جست‌وجوی استاد و پژوهشگر

Sonia Petroneمشاهده پروفایل

استاد

• Bayesian Statistics
• Predictive Modeling
• Bayesian Nonparametrics
• +۱ مورد دیگر

Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).

Massimiliano NUCCIOمشاهده پروفایل

دانشیار

• Digital Transformation
• Digital Economy
• Industry 4.0
• +۸ مورد دیگر

Massimiliano Nuccio is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Venice School of Management. He holds roles such as Scientific Director of Bliss - Digital Impact Lab (since 2023), Honorary Research Fellow at Birmingham Business School (2021-2024), and Visiting Professor at Venice International University. His research focuses on Digital Transformation, Cultural Industries, Urban Development, and Business Analytics. He holds a PhD in Communication Economics (IULM University, 2007) and has taught at institutions like Bocconi University and Leuphana Universität. Teaching includes courses on Digital Marketing, Cultural Policy, and Data Analytics across undergraduate and graduate programs. His research explores intersections between digital technologies, cultural economics, and regional development. Recent work analyzes firm growth dynamics, creative clusters, and digital skills in cultural sectors. He has contributed to policy-oriented studies on urban regeneration and cultural district governance. He has led academic programs such as the Master's in Data Analytics for Business & Society and contributed to initiatives like the MADAS Master in Turin. His interdisciplinary approach bridges economics, management, and cultural studies, with publications in journals like Industrial and Corporate Change and Economic Geography.

Federica Mucciمشاهده پروفایل

دانشیار

• International Law
• Cultural Heritage Protection
• European Union Law
• +۴ مورد دیگر

Federica Mucci is an Associate Professor of International Law at the University of Rome Tor Vergata, affiliated with the Department of History, Humanities and Society. She specializes in international protection of cultural heritage, European Union law, and treaty law. Her teaching includes courses on cultural heritage protection and EU law for programs in Education and Tourism. Legal Expert: Italian Ministry of Foreign Affairs UNESCO Delegation Member: Contributed to the 2005 UNESCO Convention on cultural diversity and its implementation. Her research focuses on international law frameworks for cultural heritage, maritime law, treaty interpretation, and environmental protection. Key publications include monographs on cultural heritage law (2012) and a PhD thesis on EU copyright law (1998). Publications span interdisciplinary topics such as topological data analysis, though the majority of her work aligns with legal and humanities disciplines. Awards: No specific prizes are mentioned, but her scholarly output includes influential books and articles on international law. Advising/Grants: No formal student advisees or grant details provided in text. Labs/Teams: No specific lab affiliations mentioned, though her role at UNESCO implies collaboration with international bodies.

• Multivariate Analysis
• Fuzzy Clustering
• Probabilistic Graphical Models
• +۳ مورد دیگر

Vincenzina Vitale serves as a Tenure-Track Assistant Professor of Statistics within the Department of Social and Economic Sciences at Sapienza University of Rome. Her academic profile centers on advanced statistical methodologies with applications spanning economics, public policy, and sustainability initiatives. She teaches core courses including Statistics and Data Science for Sustainability and Statistical Methods and Models for Economics and Public Policy, maintaining regular office hours on Tuesdays from 12:30 to 14:30 by email appointment. Her research program focuses on multivariate analysis, specializing in innovative fuzzy clustering techniques for complex data structures such as time series, spatial data, and mixed data types. She extensively employs probabilistic graphical models, particularly Bayesian networks, for data integration and modeling challenges. This work bridges theoretical statistics with practical applications in electoral analysis, financial volatility, sports analytics, and public health domains including COVID-19 pandemic response. Analysis of her 15 most recent publications reveals a dominant trend toward developing spatially-aware and robust fuzzy clustering algorithms. These methods increasingly incorporate regularization techniques, entropy principles, and copula models to handle interval-valued data, count data, and tail dependencies. Key application areas include regional competitiveness measurement (NUTS2/NUTS3 frameworks), electoral studies, sports performance analytics, and pandemic modeling, demonstrating consistent contributions to top-tier statistical journals. No scientific awards or fellowships were documented in the available materials. While her publication record indicates significant research productivity, specific details regarding graduate student advising, research grants, or collaborative projects were not explicitly mentioned in the provided texts. Similarly, information about laboratory facilities or dedicated research teams remains undocumented in the current sources.

• Bayesian Statistics
• Statistical Modeling
• Functional Data Analysis
• +۴ مورد دیگر

Raffaele Argiento is a Full Professor of Statistics at the Department of Economics, University of Bergamo since September 2021. His academic career focuses on advanced statistical methodologies with applications across various domains including environmental science, public health, and data analysis. His research is prominently featured in high-impact statistical journals and conference proceedings. Argiento's research interests center around Bayesian statistical methods, particularly in functional data analysis, nonparametric Bayesian modeling, and clustering techniques. His work demonstrates expertise in developing innovative statistical approaches for complex data structures, including spatio-temporal data, categorical variables, and high-dimensional datasets. His research has significant applications in environmental monitoring (particularly air pollution analysis), public health (obesity rate modeling), and seismic monitoring through crowdsourced data. His methodological contributions include advancements in mixture models, partition models, and computational algorithms for statistical inference. His recent publication record shows a strong trend toward developing computationally efficient Bayesian methods for real-world applications. The research spans from theoretical developments in nonparametric Bayesian statistics to practical implementations for environmental monitoring, health data analysis, and functional data processing. His work demonstrates a consistent focus on bridging theoretical statistical advancements with practical applications across multiple scientific domains. Professor Argiento teaches several advanced statistical courses at the University of Bergamo, including Applied Statistical Modelling , Probability and Statistics , and Statistical Models for both undergraduate and graduate programs in Economics and Data Analysis. His teaching reflects his research expertise, emphasizing modern statistical methodologies and computational approaches.

• Medical Statistics
• Epidemiology
• Bayesian Network Analysis
• +۵ مورد دیگر

Mariaclelia Di Serio is a Full Professor of Medical Statistics (MEDS-24/A) at Vita-Salute San Raffaele University, Milan. She has held this permanent position since 2007 and served as Associate Professor at the same institution from 2005-2015. Since 2005, she has directed the University Centre for Statistics in Biomedical Sciences (CUSSB) at San Raffaele Science Park. International collaborations with institutions in Norway, Netherlands, Switzerland, Australia, Israel, and UK Executive Committee member of the International Biometric Society (2023-2026) Corresponding Member of Istituto Lombardo Accademia di Scienze e Lettere (2020-present) Her research focuses on Bayesian networks for complex disorders, longitudinal data modeling in oncology/virology, and bioinformatics for genomic analysis. She pioneered methods for competing risks dependence structures and Simpson paradox in survival analysis. Recent publications examine gene therapy safety , joint latent class models for clinical subgroups, and network-based approaches to healthcare data sharing. She contributes to statistical methodology for COVID-19 prognosis and platform trials in biomedical research. 2016: Awarded Nature Communications publication for HIV replication modeling 2015: Elected President of International Biometric Society (Italian region) As a tenured educator since 1998, she teaches Biostatistics at the Faculty of Medicine and contributes to multidisciplinary research in medical data science and public health .

Francesca Marta Lilja Di Lascioمشاهده پروفایل

دانشیار

• Copula-based stochastic models
• Clustering techniques for dependent data
• Statistical methods for spatial/time series data
• +۵ مورد دیگر

Francesca Marta Lilja Di Lascio is an Associate Professor of Statistics at the Faculty of Economics and Management, Free University of Bozen-Bolzano. Her research focuses on copula-based statistical methods for complex data analysis, including dependence modeling, clustering algorithms, and applications in environmental science, energy economics, and medical statistics. She obtained her PhD in Statistical Methodology for Scientific Research from the University of Bologna (2008) and holds a National Scientific Qualification for Full Professor (2023-2034). Her academic roles include Vice-director of the Bachelor in Economics and Social Sciences, Coordinator of the BEMPS working paper series, and member of the Quality Committee. She has organized over 20 international conferences and workshops on statistical methods and data science, including the 2022 Workshop on Statistical Learning and Econometrics. Research interests span copula-based clustering, spatial-temporal modeling, and imputation techniques for missing data. She has secured grants totaling over €3.5M, including the €2M 'Agritech National Research Center' (PNRR funded) and the €182k 'Techno-economic methodologies for sustainable energy scenarios' project. She develops statistical software packages like CoClust (copula-based clustering) and PanelTM (dynamic threshold models). Teaching includes graduate courses on multivariate statistical methods and PhD-level quantitative research methods. She has published 40+ peer-reviewed articles in journals like Risk Analysis , Computational Statistics & Data Analysis , and Environmental and Ecological Statistics . Key Awards: Full Professor Qualification (2023), Associate Professor Qualification (2017) Recent Projects: EXTReme Events in Mountain Environments (€120k), Detecting Effects of Experimental Therapies (€82k) Professional Memberships: Italian Statistical Society (SIS), International Biometric Society (IBS), International Statistical Institute (ISI)

Roberto Corizzoمشاهده پروفایل

پژوهشگر ارشد

• Big Data Analytics
• Data Mining
• Predictive Modeling
• +۳ مورد دیگر

Roberto Corizzo is a Research Fellow at the Department of Computer Science, University of Bari, Italy. He holds a Ph.D. and is affiliated with the LACAM Laboratory. His research focuses on big data analytics, data mining, predictive modeling for sensor networks, energy prediction in smart grids, and anomaly detection. He has conducted research internships at INESC TEC (Portugal) under Prof. João Gama and at the American University (Washington D.C.) under Prof. Nathalie Japkowicz. Key research interests include: Big Data Analytics Predictive Modeling for Sensor Networks Energy Prediction in Smart Grids Anomaly Detection in Dynamic Systems His work spans over 15 publications from 2014–2019, addressing challenges in data stream analysis, renewable energy forecasting, and distributed computing. Notable contributions include Spark-GHSOM for large-scale data clustering and DENCAST for multi-target regression. He has organized conference challenges (e.g., ECML/PKDD Discovery Challenges) and contributed to projects like VIPOC for renewable energy prediction. No scientific awards are explicitly listed. He collaborates with academic institutions globally and participates in interdisciplinary projects involving energy systems and semantic services in big data platforms.

• Combinatorics on Words
• Algorithms on Strings
• Automata Theory
• +۴ مورد دیگر

Marinella Sciortino is a Professor at the University of Palermo , affiliated with the School of Basic and Applied Sciences and the Department of Mathematics and Computer Science . She serves as Director of the CINI Research Unit and is a member of the Academic Senate (2022-2024). Sciortino contributes to editorial and scientific committees for journals and conferences, including Theoretical Computer Science and the GRIN Committee . Research focuses on: Combinatorics on Words String Algorithms Automata Theory Symbolic Dynamics Data Compression Biological Sequence Analysis Her work includes groundbreaking studies on the Burrows-Wheeler Transform (BWT) , extending its application to multiple sequences and improving compression efficiency. She has supervised PhD student Giuseppe Romana and participated in numerous conference program committees (e.g., SPIRE , ICTCS , CiE ). Recent scientific contributions (2022-2020): BWT teaching methodologies String attractors Variant discovery in genomics Algorithmic perspectives on alternating BWT Positional clustering for SNP detection Mental rotation effects on computational learning Teaching roles include Laboratorio di Algoritmi (Computer Science) and Teoria dell'Informazione e Compressione Dati (Master's in Computer Science). Contact: marinella.sciortino@unipa.it , +39-091-238-91080.

• Network Theory
• Statistical Physics
• Financial Complexity
• +۵ مورد دیگر

Diego Garlaschelli is a Full Professor of Theoretical Physics at the IMT School of Advanced Studies in Lucca (Italy) and leads the Networks research unit. He concurrently serves as a Professor at Leiden University’s Lorentz Institute for Theoretical Physics, leading the Econophysics and Network Theory group. He holds positions as an external faculty member at the Complexity Science Hub Vienna and an associate member at the Enrico Fermi Research Center (Rome). His research focuses on network theory, statistical physics, financial complexity, ecological systems, and information theory. Garlaschelli has authored over 100 peer-reviewed publications, including contributions to Nature , Science , and Physical Review Letters . He teaches courses on Network Theory and Complex Systems. Notable research includes structural analysis of financial networks, maximum-entropy network models, and systemic risk estimation. Education: M.Sc. in Theoretical Physics from University of Rome III (2001), Ph.D. in Physics from University of Siena (2005). Postdoctoral positions at Australian National University, University of Oxford, and Sapienza University of Rome. Research interests span interdisciplinary topics such as financial network reconstruction, ecological community dynamics, and mesoscopic organization of neural systems. His work bridges statistical physics with real-world network applications, emphasizing data-driven approaches and theoretical frameworks.

• Seismicity analysis
• Stress field
• Crustal structure
• +۶ مورد دیگر

Raffaella De Matteis is an Associate Professor in the Department of Science and Technology at the University of Sannio, Italy, specializing in Solid Earth Geophysics (GEO/10). Her research focuses on seismic tomography, crustal structure analysis, and earthquake source characterization, with particular emphasis on volcanic and geothermal regions in Italy. Her research interests span seismicity analysis, stress field determination, crustal structure using tomographic techniques, seismic wave propagation in heterogeneous media, seismic source parameters analysis, and ground motion predictive equations for early-warning applications. Dr. De Matteis has extensively studied Italian volcanic systems including Mt. Vesuvius, Ischia island, and Campi Flegrei caldera, as well as geothermal fields like Larderello in Tuscany and Nesjavellir in Iceland. Her publication record reveals strong expertise in 3D seismic tomography, with methodological contributions to velocity model development, earthquake relocation techniques, and ground-shaking mapping. Her work often addresses the practical applications of seismic research for hazard assessment and early warning systems, with a particular focus on how advanced tomographic methods improve our understanding of seismic hazards in complex geological settings. Dr. De Matteis has collaborated extensively with researchers across Italy and internationally, particularly on projects involving seismic monitoring of volcanic and geothermal areas. Her recent work has increasingly focused on the relationship between pore fluid pressure and earthquake triggering mechanisms, reflecting a growing interest in the connections between geothermal systems and seismic activity.

• 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.

Alessandro Casaمشاهده پروفایل

دانشیار

• Mixture Models
• Modal Clustering
• Graphical Models
• +۵ مورد دیگر

Alessandro Casa is an Associate Professor of Statistics at the Faculty of Economics and Management, Free University of Bozen-Bolzano (Italy). His research focuses on developing statistical methodologies for high-dimensional and complex data, including latent variable models, density-based clustering, and applications in spectroscopy. He holds a PhD in Statistics from the University of Padova (2016-2020), preceded by master's and bachelor's degrees in Statistical Sciences and Statistics, Economics, and Finance, both from the University of Padova. Education: PhD in Statistics, University of Padova (2016-2020) Master Degree in Statistical Sciences, University of Padova (2014-2016) Bachelor Degree in Statistics, Economics, and Finance, University of Padova (2011-2014) Research Interests: Mixture models, modal clustering, graphical models, variable selection, latent variable modeling, high-dimensional data inference, computational statistics, and chemometrics. His work often addresses challenges in spectroscopic data analysis and applications in agriculture and physics. Teaching: Statistical Methods for Business Analysis (MSc programs) Research Methods and Experimental Design (Tourism Management) Statistics for Tourism, Sport, and Event Management (BSc) Professional Experience: Associate Professor (2024–Present), Free University of Bozen-Bolzano Senior Assistant Professor (2024), University of Bergamo Postdoctoral Research Fellow (2019–2021), University College Dublin Labs/Teams: Collaborations with Vistamilk SFI Research Centre and Insight SFI Research Center during his postdoctoral tenure. Active in model-based clustering research communities, contributing to conferences and workshops globally.

• Molecular Dynamics
• Condensed Matter Physics
• Biophysics
• +۳ مورد دیگر

Raffaello Potestio is an Associate Professor in the Department of Physics at the University of Trento and serves as Coordinator of the Doctoral Course in Physics. His research focuses on molecular dynamics, soft matter physics, and computational modeling of biological systems. He teaches courses such as Physics 2 (electromagnetism fundamentals), Multi-scale Methods in Soft Matter Physics, and Statistical Mechanics, emphasizing theoretical and computational approaches. His work integrates advanced simulation techniques, including adaptive resolution methods, to study complex systems like proteins, DNA, and viral capsids. He has contributed to tools like EXCOGITO for coarse-grained modeling and explored topics ranging from knot interactions in polymers to chromatin mechanics in diseases. His research bridges theoretical physics with applications in biophysics, materials science, and epidemiology. Education details are not explicitly provided in the text. His teaching activities span undergraduate and graduate levels, with a focus on equipping students with analytical and computational skills for modern physics challenges. His publications highlight contributions to multiscale modeling frameworks, protein dynamics, and data-driven analysis of mobility patterns during the pandemic. Collaborations involve interdisciplinary teams addressing both fundamental and applied questions in physics and biology. Labs and teams associated with his work include computational physics groups at the University of Trento, though specific lab names are not mentioned. His research emphasizes the development of novel algorithms and their application to diverse systems, reflecting a commitment to advancing both methodological and applied aspects of theoretical and computational physics.

• Multivariate Statistics
• Data Analysis
• Clustering Algorithms
• +۴ مورد دیگر

Laura Bocci has been an Associate Professor of Statistics at Sapienza University of Rome since 2022. Previously, she worked as a researcher at Sapienza University from 1998–2000 in the Department of Communication and Social Research, later moving to the Department of Social and Economic Sciences in 2020. She earned her PhD in Methodological Statistics (XII Cycle, 2000) from Sapienza University, where she also graduated with honors in Statistical and Economic Sciences. Current Department: Department of Social and Economic Sciences Faculty: Faculty of Political Science, Sociology, Communication Office Location: Room 107, Building CU002 (Political Sciences), Sapienza University Campus Teaching: Statistics (A-K) and Data Science for Social Research Her research focuses on Multivariate Statistics , Data Analysis , and Statistical Methods with applications in social sciences, economics, and data clustering. She has developed novel techniques in dimensional reduction , model-based clustering , unsupervised classification , multidimensional scaling , and genetic algorithms . Her work often addresses complex data structures , including three-way data and multilevel models. Publications highlight trends in regional competitiveness , well-being analysis , clustering methodologies , and genetic algorithm applications . Key subfields include three-way data analysis , spatial clustering , and categorical variable optimization . Office hours are held Tuesdays 14:00–16:00 in person or via Google Meet (by appointment). She teaches courses in Statistics and Data Science for Social Research using Sapienza’s e-learning platform.