2008). Research Infrastructure: The group utilizes high-performance systems including a Superdome Flex server (224 CPU cores, 1.5 TB RAM) and a GPU cluster at Biogem Institute. Advising and Grants: He mentors PhD students in bioinformatics and leads projects such as GENOMA E SALUTE (EU-funded) and Non-Coding RNA Explosion (Ministry of Research grant). Collaborations span institutions like the University of Naples and Biogem.

• Computational topology
• Topological data analysis
• Machine learning
• +۷ مورد دیگر

Francesco Vaccarino is an Associate Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Polytechnic University of Turin and a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is actively involved in research, teaching, and doctoral supervision across multiple programs, including Mathematical Sciences, Applied Mathematics, and Artificial Intelligence. Research Interests: Computational topology Topological data analysis (TDA) Machine learning and geometric learning Algebraic and geometric methods in data science Complex networks and higher-order interactions His recent publications reflect a strong interdisciplinary focus, combining topology with machine learning, neuroscience, and physics. Key themes include persistent homology, simplicial network models, and topological visualization of complex systems. He has made significant contributions to understanding brain networks, scientific collaboration patterns, and phase transitions through topological methods. Scientific Awards: The Best Science Visualizations of the Year (Wired.com, 2014) Advising and Grants: He supervises multiple PhD students in Mathematical Sciences and Artificial Intelligence. He has led research projects funded by competitive tenders (e.g., XTERA-FOOD) and commercial contracts (e.g., with FPT Industrial SPA and Stellantis), focusing on AI, digital transformation, and industrial data analysis. He has served as Scientific Director for corporate training programs in AI and data science. Conferences and Leadership: He has been Program Chair for several international events including AMTCS7, TOPDRIM, Toponets15, and ECCS 2013, promoting the application of topology in data science. Labs and Affiliations: He is a core member of SmartData@PoliTO, a leading data science laboratory fostering interdisciplinary research in big data and AI.

Bruno Carpentieriمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• High-Performance Computing
• Computational Electromagnetics
• +۵ مورد دیگر

Bruno Carpentieri is an Associate Professor in Applied Mathematics at the Faculty of Computer Science , Free University of Bozen-Bolzano , Italy. His work focuses on Numerical Linear Algebra , High-Performance Computing , and Preconditioning Techniques for large-scale scientific problems. Education : Laurea in Applied Mathematics (1997, University of Bari Aldo Moro); PhD in Computer Science (National Polytechnic Institute Toulouse). Research Interests : Sparse linear systems, Krylov subspace methods, computational electromagnetics, PageRank problems, and fractional calculus applications in nonlinear engineering. His publications (1998–2025) emphasize efficient solvers for multi-shifted systems, parallel computing, and preconditioning strategies. Recent works (2025) explore fractional-order methods and multilayer network centrality . He has collaborated on ITER tokamak simulations (F4E/MIUR grants) and biomedical finite element models . Grants : F4E-2008-OPE-06-06-11 (ITER analysis); MIUR PRIN 2010SPS9B3. Key Contributions : VBARMS preconditioner (2014), hybrid block GMRES variants (2018), and chaos-enhanced fractional solvers (2025).

Vincenzo Bonniciمشاهده پروفایل

دانشیار

• Bioinformatics
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Vincenzo Bonnici is an Associate Professor in Informatics at the Department of Mathematical, Physical and Computer Sciences , University of Parma, Italy. His academic career includes a PhD in Computer Science from the University of Verona (2015), preceded by a master’s degree from the University of Catania (2011). He has held research positions at prestigious institutions, including the Institute for Genomics and Bioinformatics (IGB) , University of California, Irvine (2013–2014). Education BSc/MSc in Computer Science from University of Catania (2008/2011) PhD in Computer Science from University of Verona (2015) His research focuses on bioinformatics and computational biology , with a strong emphasis on subgraph matching algorithms for biomedical graphs, genomic sequence analysis using information theory, and parallel computing for biological networks. His work spans pangenomics, phylogenomics, and non-coding RNA studies, with applications in GPU and SMP architectures. The article trends reflect his core expertise in graph algorithms and computational genomics. Key contributions include MULTI-GRAPHMATCH (2025) for multigraph analysis, ARC-MATCH (2024) for edge domain-based graph querying, and foundational work on pangenome discovery (e.g., PANDELOS series, 2023). Parallel computing and information theory are recurring themes across his publications. Scientific awards include the ICPR 2014 international graph-matching contest and a best poster award at the Jacob T. Schwartz International School for Scientific Research. He has served as a speaker at 12 international conferences. Teaching includes courses on Artificial Intelligence Algorithms (2025/2026), Artificial Intelligence Laboratory , and Software Engineering at the University of Parma. His work integrates algorithmic innovation with biological data analysis.

Riccardo Zecchinaمشاهده پروفایل

استاد

• Machine Learning
• Statistical Physics
• Computer Science
• +۹ مورد دیگر

Riccardo Zecchina is a Full Professor in the Department of Computing Sciences at Bocconi University, Milan, holding this position since 2017 after serving as Full Professor of Theoretical Physics at the Polytechnic University of Turin (2007-2017) and as research scientist and head of the Statistical Physics Group at the International Centre for Theoretical Physics (ICTP) in Trieste (1997-2007). He maintains long-term visiting affiliations with Microsoft Research (Redmond and Cambridge MA) and the Laboratory of Theoretical Physics and Statistical Models (LPTMS) at the University of Paris-Sud. His educational background includes a PhD in Theoretical Physics from the University of Turin under the supervision of Tullio Regge. Professor Zecchina's research spans the intersection of statistical physics, computer science, and machine learning, with core expertise in learning algorithms, combinatorial optimization, probabilistic message-passing, computational neuroscience, and interdisciplinary applications of statistical physics. His work emphasizes both theoretical foundations and practical implementations in modern AI and biologically constrained models, addressing fundamental questions in neural network training and information processing. His publication trajectory reveals a clear evolution from foundational work on phase transitions in satisfiability problems (1990s-2000s) to contemporary focus on deep learning optimization, loss landscape analysis, and computational biology applications (2010s-present), demonstrating consistent innovation at disciplinary boundaries. His major scientific recognitions include: ERC Advanced Grant (2011-2015) Lars Onsager Prize in Theoretical Statistical Physics (2016, awarded jointly with M. Mezard and G. Parisi by the American Physical Society) Professor Zecchina leads significant research initiatives including his ERC-funded work and co-founded Bocconi University's Department of Computing Sciences as an interdisciplinary hub for information and computation research. While specific student mentorship details are not publicly enumerated, his extensive collaborative publication record indicates active supervision of researchers across multiple institutions. The department operates under principles of openness and collegiality, fostering cross-disciplinary teams tackling fundamental modeling problems in computation.

Georgia FARGETTAمشاهده پروفایل

پژوهشگر ارشد

• Optimization
• Machine Learning
• Game Theory
• +۹ مورد دیگر

Georgia Fargetta is a Research Fellow (RTD-A) in Computer Science at the Department of Mathematics and Computer Science, University of Catania, Italy. She holds a PhD in Computer Science (2022) and both Bachelor's (2017) and Master's (2019) degrees in Mathematics from the University of Catania. Current academic affiliation: Department of Mathematics and Computer Science, University of Catania Academic rank: Research Fellow RTD-A (INF/01) Research themes: Optimization, Machine Learning, Game Theory, and applications to supply chain networks and crowd evacuation modeling Research Interests Georgia's research spans multiple domains including: Optimization Game Theory Metaheuristic Algorithms Crowd Simulation Medical Supply Chain Her publications demonstrate expertise in applying these methodologies to diverse problems such as social media content competition, emergency evacuation planning, and medical supply allocation. The scientific awards section highlights: Young Women in Operations Research Award (EURO WISDOM, 2021) Young Women for Operational Research (2022) She actively participates in international conferences including ODS, EUROPT, and MIC. As member of IPLAB (Image Processing Laboratory), she contributes to research in Computer Vision and Multimedia.

• Complex systems
• Data analysis
• Statistical analysis
• +۷ مورد دیگر

Anna Filomena Carbone is a Full Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin, where she directs the NOISELAB research laboratory. Her academic career spans over three decades since earning her PhD in Physics in 1993, with significant contributions to interdisciplinary research at the intersection of physics, data science, and complex systems. Her research interests encompass complex systems, data analysis, statistical analysis, modeling and simulation, and time series analysis, with a particular focus on noise in physical systems. Dr. Carbone has authored over 100 articles in international journals and conference proceedings, with recent publications (2023-2025) demonstrating continued productivity in areas including financial time series analysis, urban scaling laws, quantum information, and brainwave dynamics. Her work shows a clear trend toward increasingly interdisciplinary applications of statistical physics methods to real-world complex systems across multiple domains. Member of the Board of the Nonlinear and Statistical Physics (NSP Board) of the European Physical Society (EPS) since 2009 Member of Scientific Committee - European Physical Society, Belgium (2009-2015) Editorial board member for multiple journals including Physica A, European Physical Journal B, and Chaos, Solitons and Fractals Program chair for multiple International Conferences on Data Science Challenges Dr. Carbone actively supervises PhD students (including Chiara Panico in Computer and Systems Engineering) and serves as Course Instructor for Quantum Information and Quantum Computing courses. Her research projects include TED4LAT (2022-2025), FuturICT 2.0 (2017-2020), and Challenges in Data Science (2016). She maintains active collaborations with institutions including the European Commission and the Department for Digital Transformation of the Presidency of the Council of Ministers.

Luca Becchettiمشاهده پروفایل

دانشیار

• Algorithms
• Graph Theory
• Network Science
• +۷ مورد دیگر

Luca Becchetti is an Associate Professor in the Department of Computer, Automatic, and Management Engineering at Sapienza University of Rome, where he has been a core faculty member since 2012. He holds a PhD and M.Sc. in Computer Engineering from the same institution and has been actively contributing to research in algorithms and network science. PhD in Computer Engineering, Sapienza University of Rome, 1998 M.Sc. in Computer Engineering, Sapienza University of Rome, 1995 His research focuses on the design and analysis of algorithms for NP-hard and online optimization problems, with a strong emphasis on network design and resource allocation. More recently, his work has evolved to include large-scale information network analysis, such as the Web and Internet. Current research directions involve distributed graph clustering using spectral graph theory, modeling of complex systems, and pioneering applications in Network Medicine through advanced graph mining and spectral techniques. He is a member of research groups in Computer Networks and Pervasive Systems, Algorithms and Data Science, and the Theory of Deep Learning. His recent publications span areas of opinion dynamics, network medicine, and distributed algorithms, demonstrating a trend toward interdisciplinary research combining theoretical computer science with biological and social network applications. Key themes include synchronization in minority dynamics, robustness in opinion models, and multi-omics integration for disease gene prediction. He has been involved in significant research projects including the EU FET-OPEN COSIN, EU IST APPOL II, and the National FIRB WEBMINDS. While no formal list of advisees is provided, he supervises students in algorithms and data science. He teaches courses such as Big Data Computing and Web Information Retrieval. Luca Becchetti is affiliated with the Algorithm Engineering group and the Department of Computer, Automatic, and Management Engineering (DIAG), where he leads research in distributed algorithms and network analysis.

• Algorithms
• Concurrency
• Concurrent algorithms
• +۱۲ مورد دیگر

Paola Quaglia is an Associate Professor at the Department of Information Engineering and Computer Science, University of Trento. Her primary teaching activities include courses on Concurrency and Linguaggi formali e compilatori (Formal Languages and Compilers), focusing on shared-memory concurrency and compiler design methodologies applicable to formal/natural language analysis. Quaglia's research expertise spans Algorithms, Concurrency, Process Calculi, Formal Languages, Systems Biology, and Stochastic Processes . Her work bridges computational modeling with biological systems, emphasizing formal verification and concurrent programming paradigms. Notable publication trends include contributions to stochastic process algebra , BlenX/Beta-binders for biological modeling, and model-checking techniques for concurrent systems. She has extensively explored the intersection of programming language theory and computational biology.

• Machine Learning
• Medical Imaging
• Biomedical Data Analysis
• +۳ مورد دیگر

Salvatore Calderaro is a Research Fellow at the Department of Physics and Chemistry - Emilio Segrè Office, Università degli Studi di Palermo. His work focuses on biomedical data analysis, explainable AI, and graph-based machine learning methods. Current research emphasizes interpretable machine learning for medical imaging, including glioma segmentation, histopathology classification, and diabetic maculopathy grading. Publications highlight the application of Graph Neural Networks, Transformers, and deep embeddings across computational biology, medical diagnostics, and biomedical engineering. Contact: salvatore.calderaro01@unipa.it .