Find Academic Supervisors

• Nuclear Magnetic Resonance (NMR) Spectroscopy
• Metabolomics
• Chemical Ecology
• +5 more

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.

Matilde BiniView profile

Professor

• Statistical Models
• Econometrics
• Multivariate Analysis
• +5 more

Matilde Bini is a Professor of Economic Statistics (SECS-S/03) and Director of the Department of Human Sciences at the University of European Rome (UER). She holds a PhD in Applied Statistics from the University of Florence (1995) and has taught Corporate Statistics and Statistical Methods for Business Decisions at UER since 2009. Visiting positions: University of Florida (USA), IMT Lucca, University of Cordova (Spain) Active in national research groups funded by the Italian Ministry of Research Editorial roles: Associate Editor for Statistical Methods and Applications and Italian Journal of Applied Statistics Her research focuses on generalized linear mixed models, structural equation models, multivariate methods, and longitudinal models. She specializes in robust diagnostic analysis for data quality and anomaly detection, with applications in labor markets, firm productivity, entrepreneurship, and higher education evaluation. Recent publications examine zero-inflated beta regression for university career prediction, multilevel modeling of educational effectiveness, and robust analysis of high-tech firm competitiveness. Her work frequently appears in peer-reviewed international statistics journals. Professional affiliations include the Italian Statistical Society, International Statistical Institute, American Statistical Association, Royal Statistical Society, and the International Society for Business and Industrial Statistics.

• Actuarial Science
• Machine Learning
• Financial Time Series Analysis
• +4 more

Maria Carannante is a Lecturer at the European University of Rome's Department of Human Science, specializing in Mathematical Methods for Economics, Actuarial Sciences, and Finance (STAT-04). She maintains active membership in The Association for Mathematics Applied to Social and Economic Sciences (AMASES). Her research program centers on developing advanced quantitative methods for real-world applications. Primary domains include: Machine learning implementations for longitudinal data analysis Stochastic modeling of mortality and longevity risks Climate risk quantification in insurance contexts Multidimensional analysis of financial time series Big data processing frameworks for economic systems Publication analysis reveals consistent focus on actuarial innovations, with recent emphasis on: Machine learning applications in insurance risk transfer Pandemic mortality modeling and insurance implications ESG integration in actuarial pricing Advanced dependency modeling using copulas Longevity risk solutions for pension systems

Franco PellereyView profile

Professor

• Actuarial Sciences
• Dependence Notions and Orders
• Reliability Theory
• +6 more

Franco Pellerey is a Full Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin. He serves as Coordinator of the College of Mathematical Engineering and is actively involved in teaching and research coordination activities. Co-organizer of the International School on Stochastic Comparisons (2004) Program Chair for the 14th International Conference on Ordered Statistical Data (2022) Scientific Coordinator for multiple national research projects (BRIC-2024, PRIN) Research Interests : Stochastic orders and their applications in risk theory Dependence notions and positive dependence concepts Reliability theory and actuarial sciences Stochastic processes in engineering contexts Applied statistics for technical systems Scientific Leadership : BRIC-2024 Acoustics Research (2025-2027) Supermodularity in Economic Theory (2002-2004) Continuous Financial Market Models (2001-2003) International Engagements : Full Professor at Turin Polytechnic University in Tashkent (2018-2021) Full Professor at ESCP Europe (2016-2018)

• Operations Research
• Mathematical Programming
• Management Science
• +2 more

Edoardo Fadda is a Fixed-term tenure-track Assistant Professor at the Department of Mathematical Sciences (DISMA), Politecnico di Torino . He serves as a member of the College of Mathematical Engineering and College of Electronic, Telecommunications and Physics Engineering . Specializes in Operations Research and Mathematical Programming Active in stochastic optimization , reinforcement learning , and control applications Teaching roles include Optimization Methods for Control Applications and Stochastic Programming courses His research spans supply chain optimization , logistics , and AI-integrated decision systems , focusing on uncertainty modeling and multi-stage stochastic programming. He leads the Development of Decision Support Systems and the SUPERSONIC project for ecological logistics, alongside commercial consulting for RIDIX SPA through Fondimpresa contracts. Notable collaborations include Paolo Brandimarte and Francesca Maggioni . Edoardo supervises PhD candidates Alessia De Crescenzo (39th cycle) and Lorenzo Mazza (40th cycle). His publications emphasize stochastic customer behavior , perishable product policies , and kernel-based system identification , with applications in aerospace, smart cities, and industrial manufacturing.

• Batteries
• Biosourced polymers
• Design of experiments
• +7 more

Federico Bella is Full Professor of Chemical Foundations of Technologies at Polytechnic University of Turin, leading research on post-Li batteries , electrochemical nitrogen reduction , and hybrid photovoltaics within the Electrochemistry Group. He serves as Member of the Interdepartmental Center Ec-L - Energy Center Lab and oversees multiple laboratories including the Testing Laboratory and Material synthesis units. Department: Department of Applied Science and Technology (DISAT) Scientific Sector: CHEM-06/A - Chemical Foundations of Technologies ERC Skills: Electrochemistry, Chemical Engineering, Intelligent Materials His research focuses on sustainable energy systems, including green ammonia production , polymer electrolytes , and next-generation batteries . He has led over 20 major research projects, including the ERC SuN2rise (€1.5M) redesigning ammonia production and GREEN2MOVE for sustainable potassium battery manufacturing. His work aligns with SDG 7 (Affordable Clean Energy) and SDG 9 (Industry Innovation). Scientific recognitions include the ENI Award (2016), USERN Prize (2021), and European Young Chemist Award (2016). He serves on editorial boards of ChemSusChem and ACS Sustainable Chemistry & Engineering , and has coordinated the Italian Chemical Society's Early-Career Division. PhD Students Supervised: Antonio Benigno, Andrea Muscatello, Lorenzo Tamboia Key Collaborations: Royal Society of Chemistry, EuCheMS, ENI He teaches advanced courses on Research Design , Battery Systems , and Scientific Publishing across Materials Science, Chemical Engineering, and Management programs.

• Biopolymers
• Natural fillers for polymers
• Polymer processing
• +5 more

Alberto Frache is a Full Professor at the Department of Applied Science and Technology (DISAT) of Politecnico di Torino. He serves as Scientific Coordinator for numerous research projects and teaches courses on polymer technology, degradation, and recycling at the Master's program in Materials Engineering for Industry 4.0. Scientific Director for collaborations with hospitals and industry partners Member of scientific committees for Italian polymer associations Research Focus His research addresses: Development of biopolymers with natural fillers Polymer processing optimization for 3D printing Flame retardant systems and fire safety Recycling of automotive and packaging plastics Nanocomposite materials with graphene-based additives The 15 most recent publications demonstrate expertise in polymer recycling, nanocomposite processing, and fire-retardant material development with applications in: 3D printing Automotive components WEEE recycling Biodegradable materials Scientific Recognition Recipient of Polymers 2024 Best Paper Award Scientific coordinator for EU and regional projects Advisory roles for industrial innovation networks Academic Leadership Mentor for PhD students in Materials Science Principal Investigator for 20+ funded research projects Director of ALL-Polymer research team at DISAT Laboratory Facilities Oversees multiple specialized laboratories at DISAT including: Fire Testing Laboratory Plastic Processing Laboratory Polymeric Characterization Surface Treatments Chemical Synthesis

Antonio PunzoView profile

Professor

• Mixture Models
• Hidden Markov Models
• Model-Based Clustering
• +7 more

Antonio Punzo is a Full Professor of Statistics at the Department of Economics and Business, University of Catania, Italy. He holds a Ph.D. in Methodological and Applied Statistics from the University of Milano-Bicocca (2008) and has held academic positions including Assistant Professor (2010-2015) and Associate Professor (2015-2018) at the University of Catania. He has also served as a Visiting Professor at the University of Guelph, Canada (2013). His research focuses on mixture models, hidden Markov models, model-based clustering, and statistical methodologies for financial and insurance data. Punzo has developed R packages like ContaminatedMixt and flexCWM , advancing computational statistics. His work emphasizes robust statistical techniques and applications to real-world problems in economics, finance, and environmental science. Education: Ph.D. in Methodological and Applied Statistics, University of Milano-Bicocca (2008) Laurea (cum laude) in Statistical and Actuarial Sciences, University of Calabria (2004) Research Interests: Punzo specializes in advanced statistical modeling, including mixture models, hidden Markov models, and robust clustering techniques. His recent work addresses heavy-tailed distributions, serial dependence, and applications in insurance risk, economic data analysis, and environmental modeling. He also explores computational methods for high-dimensional data and develops software tools for statistical analysis. Publications: Punzo has authored/co-authored over 80 peer-reviewed articles in top journals like the Journal of the Royal Statistical Society , Journal of Classification , and Insurance: Mathematics and Economics . His research frequently addresses methodological innovations and practical applications in finance, economics, and biometry. Awards & Recognition: While no specific awards are listed, his prolific publication record and contributions to statistical software highlight his academic impact. He is a key figure in the cluster-weighted modeling community and has organized workshops on statistical methodologies. Teaching: Punzo teaches courses on statistical models for economics and finance, finite mixture models, and general statistics at the undergraduate and graduate levels at the University of Catania and the University of Messina. Collaborations: He collaborates with researchers globally, including Paul D. McNicholas (University of Guelph) and Salvatore Ingrassia (University of Catania), on projects in mixture modeling and robust statistics.

• Shape analysis
• Permutation tests
• Multivariate statistics
• +4 more

Brombin Chiara is an Associate Professor in Statistics at the Faculty of Psychology, Vita-Salute San Raffaele University, and an active researcher at the CUSSB (University Centre for Statistics in the Biomedical Sciences). Her academic journey includes a PhD in Statistical Sciences from the University of Padova (2009) and prior roles as a Researcher and Assistant Professor. She specializes in statistical methodologies with applications in biomedical research, focusing on shape analysis, permutation tests, and multivariate statistics. Education: PhD in Statistical Sciences (2006–2009), University of Padova Laurea Degree in Statistics (2000–2005), University of Padova Research Interests: Development and application of nonparametric permutation methods Statistical modeling in longitudinal studies and survival analysis Integration of clinical and biosignal data for disease subgroup identification Teaching: Advanced statistical courses for psychology and biomedical students Course leader for Advanced Modelling in Psychology (Master’s program) Awards: 2012: Futuro in Ricerca grant (MIUR) for interdisciplinary research on emotion interpretation Collaborations: Member of doctoral colleges in Cognitive and Behavioral Sciences Public engagement initiatives like the SatisFace study on youth self-image

Stefano PelusoView profile

Associate Professor

• Bayesian Statistics
• Network Analysis
• Spatio-temporal Modeling
• +3 more

Stefano Peluso is an Associate Professor in the Department of Statistics and Quantitative Methods at the University of Milano-Bicocca. His academic appointments include: Current: Associate Professor, University of Milano-Bicocca Former: Assistant Professor, Catholic University of the Sacred Heart (2015-2020) Peluso's research centers on Bayesian statistics and network analysis with applications in epidemiology and biostatistics. He develops advanced spatio-temporal models for public health crises, specializing in dependency structures in mixed-type data and longitudinal networks. His methodological innovations bridge machine learning and statistical inference to address pandemic forecasting and cardiovascular event prediction, supporting evidence-based healthcare decision-making. His 2024 publications reveal a cohesive research trajectory focused on Bayesian network structures for dependency learning and spatio-temporal prediction. Five articles demonstrate consistent application of these methods to real-world health challenges—from COVID-19 forecasting using neural networks to cardiovascular event modeling—highlighting an interdisciplinary approach that transforms complex health data into actionable public health insights during emergencies. Peluso leads significant research initiatives funded by the Lombardy Region and Italian Ministry of University and Research, including the 'InPreSa' project for SARS-CoV-2 detection and Bayesian network models for pandemic risk forecasting. These projects reflect his commitment to deploying statistical science against societal threats, though no student advising details appear in the available records.

• Food Science
• Sensory Analysis
• Statistical Modeling
• +3 more

Michele Ricci is a Research Fellow at the University of Gastronomic Sciences (UNISG) in Pollenzo, Italy. His work focuses on interdisciplinary research in the areas of Perception and Quality, with specific expertise in Food Science, Statistics, and Sensory Analysis. He holds a Ph.D. from the University of Trento (2023), where his research involved statistical approaches for quality control in Italian D.O.P. cheeses, data analysis, and sensory laboratory support. He has developed skills in R and Python for analytical solutions, including image analysis and multivariate datasets of volatile organic compounds. Education: Bachelor’s in Food Science (2016) – University of Florence (thesis on B-group vitamins estimation). Master’s in Food Science and Technology (2019) – University of Bologna (thesis on microwave probes for salami water content). Ph.D. in C3A Department, University of Trento (2023) – Collaborative project with Edmund Mach Foundation (FEM). Ricci’s research emphasizes bridging sensory analysis with statistical tools, including the development of an R package for dynamic sensory data analysis. His current projects at UNISG include the DMD initiative, leveraging digital platforms to promote the Mediterranean Diet. No scientific awards or grants are explicitly mentioned in the provided text.

Marco StefanucciView profile

Assistant Professor

• Statistical Methodology
• Functional Data Analysis
• Spectroscopy
• +3 more

Marco Stefanucci is an Assistant Professor in Statistics at the Department of Economics and Finance, University of Rome Tor Vergata. Previously, he held positions at the University of Rome La Sapienza, University of Trieste, and served as a postdoctoral researcher at the University of Padova. He earned his PhD from the University of Rome La Sapienza under Professor Pierpaolo Brutti. His research focuses on statistical methodology, functional data analysis, and applications in spectroscopy, mortality trends, and neuroscience. Collaborations include work with Professors Mauro Bernardi, Pierpaolo Brutti, and Laura Sangalli, among others. Research interests include advanced statistical techniques for functional data, compositional data analysis, and their applications in diverse fields such as demography, chemistry, and medical imaging. His recent work emphasizes adaptive regression frameworks, sparse modeling, and classification of complex datasets. He has published extensively on topics like mortality trend analysis, spectroscopic data interpretation, and multimodal imaging methodologies. While no awards or grants are explicitly mentioned, his contributions to statistical theory and applied research are evident through his academic trajectory and collaborative projects. Teaching roles include lecturing on Quantitative Methods and Statistical Learning at undergraduate levels.

Claudio LUPIView profile

Professor

• Econometrics
• Financial Accounting
• Time Series Analysis
• +3 more

Prof. Claudio LUPi is a Full Professor at the Department of Economics, University of Molise. His expertise spans econometrics, financial accounting, and time series analysis with a focus on Benford's Law applications, regional economics, and consumer behavior. He has contributed significantly to understanding financial statement quality in public entities, business cycle synchronization, and the impact of elections on governance. His research also explores innovative areas like medical data simulation for atrial fibrillation detection. Key research areas include statistical methodology (e.g., unit root testing, copula models), public sector economics (municipal governance, fiscal policies), and consumer studies (cultured meat acceptance, dietary trends). His work bridges theoretical econometrics with practical applications in policy and healthcare. Prof. LUPi has authored over 50 publications since 1992, with recent articles emphasizing Benford's Law validation, cross-country banking analysis, and spatial economic dynamics. He actively contributes to R package development for econometric testing (e.g., CADFtest) and has collaborated on national projects like Italy's GDP forecasting.

Rosario BaroneView profile

Assistant Professor

• Statistics
• Economics
• Finance
• +2 more

Rosario Barone is an Assistant Professor of Statistics at the University of Rome Tor Vergata. His research focuses on methodological development of statistical tools with applications in economics, finance, social sciences, and medicine. He specializes in Bayesian inference, Markov processes, and copula models, with recent work addressing complex dependencies and stochastic systems. His research interests include Bayesian nonparametrics, time-interaction processes, and semi-Markov models. Applications span healthcare (e.g., preterm birth analysis) and econometrics. Barone's publications highlight advancements in MCMC methods for continuous-time models and high-dimensional data analysis. He has contributed to interdisciplinary fields, merging statistical theory with practical applications in diverse domains.

Alessio FarcomeniView profile

Professor

• Statistics
• Statistical Methodology
• Data Science
• +5 more

Alessio Farcomeni is a Professor of Statistics at the University of Rome Tor Vergata, specializing in statistical methodology development. His work bridges academia and applied research across disciplines including economics, medicine, ecology, and engineering. He has authored/co-authored over 250 peer-reviewed papers and two books, with notable contributions in hidden Markov models, Bayesian analysis, and quantitative social science measurement. His research emphasizes interdisciplinary collaboration, reflected in studies on population health, economic policy evaluation, and surgical outcome optimization. Farcomeni’s research interests span statistical theory, with a focus on methodological innovations for complex data structures (e.g., longitudinal, spatial, and high-dimensional datasets). He has pioneered approaches in quantile regression, semi-Markov processes, and latent variable modeling. His applied work addresses real-world challenges such as estimating material deprivation scales, modeling cardiovascular disease risk factors, and analyzing pandemic dynamics (e.g., COVID-19 forecasting in Italy). His recent articles highlight advancements in statistical techniques for healthcare (e.g., AI-driven dermatology diagnostics, atrial fibrillation prediction models) and socio-economic analysis (e.g., macroprudential policy impacts, cross-country material deprivation comparisons). He is recognized for developing open-source statistical software packages, contributing to reproducible research practices. Farcomeni holds the distinction of being ranked among Italy’s top scientists by VIA-Academy. His work frequently integrates Bayesian methods, machine learning, and big data analytics to address pressing questions in public health, environmental science, and economic policy.