Find Academic Supervisors

• Biomedical Engineering
• Medical Robotics
• Neural Engineering
• +3 more

Giacomo Severini is a Lecturer in Biomedical Engineering at University College Dublin, Ireland. He holds a MSc in Electronic Engineering (2008) and a PhD in Biomedical Engineering (2012) from Roma Tre University. His postdoctoral research included roles at Harvard Medical School and Spaulding Rehabilitation Hospital in the USA. His work focuses on applying robotics to upper/lower limb rehabilitation, with contributions to muscle synergy analysis and tremor suppression systems. He participated in the EU-FP7 'TREMOR' project (2008–2011). Key research areas include EMG signal processing, cortico-muscular coherence, and rehabilitation technology. His projects emphasize clinical applications such as functional electrical stimulation and real-time movement intent detection. He has published widely in journals like IEEE Transactions on Neural Systems and Rehabilitation Engineering, and conferences including the International Society of Electrophysiology and Kinesiology (ISEK). His work also extends to bio-inspired neural models for tremor control and wearable robotic systems for mobility assistance. Lab affiliations include BioLab³ at Roma Tre University and the Motion Analysis Laboratory at Spaulding Hospital. His research integrates robotics, neuroscience, and biomechanics to enhance rehabilitation outcomes. Current efforts center on synergy-based approaches to movement analysis and robotic-assisted therapies.

• Statistics
• Environmental Science
• Climate Change
• +4 more

Isabella Morlini is an Associate Professor at the Department of Communication and Economics, University of Modena and Reggio Emilia. Her academic roles include teaching courses such as Multidimensional Data Analysis, Statistical Inference, and Statistics for Law students. She specializes in statistical methodologies applied to environmental science, education, and public health. Research Interests: Climate modeling, pandemic impact analysis, educational data challenges, dyslexia measurement, and fuzzy composite indicators. Teaching: Focuses on data analysis techniques for business, statistical inference, and environmental data interpretation. Her research highlights include analyzing urban climate changes in Modena, assessing risk factors for COVID-19 outcomes, and developing online health promotion programs. Collaborations span environmental scientists, public health experts, and educational technologists. Key methodologies involve nonlinear regression, cluster analysis, and fuzzy set theory. Publications emphasize statistical applications in diverse fields, with recent work addressing pandemic impacts and climate change mitigation strategies.

• Analytical Chemistry
• Environmental Science
• Proteomics
• +3 more

Emilio MARENGO is a Professor at the Department of Science and Technological Innovation, Università degli Studi del Piemonte Orientale 'Amedeo Avogadro'. His research focuses on analytical chemistry, environmental science, and proteomics, with a strong emphasis on chemometric methodologies and their applications in food safety, environmental monitoring, and biomedical analysis. Key research interests include: Development of advanced analytical techniques (e.g., UHPLC-HRMS, LC-MS/MS) Contaminant detection in environmental and biological matrices Food authenticity verification using spectroscopic methods Proteomic profiling for disease biomarker discovery He leads projects funded by the European Commission, Italian Ministry of Health, and Ministry of Universities and Research, including initiatives on PFAS pollution control, sustainable aquaculture systems, and chemometric traceability of agro-food products. Recent publications highlight innovations in perfluorinated substance analysis, veterinary drug residue detection, and biogas optimization from organic waste. Collaborations span environmental engineering, food technology, and virology, reflecting interdisciplinary strengths in addressing global challenges like pollution control and food safety. No scientific awards are explicitly listed, though his extensive publication record indicates impactful contributions.

• Economic Growth
• Health Economics
• Macroeconomics
• +3 more

Giovanni Trovato is a Professor of Economic Statistics at the University of Rome Tor Vergata. His research focuses on economic growth, health economics, macroeconomics, and semiparametric models. He has published extensively on topics such as the impact of organized crime on economic development, macroprudential policies, and healthcare system efficiency. Trovato’s work often combines theoretical frameworks with empirical analyses, addressing issues like fiscal consolidation effects, human capital accumulation, and institutional quality measurement. His recent studies include investigations into the relationship between taxation and growth, life expectancy disparities in Europe, and the role of health amenable deaths in social value assessments. He has contributed to understanding corruption’s economic consequences and the dynamics of credit volatility. His research has been disseminated through leading journals and conferences, reflecting his interdisciplinary approach to economics and statistics. Education details are not explicitly listed, but his academic career suggests advanced training in econometrics and economic theory. His articles span from 2001 to 2025, demonstrating sustained contributions to multiple subfields. Notable works include Organized Crime, Corruption, and Economic Growth (2025) and Human capital-based growth with depopulation and class-size effects (2024). Trovato’s work emphasizes empirical rigor and policy relevance, often employing innovative statistical methodologies like finite mixture models and frontier analysis.

Raffaella PiccarretaView profile

Associate Professor

• Sequence Analysis
• Life Course Trajectories
• Categorical Longitudinal Data
• +2 more

Raffaella Piccarreta is an Associate Professor of Statistics at Bocconi University and a fellow of several research centers including Dondena Centre and BIDSA. Her research focuses on data analysis methodologies, particularly sequence analysis for categorical longitudinal data and life course trajectories. She has actively contributed to COVID-19 research during 2020/2021. She maintains a strong programming focus, developing educational tools like the UBStats R package for introductory statistics courses. Her recent publications demonstrate interdisciplinary collaborations in epidemiology, social sciences, and computational methods.

Leonardo TenoriView profile

Associate Professor

• Metabolomics
• Foodomics
• Nuclear Magnetic Resonance (NMR) Spectroscopy
• +4 more

Leonardo Tenori is an Associate Professor at the Department of Chemistry, University of Florence, affiliated with the Magnetic Resonance Center (CERM). He holds a master’s degree in Chemistry (2002) and a PhD in Structural Biology (2008), both from the University of Florence. His research focuses on metabolomics, particularly applying Nuclear Magnetic Resonance (NMR) spectroscopy to biomedical, pharmacological, and agricultural challenges. Education: Master’s in Chemistry, University of Florence, 2002 PhD in Structural Biology, University of Florence, 2008 Research Interests include metabolomics applications in disease diagnosis (e.g., celiac disease, breast cancer, cardiovascular disorders), development of statistical algorithms for data analysis (e.g., KODAMA), and collaborations in clinical and agricultural research. His work emphasizes metabolic biomarker discovery and integrative omics approaches. Recent research highlights include studies on stroke outcome prediction via blood biomarkers, metabolomic profiling of plant-based beverages, and lipidomic analysis of human sperm. These projects underscore his expertise in NMR-based metabolomics and its translational applications. Awards: 2015 Fellowship from the Italian Foundation Veronesi for melanoma metabolomics research He has contributed to over 100 publications and collaborates internationally. His work includes developing predictive models for disease recurrence in cancer patients and exploring metabolomic signatures in chronic diseases. He also investigates applications in agriculture, such as olive oil quality assessment and dairy cow health monitoring. Labs/Teams: Part of the Magnetic Resonance Center (CERM) at the University of Florence, contributing to interdisciplinary research in structural biology and metabolomics.

Gaia MeoniView profile

Research Fellow

• Metabolomics
• Foodomics
• NMR Spectroscopy
• +3 more

Gaia Meoni is a Research Fellow in the Department of Chemistry at the University of Florence, Italy, affiliated with the Magnetic Resonance Center (CERM). She holds a Master's Degree in Molecular Biology and Genetics from the University of Pavia (2015) and a PhD in Structural Biology from the University of Florence (2018, cum laude). Her research focuses on NMR-based metabolomics, foodomics, and multivariate statistics, with applications in biomedical diagnostics, food quality, and agro-food systems. Key projects include the COMETA initiative studying metabolic profiles of COVID-19 patients, metabolomic analysis of Parkinson’s disease, and lipidomic studies of bacterial infections. She has contributed to over 30 peer-reviewed articles, emphasizing NMR applications in disease biomarker discovery and food authentication. Awards include the 2019 Faber 2 Prize for innovation in scientific talent development in Tuscany. Her work bridges clinical and agricultural sectors: analyzing biofluids (urine, saliva) for disease pathways, optimizing dairy cow health via milk metabolite profiling, and evaluating olive oil quality through post-harvest practices. She collaborates with industry (Giotto Biotech) and academic networks (CIRMMP), advancing translational metabolomics research. Grants include Tuscany Regional funding for SARS-CoV-2 metabolic studies and postdoctoral support from the University of Florence. Current projects explore boron supplementation in calves and selenium biofortification in tomatoes, reflecting a commitment to both human and agricultural health. Lab affiliations include CERM and CIRMMP, where she applies advanced NMR spectroscopy and machine learning to metabolomic challenges. Her research narrative emphasizes interdisciplinary solutions to global health and sustainability issues.

Alejandro Luis CallaraView profile

Researcher

• Biomedical Signal Processing
• Neuroimaging
• Brain Connectivity
• +6 more

Alejandro Luis Callara serves as a postdoctoral researcher at the University of Pisa's Research Center "E. Piaggio," specializing in biomedical signal and image processing methodologies for neuroscience applications. His work bridges engineering and cognitive science through advanced computational approaches to physiological data analysis. His academic foundation includes: Bachelor’s Degree in Biomedical Engineering (2012), University of Pisa Master’s Degree in Biomedical Engineering (2015), University of Pisa PhD in Information Engineering (2019), University of Pisa Callara's research centers on developing analytical pipelines for EEG and fMRI data to map brain connectivity networks, with particular expertise in autonomic nervous system interactions during emotional processing. He actively contributes to the Brain matters team's development of neuronal segmentation tools for confocal microscopy data, extending his methodological focus to multi-scale tissue imaging. His technical proficiency spans directed coherence analysis, dynamic causal modeling, and thermal response quantification. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) autonomic physiology investigations examining parasympathetic-sympathetic coupling during emotional tasks using EDA and HRV metrics; (2) innovative neuroimaging techniques for brainstem fMRI analysis and sparse connectivity mapping; and (3) multimodal integration studies exploring olfactory-visual-auditory interactions in affective disorders through VR/AR paradigms. Methodologically, his work consistently employs advanced signal processing frameworks like PCA-based partial correlation and ThermICA for multivariate analysis. Within the Research Center "E. Piaggio," Callara collaborates across bioengineering and robotics initiatives, particularly through the Brain matters team where he applies computational neuroscience to neuronal morphology analysis. His current projects focus on refining contactless stress classification systems using thermal imaging and developing immersive VR scenarios for anxiety disorder research, demonstrating strong translational potential for clinical applications.

Mimma NardelliView profile

Assistant Professor

• Biomedical Signal Processing
• Wearable Monitoring
• Nonlinear Physiological Dynamics

Mimma Nardelli is an Assistant Professor at the University of Pisa's Department of Information Engineering and Research Center 'E. Piaggio'. Her research focuses on biomedical signal processing, wearable monitoring, and nonlinear analysis of physiological dynamics in clinical and healthy populations. She develops methods for assessing autonomic function, emotional states, and clinical conditions through multimodal biosignal analysis. She coordinates the PRIN project MOONSHINE and collaborates on EU projects including POTION and NEVERMIND. Her teaching includes Biometrics Systems, Affective Computing, and AI in Medicine. Awards include the 2017 GNB Award for her PhD thesis on HRV analysis for mood assessment. Research spans: wearable photoplethysmography validation, autonomic state characterization using multiscale entropy, cardiovascular coupling analysis, and physiological responses to affective stimuli. Applications include neonatal care, mental health monitoring, and human-robot interaction.

• 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.

Luca RossiniView profile

Associate Professor

• Econometrics
• Statistics
• Bayesian Methods
• +6 more

Luca Rossini is an Associate Professor in Statistics at the Department of Economics, Management and Quantitative Methods, University of Milan (Statale), and a Senior Researcher at Fondazione Eni Enrico Mattei (FEEM). His research focuses on Bayesian econometrics, time series analysis, and network theory applications in economics. He leads major grants such as 'Modelling Non-standard data and Extremes in Multivariate Environmental Time series (MNEMET)' and 'Mapping and Pricing of Methane Emissions from the European Electricity Sector (MAP-of-MeLEES)'. Rossini is also involved in organizing the Milan Time Series Seminars across multiple institutions. Grants: Co-PI for EUR 285k MAP-of-MeLEES (2023-2025) and EUR 250k MNEMET (2023-2025) Editorial Role: Associate Editor for Annals of Applied Statistics since 2024 His work bridges methodological advancements in statistics with practical applications in energy economics, financial systems, and environmental data analysis.

Massimo TavoniView profile

Professor

• Climate Change Economics
• Environmental Economics
• Energy Economics
• +7 more

Massimo Tavoni is a Full Professor at the School of Management, Politecnico di Milano, and Director of the RFF-CMCC European Institute on Economics and the Environment (EIEE). He is a leading expert in climate change economics, energy policy, and integrated assessment modeling, with extensive contributions to international climate science and policy. Research Interests: Climate Change Economics Environmental and Energy Economics Behavioral Economics Integrated Assessment Modeling Climate Policy Design and Evaluation His recent research explores behavioral interventions for energy conservation, climate finance mechanisms, carbon pricing, and modeling of low-carbon transitions. The analysis of his publications reveals a strong focus on interdisciplinary approaches combining economic modeling with behavioral insights and climate science, particularly in the context of global climate agreements and national mitigation strategies. Scientific Awards and Honors: European Research Council (ERC) Grant recipient Lead Author, IPCC 5th and 6th Assessment Reports Best Invention of the Year, Time Magazine (2009) Service and Advisory Roles: Massimo Tavoni has advised major international institutions including the OECD, the World Bank, and the Asian Development Bank on climate change policy. He co-directs the International Energy Workshop and served as Deputy Editor for Climatic Change . He has held prestigious fellowships at Stanford University’s Center for Advanced Study in the Behavioral Sciences and as a postdoctoral fellow at Princeton University. Laboratories and Research Groups: As Director of EIEE, he leads a multidisciplinary research team focused on economics and environmental policy, fostering collaboration between economists, modelers, and policy analysts to inform climate action at global and regional levels.

• Multidimensional Data Analysis
• Classification Problems
• Information Society
• +4 more

Maria Adele Milioli is an Associate Professor at the Faculty of Economics, Department of Economic and Business Sciences, University of Parma. She has been a faculty member since 1998 and previously served as a researcher at the same institution from 1990 to 1998. She earned her PhD in Statistics from the University of Trento in 1988. Current Appointments: Associate Professor, University of Parma (1998-present) Past Appointments: Researcher, University of Parma (1990-1998) Her research focuses on multidimensional data analysis , with recent emphasis on classification problems in the context of the information society . She has co-authored over 40 scientific papers, primarily in business statistics and applied statistical inference. Key research areas include: Econometrics Statistical Modeling Financial Inclusion Social Equity Business Data Analysis She teaches Business Statistics (Modules 1-2) at the second cycle degree level in Business Administration and Statistics in Economics and Management programs across multiple academic years (2013-2025). Her publications reflect expertise in statistical methodology, economic applications, and financial policy analysis. Contact: mariaadele.milioli@unipr.it

• Statistics
• Decision Analysis
• Multivariate Analysis
• +3 more

Antonio Lucadamo is an Associate Professor at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of the University of Sannio. His research focuses on statistical modeling, decision analysis, and multivariate methods with applications in diverse fields such as financial markets, cultural festivals, environmental science, and healthcare evaluation. Key Research Areas : Decision theory, ordinal data handling, AHP methodology, environmental quality assessment Recent Collaborations : Pietro Amenta, Ida Camminatiello, Gabriella Marcarelli, and Matteo Rossi Publication Trends : His work spans from foundational statistical techniques (e.g., co-inertia analysis, Taguchi index generalization) to applied studies in financial decision-making, festival loyalty models, and environmental quality assessment. Notable subfields include pairwise comparison matrices, OWA operators, and spectral soil analysis. Contact : antonio.lucadamo@unisannio.it

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 .