جست‌وجوی استاد و پژوهشگر

Stefano Pelusoمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Network Analysis
• Spatio-temporal Modeling
• +۳ مورد دیگر

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.

Marco Stefanucciمشاهده پروفایل

استادیار

• Statistical Methodology
• Functional Data Analysis
• Spectroscopy
• +۳ مورد دیگر

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.

Marcello Carioniمشاهده پروفایل

استادیار

• Optimal Transport
• Inverse Problems
• Machine Learning
• +۴ مورد دیگر

Marcello Carioni is an Assistant Professor in Mathematics at the University of Twente, affiliated with the Mathematics of Imaging & AI (MIA) group and the SACS group within the Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on optimal transport theory, inverse problems, calculus of variations, and their applications in machine learning and imaging. He holds a PhD in Mathematics from the Max-Planck Institute for the Mathematics in the Sciences (Leipzig), preceded by a M.S. and B.S. in Mathematics from the University of Parma. Education: B.S. (2009, University of Parma), M.S. (2012, University of Parma), PhD (2017, Max-Planck Institute) Research interests include: Dynamic optimal transport and preferential path analysis Sparsity in variational regularization Applications to medical imaging and neural networks Generalized conditional gradient methods Recent work emphasizes sparsity optimization for dynamic inverse problems, Lipschitz spaces in neural networks, and end-to-end reconstruction techniques. His 2025 preprints explore Wasserstein curves and distributionally robust optimization. He has advised PhD students including Christian Amend (2024) and Floor van Maarschalkerwaart (2024).

Andrea Piano Mortariمشاهده پروفایل

استادیار

• Health Economics
• Dynamic Microsimulation
• Exposome
• +۴ مورد دیگر

Andrea Piano Mortari is an Assistant Professor of Economics at the University of Rome Tor Vergata. He specializes in health economics, dynamic microsimulation, and the exposome. His work integrates economic analysis with public health challenges, focusing on chronic disease prevention, healthcare productivity, and mental health. He leads major research initiatives including EU-funded projects STAGE, Obelisk, and LongITools, and is Principal Investigator of a NIH-funded study on Alzheimer’s disease. Prof. Mortari teaches Microeconomics and Research Methods for Social Sciences at the undergraduate level. His research emphasizes policy-relevant analysis, such as evaluating the economic impacts of obesity, diabetes drugs, and public health interventions. He has contributed to projects like the DARE FALLSPREDICT wearable sensor initiative and analyses of healthcare costs linked to overweight/obesity in Italy. His work frequently intersects with spatial econometrics and longitudinal data modeling. He serves on the Project Council of DECIDEHealth and collaborates with international organizations to model disease burden and health system resilience. Notable contributions include studies on the economic implications of the Great Recession on health outcomes, psychotropic drug use during the pandemic, and cost-effectiveness of chronic disease prevention strategies in Europe and the U.S. His research portfolio spans over 25 years, with publications addressing aging populations, healthcare productivity, and the interplay between socioeconomic factors and health. Methodologically, he combines dynamic microsimulation with spatial analysis to forecast healthcare needs and evaluate policy impacts across demographic groups.

Rosario Baroneمشاهده پروفایل

استادیار

• Statistics
• Economics
• Finance
• +۲ مورد دیگر

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.

Guido Gazzaniمشاهده پروفایل

استادیار

• Quantitative Finance
• Stochastic Modeling
• Asset Pricing
• +۳ مورد دیگر

Guido Gazzani is an Assistant Professor in the Department of Economics at the University of Verona, specializing in quantitative finance and stochastic modeling. He holds a Ph.D. in Statistics and Operations Research from the University of Vienna and has held postdoctoral roles at École des Ponts ParisTech and the University of Verona. His research focuses on signature-based models, computational finance, and volatility modeling. He has published in top journals like Quantitative Finance and Mathematical Finance, and actively participates in academic conferences such as the Bachelier Congress. Dr. Gazzani teaches courses on asset pricing and optimal control in finance at the University of Verona and has received awards including a Travel Grant for the Bachelier Congress 2024. He is affiliated with research labs like VELE and contributes to initiatives like the 12th AMaMeF conference. His work bridges theoretical finance with practical applications in derivative pricing and risk management.

• Statistics
• Bayesian Inference
• Nonparametric Inference
• +۳ مورد دیگر

Catia Scricciolo is an Associate Professor in the Department of Economics at Università di Verona since 2015. Previously, she served as Assistant Professor at Università Bocconi di Milano. She earned a degree in Statistics from Università La Sapienza di Roma and a Ph.D. in Statistics from Università di Padova. Her research focuses on Bayesian Nonparametric Inference Nonparametric curve estimation Mixture models Empirical Bayes procedures Bayesian inverse problems with theoretical work on asymptotic properties and likelihood-based procedures. She is actively involved in academic governance as a member of the Faculty Board of Ph.D. in Economics and Finance Collegio Didattico di Economia Aziendale e Management Commissione Paritetica Docenti-Studenti Research Advisory Board (RAB) and contributes to the Verona Experimental Lab in Economics (VELE).

Irene Crimaldiمشاهده پروفایل

دانشیار

• Probability
• Statistics
• Economics
• +۶ مورد دیگر

Irene Crimaldi is an Associate Professor of Statistics at IMT Lucca (Italy) since 2011. She serves on the Scientific Board of IMT's PhD programs . Her academic journey includes roles as a researcher and part-time professor at the University of Bologna (2006-2011) and a tenured assistant professor at IMT Lucca (2011-2014). Crimaldi holds a cum laude degree in Mathematics from the University of Pisa and a cum laude PhD in Financial Mathematics from the Scuola Normale Superiore (Pisa). She has received scholarships from Istituto Nazionale di Alta Matematica and fellowships from University of Pisa and University of Bologna . Her research focuses on Probability and Statistics with applications in Economics, Social Sciences, Computer Science, and Engineering . Key areas include interacting reinforced stochastic processes , network modeling , urn models , species sampling , and stable convergence . She has co-authored influential works on synchronization phenomena, systemic risk in OTC markets, and latent attribute network models. Crimaldi's peer-reviewed publications span 15 most recent articles (2013-2025) addressing synchronization rates, network dynamics, and systemic risk. These works employ stable convergence, central limit theorems, and reinforcement mechanisms, contributing to disciplines like stochastic processes, financial mathematics, and network science. She actively collaborates with institutions such as the University of Bologna , University of Pisa , and IMT Lucca , integrating mathematical rigor with real-world applications in financial systems and social networks.

• Complex Networks
• Neuroscience
• Functional Brain Connectivity
• +۳ مورد دیگر

Tommaso Gili is an Assistant Professor at the IMT School for Advanced Studies Lucca, specializing in interdisciplinary research at the intersection of network science, neuroscience, and biomedical engineering. His work focuses on analyzing complex systems through network theory and advanced imaging techniques, with emphasis on brain connectivity, functional networks, and their disruptions in neurological disorders. Key research areas include: Functional and structural brain network integration Multi-scale Laplacian analysis in heterogeneous systems Applications of network theory to medicine (e.g., schizophrenia, epilepsy, inflammatory bowel disease) Development of novel methods for network coarse-graining and renormalization His research leverages fMRI, MRI, and computational modeling to study: Cerebral blood flow dynamics Neurological connectivity alterations Biomedical signal processing Complex disease biomarker discovery Notable contributions include innovations in: Functional connectivity analysis of C. elegans Topological symmetry-breaking in brain networks Machine learning approaches for brain digital twins

• Large Scale Numerical Linear Algebra
• Data Assimilation
• Covariance Matrices
• +۳ مورد دیگر

Jemima M. Tabeart is an Assistant Professor in the Computational Science group at TU Eindhoven, within the Centre for Analysis, Scientific Computing and Applications. Her research focuses on large-scale numerical linear algebra, data assimilation, and covariance matrices. She also engages in scientific outreach and crafting. Prior roles include a Hooke Fellowship at the University of Oxford and a PDRA position at the University of Edinburgh. Education: PhD in Mathematics from the University of Reading (2019), MRes in Mathematics of Planet Earth from Imperial College London and Reading (2016), MMath from the University of Bath (2015) with a study year at Université Grenoble Alpes. Research interests span numerical linear algebra applications, preconditioning techniques, and variational data assimilation. She co-organizes the 2025 CWI research semester and the 4TU.AMI initiative bridging numerical analysis and machine learning. Her recent work includes advancements in saddle point preconditioners and model reduction techniques for Bayesian inference. Teaching responsibilities at TU Eindhoven include courses on linear algebra and differential equations. She actively supervises Bachelor/Master projects and has one PhD student. Awards and grants include funding from the INI Network Support for Mathematical Sciences. Professional activities include blogging about sustainable travel and co-organizing the Communications in Numerical Linear Algebra series, with talks available on YouTube.

• Cognitive Psychology
• Neuroscience
• Body Representation
• +۵ مورد دیگر

Alessia Tessari is an Associate Professor at the Department of Psychology 'Renzo Canestrari', University of Bologna, specializing in Cognitive Psychology and Experimental Psychology. Her research focuses on body representation, action imitation, apraxia, and the effects of physical exercise on cognitive abilities in children and older adults. She coordinates the BRACCO research group (Body Representation and ACtion COgnition). Education : PhD in Cognitive Neuroscience (S.I.S.S.A., Trieste, 2004) MSc in Forensic Psychology (University of Padova, 2000) BSc in Experimental Psychology (University of Padova, 1998) Research Interests : Body perception and dysmorphia Neurological disorders (e.g., apraxia) Neurorehabilitation using virtual reality Cognitive effects of physical activity Motor control and visuomotor pathways Key Projects : Leading the I-MOVE study on active breaks in schools Developing VR tools for stroke rehabilitation Investigating decision-making in athletes and older adults Lab/Teams : Coordinates the BRACCO group, focusing on interdisciplinary approaches to body representation and action cognition.

Alessio Lachiمشاهده پروفایل

استادیار

• Epidemiological Modeling
• Medical Statistics
• Global Sensitivity Analysis
• +۳ مورد دیگر

Alessio Lachi is an Assistant Professor (Junior RTD A) at Saint Camillus International University of Health and Medical Sciences in Rome, specializing in Medical Statistics (MEDS-24/A). He holds a background from the University of Florence and focuses on mathematical and statistical analysis of contagion processes, epidemiological data modeling, and global sensitivity analysis applied to health policy. His research bridges statistical methodologies with public health challenges, addressing topics like smoking dynamics, gambling behavior, and environmental health risks. Education: Medical Statistics specialization from University of Florence Collaborations: ISPRO, University of Florence (UNIFI), National Research Council (CNR) His work emphasizes interdisciplinary approaches, such as compartmental models for disease transmission and Bayesian inference techniques. Recent studies include analyses of canine tumor epidemiology, alcohol-breast cancer links, and regional gambling prevalence. Despite prolific output, no awards or grants are explicitly mentioned. No student advising records were found in the provided data.

Kai Zhuمشاهده پروفایل

استادیار

• Computational Social Science
• Text as Data
• Digital Platforms
• +۳ مورد دیگر

Kai Zhu is an Assistant Professor at Bocconi University in Milan, Italy. He holds a PhD in Information Systems from Boston University and additional degrees from Peking University and Beijing Language and Culture University. His research focuses on computational social science, leveraging machine learning and network analysis to study digital technologies' impact on markets, media, politics, and society. Key areas include digital transformation in cultural markets, text-as-data methodologies, and social network dynamics. Teaching responsibilities include courses like 'Data Mining for Marketing Analytics' and 'Computational Applications in Marketing.' His work integrates interdisciplinary methods to analyze large-scale datasets from platforms like Wikipedia and social media, addressing challenges such as information poverty and platform monetization. Collaborative projects include contributions to the Wikipedia Detox initiative aimed at detecting toxic online discourse through machine learning models. Notable publications explore topics ranging from platform economics to crisis communication during the early stages of the COVID-19 pandemic. His research emphasizes the ethical and societal implications of digital technologies while advancing computational methodologies for social science inquiry.

Rossana Mastrandreaمشاهده پروفایل

استادیار

• Complex Networks
• Health Economics
• Environmental Economics
• +۳ مورد دیگر

Rossana Mastrandrea is an Assistant Professor at the IMT School for Advanced Studies Lucca, affiliated with the AXES Research Unit. Her work integrates complex network analysis with interdisciplinary research in Economics, Environmental Science, and Behavioral Studies. She holds a PhD in Economics from the Sant’Anna School of Advanced Studies (Pisa) and has conducted research at the Lorenz Institute of Theoretical Physics (Leiden, Netherlands) and the INET project on Macroeconomics Networks. Her research focuses on theoretical and empirical analysis of complex networks in economic, social, and biological systems. Current projects include studying pharmaceutical innovation through patent citation networks (PRIN project on mRNA vaccines), pro-environmental behavior using evolutionary game theory, and carbon emissions via network approaches. She also explores brain connectivity dynamics and global trade networks using statistical physics and agent-based modeling. Key themes in her work include network reconstruction, motif analysis, and the application of optimal transport methods. Her recent studies address topics like cooperation dynamics in structured populations, coalition formation in litigation, and the role of information load in functional brain connectivity. Rossana coordinates the PRIN project analyzing public and private roles in pharmaceutical innovations and has contributed to high-impact studies on environmental performance metrics, global value chains, and schizophrenia-related brain network alterations.

Mimma Nardelliمشاهده پروفایل

استادیار

• 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.