جستجوی استاد راهنمای دانشگاهی

• fuzzy clustering
• time series clustering
• clustering methods for complex data structures
• +۶ مورد دیگر

Pierpaolo D'Urso is a Full Professor of Statistics at Sapienza University of Rome, where he serves as Dean of the Faculty of Political Science, Sociology, Communication. He is Director of the Master's Program in Data Science for Public Administration and holds the Department of Social and Economic Sciences. His academic career includes serving as Director of the Department of Social and Economic Sciences, Vice Rector for Staff Training, and Member of the Academic Senate at Sapienza University. His primary research interests focus on advanced statistical methodologies, particularly fuzzy clustering , time series clustering , and clustering methods for complex data structures . He has extended these methods to diverse applications including statistics and democracy, electoral systems analysis, sports analytics, and public sector management. His work bridges theoretical statistical development with practical applications in social sciences and policy-making. His recent publications demonstrate a strong focus on spatial regularization techniques, entropy-based approaches, and novel applications of fuzzy clustering to interval-valued data and network structures. The research spans multiple disciplines including statistics, data science, economics, and political science, reflecting his interdisciplinary approach to statistical methodology. D'Urso has been recognized with inclusion in Stanford University's World's Top 2% Scientists ranking. He serves as Associate Editor and reviewer for numerous international journals in Statistics and Data Science, contributing significantly to the scholarly community through peer review and editorial work. At the institutional level, he has held significant administrative roles including Director of the Master in Data Science for Public Administration, demonstrating his commitment to developing statistical education for public sector applications. His work with government entities, particularly as a member of expert committees at the Presidency of the Council of Ministers, highlights the practical impact of his research on policy and governance.

• Multivariate Data Analysis
• Tensor Analysis
• Compositional Analysis
• +۲ مورد دیگر

Michele Gallo is a Full Professor of Statistics at the Department of Human and Social Sciences , University of Naples L'Orientale, where he has taught statistical methods to master's students since 2020. His academic career includes roles as Associate Professor (2005-2011) and Researcher in Statistics. He currently serves on the Board of Directors at his university and the Steering Committee of the Italian Statistical Society. His research focuses on Multivariate Data Analysis , Tensor Analysis , Compositional Analysis , and Rasch Models , with applications in quality management and social sciences. Recent publications address robust multiway data analysis, sustainable sports metrics, and statistical software development in R. Key trends in his 15 most recent articles (2023-2025) include: Advancements in tensor decomposition methods for multidimensional data Applications of compositional data analysis in sustainability research Development of open-source statistical software packages Integration of machine learning with classical statistical techniques Evaluation frameworks for service quality and innovation

Giovanni Trappoliniمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Geometric Deep Learning
• +۹ مورد دیگر

Giovanni Trappolini is an Assistant Professor in the Department of Computer, Control, and Management Engineering at Sapienza University of Rome, where he conducts research in the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He earned his PhD in Machine Learning from Sapienza in 2022, focusing on Geometric Deep Learning under Prof. Emanuele Rodolà. Trappolini has collaborated with leading institutions such as Stanford, Technion, Meta, Amazon, TII, and the University of Pisa, contributing to cutting-edge research in AI and information retrieval. His research interests include machine learning, deep learning, geometric deep learning, multimodal AI, information retrieval, and large language models. He has pioneered work on Retrieval-Augmented Generation (RAG), Neural Databases, and the development of Fauno, the leading Italian LLM. His teaching experience includes courses on Python for Data Science, Advanced Data Mining, and Statistical Learning at both Sapienza and Luiss Guido Carli. Trappolini's recent publications span top-tier venues like SIGIR, NeurIPS, ECCV, and IEEE Transactions, with a focus on graph neural networks, federated learning, anomaly detection in 5G networks, and multimodal systems. His work shows a strong trend toward robust, privacy-preserving, and multimodal AI systems with real-world applications in language, vision, and network security. Sapienza honor graduate IELTS 8.0 He has advised several teaching roles and delivered courses on Python, data science, and statistical learning. His research is supported through collaborations with industry leaders like Meta and Amazon, and he leads innovative projects such as Fauno and Neural Databases. Trappolini is actively involved in the AI research community and continues to push the boundaries of language and retrieval systems. He is a core member of the RSTLess Lab and has been instrumental in advancing geometric and multimodal deep learning at Sapienza University. His work bridges theoretical innovation with practical implementation, particularly in the domains of Italian language modeling and secure, efficient information retrieval systems.

• 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.

• Statistics
• Econometrics
• Biostatistics
• +۷ مورد دیگر

Professor Alessio Farcomeni is a distinguished statistician at the University of Rome Tor Vergata, affiliated with the Department of Economics and Finance. His work spans statistical methodology, biostatistics, and applied econometrics. He teaches advanced courses such as Statistical Learning and Big Data Analysis for Economics and Finance , reflecting his expertise in modern data science. His research focuses on latent Markov models, quantile regression, and population size estimation, with applications in healthcare, epidemiology, and policy analysis. Farcomeni has contributed significantly to statistical methodology through publications in high-impact journals. Notable projects include modeling the Italian response to the Covid-19 pandemic, analyzing atrial fibrillation risk prediction, and developing Bayesian approaches for complex datasets. His work often bridges theoretical statistics with real-world challenges, such as optimizing treatment protocols for cancer patients and evaluating macroprudential policies. He maintains active collaborations with institutions like the Italian National Health Service and has advised on international studies, including the CALIPSO transplant infection project. His research has addressed critical issues like material deprivation measurement in Europe and the prognosis of metastatic prostate cancer. Farcomeni is recognized for pioneering methods in latent variable modeling and robust statistical inference. His teaching and mentorship are complemented by software development contributions, such as the Qtools package for quantile regression analysis. Despite prolific output, he emphasizes interdisciplinary collaboration, evidenced by his work in cardiology, oncology, and public health.

Floriana Espositoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Programming Languages
• +۳ مورد دیگر

Floriana Esposito is a Full Professor and current Director of the Department of Computer Science at the University of Bari, Italy. With a career spanning over 50 years since her 1974 appointment, she has held pivotal leadership roles including Dean of the Faculty of Computer Science (1997-2002) and Director of the Interdepartmental Center for Logic and Applications (1997-2001). She teaches core courses including Algorithms and Data Structures, Knowledge Engineering, and Machine Learning. Education: Degree in Electronic Physics from University of Bari (1970) First Academic Appointment at University of Bari (1974) Research Focus: Her work centers on Artificial Intelligence and Machine Learning with emphasis on logical/algebraic foundations. Initial research in statistical pattern recognition evolved into Intelligent Tutoring Systems and Machine Learning, now focusing on similarity-based learning, multistrategy approaches, and integration of symbolic/numeric methods. Major applications target Document Analysis and Understanding for paper-based systems, with significant contributions to symbolic data analysis and knowledge discovery. Publication Trends: Recent work shows strong continuity in multistrategy and incremental learning techniques applied to document processing and symbolic data analysis. Her research consistently bridges theoretical foundations (logical theories, concept drift handling) with practical implementations in spatial data mining, e-learning systems, and urban planning, demonstrating a 30-year trajectory from knowledge acquisition fundamentals to real-world AI deployment. Scientific Awards: No specific awards were mentioned in the source text. Advising & Grants: As founder of LACAM Laboratory (1986), she has mentored numerous PhD students and researchers. Her group secured extensive EU funding through ESPRIT, IST, and Framework programs including MLNet, KDNET, and DELOS Networks of Excellence, focusing on machine learning applications in document analysis, digital libraries, and spatial data mining across 15+ European collaborative projects. Labs & Teams: She chairs LACAM Laboratory (2 full professors, 3 associate professors, 5 assistant professors, multiple PhD students), which serves as a European hub for AI research. The lab has been integral to ESPRIT Intrepid (document processing), CONCERTO (digital document retrieval), COLLATE (historical archive systems), and SODAS (symbolic data analysis), maintaining continuous involvement in EU Networks of Excellence since the 1990s.

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.

Federico Mariniمشاهده پروفایل

استاد

• Chemometrics
• Analytical Chemistry
• Food Chemistry
• +۳ مورد دیگر

Federico Marini is a Full Professor at the Department of Chemistry, University of Rome La Sapienza. He holds editorial roles including Editor-in-Chief of *Chemometrics and Intelligent Laboratory Systems* and membership in multiple journal boards. His research focuses on chemometrics, particularly method development and application in food chemistry, metabolomics, and multi-block data analysis. He has authored over 300 publications and edited books like *Chemometrics in Food Chemistry* (Elsevier 2013). Education: PhD in Analytical Chemistry (University of Rome La Sapienza, 2004) with Marie Curie Fellowship (2000–2004). Postdoctoral roles included visiting positions at universities in Copenhagen, Stellenbosch, and Lille. Research interests span advanced chemometric methods for food authentication, omics data processing, and non-linear modeling. Notable contributions include SO-PLS(-LDA) algorithms and multi-block fusion strategies. He coordinates the Chemometrics Task Group of DAC-EuCheMS and serves as Vice President of the Italian Society of NIR Spectroscopy. Awards include the 2006 Young Researcher Award (Italian Chemical Society) and 2012 Chemometrics Award. His work bridges analytical chemistry with data science, addressing real-world problems in agriculture, food safety, and biomedicine through innovative spectroscopic and statistical techniques.

FRANCESCO MARIA SANNAمشاهده پروفایل

استاد

• Econometrics
• Territorial Analysis
• Statistical Methodology
• +۵ مورد دیگر

Francesco Maria Sanna is a Professor at the University of Rome 'La Sapienza', affiliated with the Faculty of Economics and the Department of Methods and Models for Economics, Territory and Finance. With a career spanning over five decades, he has held roles including Full Professor of Statistics (2004–2018), Vice Dean of the Faculty of Economics (2007–2018), and Delegate for Teaching at the University level. His research focuses on statistical methodology, territorial analysis, demography, and environmental economics, with notable contributions to urban demography, energy policy, and regional development. Teaching Activities: Basic Statistics course (academic years 2018–present) Visiting Professor of Economic Theory at the Pontifical Gregorian University (1992–2018) Contributions to master's programs in Tourism and Environmental Resources Management Research Interests: Statistical analysis of territorial data Urban and coastal region studies Energy and economic activity interdependencies Historical socioeconomic trends Public policy evaluation frameworks Publications highlight exploratory data analysis, energy policy debates, and urban evolution studies. He actively participates in academic governance roles, including membership in the National University Council and leadership positions in university commissions. His work bridges statistical theory with practical applications in regional development and public administration.

Giovanna Maria Dimitriمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۷ مورد دیگر

Giovanna Maria Dimitri is a tenure-track Assistant Professor in Artificial Intelligence at the Università degli Studi di Milano (Statale), with strong affiliations at the University of Siena (DIISM) and the University of Cambridge. She conducts research in deep learning, machine learning, and AI applications across domains such as computer vision, bioinformatics, climate modeling, and healthcare. She also lectures in Business Intelligence at the University of Siena and serves as a Guest Lecturer in Data Science at the University of Cambridge's Institute of Continuing Education. PhD in Artificial Intelligence, University of Cambridge MPhil in Advanced Computer Science (Distinction), University of Cambridge Master's and Bachelor's in Computer and Automation Engineering (110/110 cum laude), University of Siena Her research spans foundational AI models and their applications in diverse fields including neuroscience, environmental science, and medical diagnostics. She develops methodologies in deep learning, graph neural networks, and multimodal data analysis, with a growing interest in sustainable AI and ethical implications of machine learning systems. Her recent publications reflect a strong trend in applying AI to real-world challenges: from detecting synthetic images and analyzing brain signals to modeling climate impacts and assessing sustainable development goals. Her work bridges theoretical innovation with practical implementation across healthcare, environmental monitoring, and digital humanities. Scientific Awards and Editorial Roles: Ai-Net Fellows Scholarship (DAAD, 2023) Associate Editor, Neurocomputing (Elsevier) Associate Editor, IEEE Transactions on Technology and Society (since May 2024) Giovanna has extensive experience in teaching, supervising, and science communication. She has delivered seminars at top institutions like the University of Cambridge and has been featured in Italian media for her AI expertise. She mentors students and collaborates internationally, including with Prof. Gemma Roig at the DAAD-sponsored Ai-Net program. She is actively involved in academic service, conference organizing, and public engagement, contributing to both technical and societal aspects of AI advancement. She is a life member of Clare Hall College, University of Cambridge, and maintains active research collaborations across Europe. Her lab and research group focus on developing robust, interpretable, and sustainable AI systems, with ongoing projects in medical AI, climate informatics, and ethical machine learning.

• Nonlinear PDEs
• SPDEs
• Shell Models (Dyadic, GOY)
• +۱۰ مورد دیگر

Francesco Morandin is a Researcher (Assistant Professor) in Probability and Statistics at the Department of Mathematical, Physical and Computer Sciences, University of Parma. His research spans theoretical and applied mathematics, focusing on nonlinear PDEs, SPDEs, and stochastic processes like branching and generalized urn models, with applications in medical data analysis, industrial statistics, and computational biology. Education: Mathematics degree from Scuola Normale Superiore di Pisa (1998), PhD program (1999-2000). Collaborations: University of Pisa (microarray analysis), Avio S.p.A. (mechanical failure detection), Parma burial office (cremation dynamics), Heinz-Plasmon (quality control). His recent work in machine learning includes SwitchPath activation functions for exploration enhancement, GloNets for global connectivity, and reinforcement learning applications in healthcare and game theory. He has supervised 4 bachelor's, 13 master's, and co-supervised 15 theses, with a focus on industrial and medical statistics education. His publications integrate probability theory with applications in cancer therapy prediction, single-cell RNA analysis, and stochastic modeling of turbulence.

• Lean Manufacturing
• Supply Chain Management
• Discrete Event Simulation
• +۳ مورد دیگر

Francesco Zammori is an Associate Professor at the Department of Systems Engineering and Industrial Technologies (DISTI) , University of Parma. His career spans academic research, teaching, and industry collaboration, focusing on lean manufacturing, supply chain innovation, and simulation modeling. Scientific High School Diploma, 100/100, Leonardo da Vinci Scientific High School (2000) Bachelor's in Management Engineering (logistics and production), 110/110 cum laude , University of Pisa (2002) Master's in Management Engineering (logistics and production), 110/110 cum laude , University of Pisa (2004) PhD in Mechanical Engineering (plants and technologies), University of Pisa (2009) Zammori's research centers on lean manufacturing , where he develops techniques for identifying inefficiencies using Value Stream Mapping (VSM) and Overall Equipment Effectiveness (OEE). He specializes in Vendor Managed Inventory (VMI) modeling, discrete-event simulation of complex systems with multivariate statistical analysis, and solving NP-hard problems in operations management using mathematical programming and heuristics. His work also explores process mapping with IDEF/BPMN methods and IT system prototyping for transactional data. Recent publications highlight trends in supply chain circularity (2024), automated storage systems (2023), and educational gamification (2023). He applies Monte Carlo simulation , Markov chains , and life cycle analysis to industrial challenges. Scientific Contributions: Editor-in-Chief (European area), Decision Science Letters (Growing Science) Organizer & Chair, IESM2015 special session on 'Lean in Make To Order Firms' Member, AIIE2015 international conference scientific committee Zammori has taught 40+ corporate training modules for Tuscan companies and delivered university courses on ERP systems , business process management , and management information systems across multiple Italian universities since 2005. His funded projects include RFID tracking systems for paper coils and plasma bags, extended enterprise logistics frameworks, and lean process reengineering for manufacturing firms.

• Control Engineering
• Biomedical Engineering
• Pharmacokinetics/Pharmacodynamics (PK/PD)
• +۵ مورد دیگر

Mattia Laurini is a Fixed-term Researcher at the Department of Engineering and Architecture , University of Parma. His academic work focuses on control engineering, biomedical applications, and optimization algorithms, with teaching assignments in Computer Engineering for Master's Degree programs since 2023. Research Interests: Control systems for anesthesia Pharmacokinetics/Pharmacodynamics (PK/PD) modeling Traffic flow estimation Dynamic programming applications Vehicle motion optimization Medical device automation Research Trends: Recent publications (2025) demonstrate interdisciplinary work merging control theory with biomedical applications, particularly in automating anesthesia delivery. Key methodologies include Branch and Bound optimization, dynamic programming for traffic systems, and hybrid control paradigms combining MPC with PID strategies. Research spans both theoretical advances (e.g., acceleration-constrained shortest path algorithms) and clinical applications (e.g., propofol/remifentanil co-administration optimization). Teaching: Laboratory of Control Engineering (2023-2025) Multivariable Systems (2023-2025) Reference teacher for Computer Engineering program Contact: mattia.laurini@unipr.it | Office: Building 1, Parco Area delle Scienze, 181/A 43124 Parma | Phone: 906183

• Statistics
• Multivariate Analysis
• Correspondence Analysis
• +۷ مورد دیگر

Biagio Simonetti is an Associate Professor in Statistics (SECS-S/01) at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of the University of Sannio, Italy. He has been serving in this role since 2015, following a progression from Researcher since 2006. His academic journey began with a PhD in Computational Statistics from the University of Naples Federico II in 2003. His research interests center on applied statistics , particularly in multivariate analysis , correspondence analysis , fuzzy systems , cluster analysis , and ordinal data modeling . His work extends into interdisciplinary domains such as healthcare economics, customer satisfaction, tourism, and financial markets. He has applied statistical techniques to evaluate obesity’s impact on healthcare costs, model doctor-patient communication, and analyze cultural perceptions of food. The trend in his recent publications reflects a strong focus on fuzzy logic , big data applications , and robust statistical methods in socio-economic and health contexts. His work often involves international collaboration, particularly with researchers in Spain, Australia, India, and Turkey. Certificate of Appreciation for collaboration with Hanoi University (Vietnam), 2014 Simonetti has participated in numerous research projects, including European TEMPUS and Jean Monnet programs, and has coordinated EU-funded research on grapevine virology and applied statistics education. He has served on the editorial boards of journals such as the Journal of Reliability and Statistical Studies and Electronic Journal of Applied Statistical Analysis . He has also been actively involved in organizing and serving on scientific committees for international conferences like DYSES, MTISD, and Agrostat. He has collaborated extensively with institutions abroad, including the University of Moncton (Canada), University of Western Sydney (Australia), and Universitat Rovira i Virgili (Spain), and has held research visits in Denmark and Barbados. His academic service includes participation in PhD programs and doctoral examination committees, particularly in biostatistics and socio-economic systems.

• Nuclear Magnetic Resonance (NMR) Spectroscopy
• Metabolomics
• Chemical Ecology
• +۵ مورد دیگر

Adele MUCCI is an Associate Professor at the Department of Chemical and Geological Sciences , University of Modena and Reggio Emilia. Her research spans multiple domains including: NMR spectroscopy in biomedical and environmental contexts Metabolomics for disease diagnosis and biomarker discovery Green materials synthesis and characterization Chiral catalysts for oxygen evolution reactions Key research areas: She leads studies on solid-state and solution NMR for structural analysis of complex systems, metabolomic profiling of oral diseases and autoimmune conditions, and functionalization of montmorillonite for environmental remediation. Her work on chiral metal-organic frameworks explores spin-selective catalysis without external electromagnetic fields. Publications & Collaborations: Recent 2025 articles focus on salivary diagnostics , structural isomerism in metal complexes , and green ammonia capture materials . Collaborators include researchers from the CNR Institute of Molecular Science and international environmental chemistry groups. Technical Expertise: MUCCI employs HR-MAS NMR , DFT calculations , X-ray diffraction , and ESI-MS across her projects. She has contributed to biopolymer functionalization and graphite-epoxy composite optimization for fuel cell bipolar plates.