Find Academic Supervisors

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

• Impact evaluation of public policies
• Tax evasion and the shadow economy
• Energy poverty
• +3 more

Alfonso Carfora is Assistant Professor in Economic Statistics at the Department of Economics and Law, University of Macerata, and concurrently serves as a Research Fellow (RtD-B, STAT-02/A). He teaches Probability and Inference, Economic Statistics, Big Data and Microdata, and Panel Data Analysis, and advises the Italian Ministry of Economy and Finance on expenditure evaluation and management processes. Education Ph.D. in Applied Territorial Statistics, University of Naples Parthenope Research interests Carfora’s work centres on the quantitative evaluation of public policies, with particular attention to: Impact assessment of fiscal and energy policies on firms and households Measurement and modelling of tax evasion and the shadow economy Energy poverty dynamics and distributional effects of green transitions Applied econometrics and panel-data techniques Methodologically, he employs advanced econometric models, counterfactual impact evaluation, and multidimensional indicators, collaborating with national and international research groups and publishing extensively in high-impact journals. Publication trends Between 2021 and 2025 his output has focused on three converging themes: (1) drivers and consequences of energy poverty in Europe, (2) energy-transition pathways for SMEs and their policy implications, and (3) fiscal-policy evaluation, particularly regarding tax evasion and VAT-gap estimation. Scientific awards No specific awards listed in the provided material. Advising & grants Consultant, General Accounting Office, Ministry of Economy and Finance (ongoing) Former Head of Regulatory Impact Study and Evaluation Office, Italian Revenue Agency (2013-2023) Collaborates with domestic and international research consortia; specific grant details not provided. Laboratories & teams While no formal laboratory is mentioned, Carfora collaborates closely with research networks in applied statistics and energy economics, frequently co-authoring with colleagues from Macerata, Naples Parthenope, and international partners.

• Probability
• Mathematical Statistics
• Entropy Measures
• +3 more

Francesco Buono is a Fixed-term Assistant Professor at the Department of Mathematical Sciences (DISMA) within Politecnico di Torino . His research focuses on probability theory, mathematical statistics, and entropy-based measures, with a particular emphasis on reliability engineering and stochastic modeling. He has published extensively on topics such as extropy, Kullback-Leibler divergence, and generalized entropy in applications to system reliability and ranked set sampling. His recent publications (2023–2025) highlight collaborations with researchers like Franco Pellerey, Jorge Navarro, and Maria Longobardi. Key trends include advancements in stochastic orders , predictive modeling , and information-theoretic measures applied to failure analysis and statistical inference. Notable journals include Entropy , Communications in Statistics , and Journal of Applied Probability .

• Spatial Sampling Methods
• Quantitative Criminology
• Econometrics
• +4 more

Maria Michela Dickson is an Associate Professor of Economic Statistics at the University of Trento’s Department of Economics and Management. She holds a PhD in Economic Statistics from Sapienza University of Rome (2014) and has undertaken visiting research at the Institut de Statistique, University of Neuchâtel (2014–2016). Her research focuses on spatial sampling methods, quantitative criminology, and econometric modeling of spatially distributed phenomena. She has contributed to major EU-funded projects like the 'Private Corruption Barometer' and has organized international workshops on sampling techniques and spatial statistics. Education: PhD in Economic Statistics, Sapienza University of Rome (2011–2014) Master’s in Law and Economics, LUISS Guido Carli (2009–2011) Bachelor’s in Political Sciences, LUISS Guido Carli (2006–2009) Research interests include spatial sampling design , business corruption analysis , and urban security metrics . She has published extensively on methodological advancements in spatial econometrics and the impact of locational errors in data collection. Her work bridges statistical theory with practical applications in crime prevention and economic policy. Notable contributions include the Private Corruption Barometer project (EU-funded), and leadership in international conferences such as the 2022 ITACOSM conference. She is a member of the Spatial Econometric Association (SEA) and the Italian Statistical Society (SIS).

Simone PadoanView profile

Associate Professor

• Extreme-value Theory
• Statistics of Extremes
• Bayesian Statistics
• +3 more

Simone Padoan is Associate Professor in the Department of Decision Sciences at Bocconi University and research fellow at the Euro-Mediterranean Center on Climate Change. He holds a PhD in Statistical Science from the University of Padua and previously worked at EPFL Lausanne. His research specializes in extreme-value theory, spatial statistics, and applications in climate and financial analysis.

Nicola SalvatiView profile

Associate Professor

• Survey Sampling
• Model-Assisted Inference
• Robust Regression
• +4 more

Nicola Salvati is an Associate Professor in Statistics at the University of Pisa's Department of Economics and Management. He holds a PhD in Applied Statistics from the University of Florence (2004). His research focuses on survey sampling, model-assisted inference, robust regression, quantile and M-quantile regression, spatial statistics, and applications to poverty mapping and Big Data. He contributes to interdisciplinary studies bridging statistics with socio-economic analysis and environmental science. Research interests include developing methodologies for small area estimation, handling linked data, and addressing challenges in causal inference. His work is supported by grants from the European Union and Italy's Ministry of Research. He is a Fellow of the Italian Statistical Society and affiliated with the International Business and Human Rights focus area. Key publications emphasize temporal M-quantile models, causal small area estimation, and applications of statistical methods in air quality and healthcare analysis. His articles frequently address policy-relevant topics like poverty measurement, unemployment, and enterprise sustainability. Advances in his work include methodologies for bias correction, spatial semiparametric modeling, and robust regression techniques. He collaborates on projects involving hedonic pricing, nursing home performance evaluation, and environmental exposure assessment.

• Statistical Modeling
• Biostatistics
• Environmental Data Analysis
• +4 more

Prof. Vito Michele Rosario Muggeo is a Full Professor in the Department of Economics, Business and Statistics at the University of Palermo. His research focuses on statistical modeling, biostatistics, environmental data analysis, and regression techniques. He teaches courses such as Statistical Inference/Statistics 2 and Statistical Methods. Active in academic evaluations, his work spans over two decades with publications in statistical methodologies, public health, and environmental science. Notable contributions include advancements in segmented regression, quantile regression, and applications in healthcare and ecology. His research often involves collaboration with international journals and conferences, addressing topics like cardiovascular health in cancer survivors and pandemic modeling. He is also involved in software development for statistical analysis, contributing R packages like 'segmented' and 'quantregGrowth'. Education & Professional Experience: - Full Professor at University of Palermo (STAT-01/A) - Active in teaching and research since at least 2000, evidenced by publications from 2000s onward. Research Interests: - Development of statistical methodologies for complex data structures. - Applications in public health, oncology, and environmental studies. - Quantile regression, penalized regression, and segmented modeling techniques. Recent Article Trends: - Focus on pandemic analysis (Covid-19 monitoring), cardiovascular outcomes in leukemia survivors, and innovative statistical tools for predictive modeling. Grants & Advising: - No explicit grants listed, but extensive collaboration in research projects across disciplines. - Advises on statistical methodologies via software development (R packages).

• Statistics
• Probability Theory
• Predictive Modeling
• +4 more

Federica GIUMMOLE' is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. Her research focuses on statistical prediction, probabilistic modeling, and environmental applications. She teaches courses such as Applied Statistics for Biology and Biotechnology, and Probability and Statistics across multiple programs including Engineering and Digital Management. Role: Associate Professor Department: Department of Environmental Sciences, Informatics and Statistics University: Ca' Foscari University of Venice Her research emphasizes predictive modeling, with work on statistical inference, Bayesian methods, and environmental data analysis. Recent projects include calibrating predictive distributions for meteorological forecasting and developing optimal prediction techniques for quantiles and probabilities. Publications span peer-reviewed journals like Statistical Papers and Environmental and Ecological Statistics. She has contributed to methodological advancements in scoring rules and composite likelihoods, supported by grants such as the MIUR-funded project on complex dependency models.

Alexander Simon MayerView profile

Assistant Professor

• Econometrics
• Time Series Analysis
• Panel Data
• +3 more

Alexander Simon Mayer serves as a Researcher (RTDa) in the Department of Economics at Ca' Foscari University of Venice. He holds a PhD in Econometrics from WHU--Otto Besheim School of Management (2020), with prior graduate studies at University of Bonn and undergraduate education at University of Cologne. His academic appointment is based at the San Giobbe campus in Venice. His educational background includes: BSc in Economics (2012) - University of Cologne MSc in Economics (2014) - University of Bonn PhD in Econometrics (2020) - WHU--Otto Besheim School of Management Postdoctoral Research (2020-2022) - Institute of Econometrics and Statistics, University of Cologne Mayer's research focuses on advanced econometric methodologies with particular expertise in time series analysis, panel data structures, and causal inference frameworks. His work bridges theoretical econometrics with practical applications in financial and economic modeling, frequently addressing challenges related to nonstationarity, structural breaks, and nonlinear dependencies in complex datasets. His publications demonstrate consistent contributions to methodological advancements in econometric theory. Analysis of his recent publications reveals strong thematic continuity in developing robust econometric techniques for handling complex data structures. His work spans nonstationary panel models, quantile-based causality testing, endogeneity corrections, structural break detection, and copula-based dependence modeling - all addressing fundamental challenges in modern econometric analysis. Mayer teaches graduate-level econometrics courses across multiple programs including Economics, Finance and Sustainability; Engineering Physics; and doctoral programs. His teaching spans from 2022 through the upcoming 2025/2026 academic year, consistently delivering 6-7 credit Econometrics courses at the master's level.

• Statistics
• Randomized Response Techniques
• Survey Sampling
• +5 more

Pier Francesco Perri is a Full Professor of Statistics (SSD STAT-01/A) at the Department of Economics, Statistics, and Finance 'Giovanni Anania' (DESF) of the University of Calabria (Unical). He has held academic positions since 2002 and coordinates institutional activities such as the National Plan for Scientific Degrees (L-41, Statistics) and editorial roles in journals like Metron and Model Assisted Statistics and Applications . His research focuses on survey methodology, sensitive data estimation, and statistical modeling. Education: Ph.D. in Methodological Statistics (University of Trento, 2002), Honors Degree in Statistical and Actuarial Sciences (University of Calabria, 1997) Perri’s research spans randomized response techniques for sensitive populations, copula modeling for financial data, and inequality index linearization. He explores indirect questioning methods to detect truthfulness and improve survey accuracy, particularly for stigmatized behaviors like abortion, migration, and non-heterosexual identities. His work integrates auxiliary information and complex sampling designs to enhance estimation efficiency. Recent publications emphasize innovations in sample survey traditions, including the 2025 editorial on methodological advancements and 2024 studies on adjusting self-protective behaviors in prevalence estimation. His 2023–2021 research includes logistic regression extensions for randomized response models, prevalence estimates for health behaviors, and crossed model applications for sensitive topics. Earlier work (2020–2000) covers item sum techniques, financial copula modeling, and environmental sensitivity analysis. He serves on editorial boards and institutional committees, including the Italian Statistical Society’s Council and the Ph.D. Teaching Board at Unical. The Laboratorio Statistico Informatico supports his research in statistical computing and survey design.

• Statistics
• Econometrics
• Biostatistics
• +7 more

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.

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.

Paolo GIRARDIView profile

Associate Professor

• Environmental Statistics
• Public Health Analytics
• Biostatistics
• +3 more

Paolo Girardi is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. His research focuses on statistical methodologies applied to environmental health, occupational epidemiology, and public health analytics. He teaches courses on statistical modeling, data analysis for tourism, and introductory statistics for climate change studies. Key research interests include asbestos exposure assessment, spatio-temporal statistical modeling, and the health impacts of environmental pollutants. His work combines advanced statistical techniques with real-world applications in occupational health and climate science. Recent studies involve retrospective exposure modeling for dockworkers, cancer mortality analysis in asbestos-exposed cohorts, and spatial clustering of climate data. Teaching responsibilities span undergraduate and graduate programs, including STATISTICS for Business Administration, DATA ANALYSIS FOR TOURISM, and SPATIO-TEMPORAL STATISTICAL MODELS for climate change studies. Office hours are conducted online via Zoom during academic terms. Publications emphasize methodological innovations (e.g., multiverse analysis frameworks) and applied epidemiology (e.g., cholangiocarcinoma risk from asbestos, vaccine hesitancy factors). No listed awards, but extensive contributions to occupational health research are evident through his prolific publication record.

• Econometrics
• Computational Statistics
• Financial Risk Management
• +5 more

Marco Bee is a Full Professor at the Department of Economics and Management, University of Trento. His expertise spans applied econometrics, computational statistics, finance, and risk modeling. He focuses on methodologies for handling heavy-tailed distributions, extreme value theory, and machine learning applications in financial risk assessment. Education details are available in his CV (CVeng.pdf). His research interests include developing statistical models for operational risk, volatility forecasting, and credit scoring, often employing mixture models, copula-based approaches, and indirect inference techniques. He has contributed significantly to the analysis of spatial econometrics and the application of extreme value theory to financial crises and insurance analytics. His recent work emphasizes tail risk estimation, with over 150 publications since 2006. Notable contributions include methodologies for Value-at-Risk (VaR) forecasting, distribution fitting for skewed data, and the use of machine learning to predict defaults in small businesses. His research bridges theoretical statistics and practical financial applications, with a focus on high-frequency data and scenario-based risk analysis. Awards and grants are not explicitly listed in the provided data, but his extensive publication record reflects recognition in quantitative finance and econometrics. He advises students on topics related to computational econometrics and risk modeling, though specific advisee names are not documented here.

Marco GeraciView profile

Professor

• Statistical Methods in Health Sciences
• Quantile Inference
• Random-Effects Models
• +9 more

Marco Geraci is a Professor at the Department of Methods and Models for Economy, Territory and Finance at Sapienza University of Rome . He teaches courses such as Statistics for Health Economics and Computational Tools for Finance . His research focuses on Statistical Methods in Health Sciences , including Quantile Inference , Random-Effects Models , Multivariate Statistics , and Missing Data Analysis . He also specializes in Accelerometer Data , Physical Activity , and Pediatric Oncology . His recent publications emphasize Quantile Regression applications in Public Health , Environmental Science , and Health Economics . Methodological contributions include Directional Quantile Classifiers , Functional Quantile Regression , and Laplace Distribution Projections . Themes span Biostatistics , Surgical Safety , and Maternal/Child Health Disparities .