Find Academic Supervisors

• Calculus of Variations
• Partial Differential Equations
• Mathematical Analysis
• +3 more

Edoardo Mainini is an Associate Professor at the University of Genova, specializing in mathematical analysis and applied mathematics. His research focuses on calculus of variations, partial differential equations, elasticity theory, optimal transport, and nonlinear dynamics. He has contributed to studies on fractional equations, material science, and stochastic processes. His work often bridges pure and applied mathematics, addressing problems in mechanics, probability, and geometric analysis. Mainini has collaborated extensively with researchers such as M. Kružík, D. Percivale, and U. Stefanelli, producing influential papers in journals like Calc. Var. Partial Differential Equations and Arch. Ration. Mech. Anal. . His recent articles (2020–2025) address topics ranging from fractional linear equations to Bayesian nonparametric models. Education includes a PhD in Mathematical Analysis from the University of Genova (2010) and a thesis on Infinite-dimensional porous media equations and optimal transportation . He has organized international conferences and seminars on variational methods and geometric structures. His research emphasizes rigorous mathematical frameworks for physical phenomena, including the linearization of elasticity models and the study of ground states in diffusion-dominated systems. Mainini’s contributions to carbon nanotube geometries and optimal transport theory have been recognized through invited talks at major events, such as the International School of Mathematics “Guido Stampacchia” . His work often explores the interplay between discrete and continuous models, with applications to materials science and geometric optimization.

Michele MancusiView profile

Researcher

• Deep Learning
• Source Separation
• Generative Models
• +3 more

Michele Mancusi is a Senior Research Scientist at Sony and holds a Ph.D. in Computer Science from Sapienza University of Rome, where he remains affiliated with the GLADIA research group under Prof. Emanuele Rodolà. He earned dual degrees in Physics (Quantum Information) from Sapienza, blending computational and theoretical expertise. His research focuses on deep learning applications in audio signal processing, including source separation techniques for music and ecological monitoring. Notable contributions include data-driven methods for marine biodiversity assessment via fish vocalization analysis and innovations in diffusion models for high-resolution audio generation. His work bridges academia and industry, with publications in top venues like ISMIR, ICASSP, and ICLR. Research interests emphasize generative models (autoregressive/diffusion), music technology, and interdisciplinary applications in environmental science. Recent efforts explore coherence-oriented audio representation learning (COCOLA) and inference-time optimizations for music mastering processors (ITO-Master). Publications reflect a trend toward real-world impact, such as accelerating transformer-based translation systems and developing latent diffusion techniques for timbre transfer. Collaborations include cross-disciplinary projects with marine biologists to advance biodiversity tracking through passive acoustic monitoring.

• Statistical Methodology
• Machine Learning
• Parametric Inference
• +7 more

Claudia Di Caterina is an Associate Professor of Statistics at the University of Verona's Department of Economics. Her research focuses on statistical methodology, including likelihood asymptotics, pseudo-likelihoods, anytime-valid inference, and bias reduction techniques. She holds a PhD in Statistical Sciences from the University of Padova and has conducted research at University College London and the University of Toronto. She teaches courses such as Machine Learning for Economics and Statistics for Business Analysis across various programs. Her academic roles include membership in the Economics Department Council and involvement in committees like the Comitato per le Ricerche con Dati sull'Individuo (CORDI) and Commissione Paritetica Docenti-Studenti. She contributes to research laboratories like VELE (Verona Experimental Lab in Economics) and collaborates with centers such as CRIEP and CIDE. Her publications span topics in high-dimensional data analysis, confidence sequences, and causal inference. She actively engages in public events and third mission activities, including workshops on climate risk and cyber security survey methodologies.

• Bayesian Statistics
• Statistical Modeling
• Applied Mathematics
• +3 more

Luca LA ROCCA is an Associate Professor in the Department of Education and Humanities at the University of Modena and Reggio Emilia. His research focuses on Bayesian statistics, graphical models, complex systems analysis, and statistical applications in education and social sciences. He teaches courses such as Applied Mathematics and Social Statistics for engineering and pedagogy programs. His research spans topics including Bayesian inference for directed acyclic graphs, non-local priors, and statistical methods for analyzing complex systems. Notable contributions include work on detecting pairwise interactions in biological networks and assessing skewness in financial markets. He has also developed methodologies for structural learning in heterogeneous data and seismic hazard assessment. LA ROCCA has published extensively in top journals and conferences, with recent works emphasizing the interplay between statistical theory and real-world applications. His teaching responsibilities include courses on applied statistics, probability, and regression analysis.

• Performance Engineering
• Cloud Computing
• Machine Learning
• +3 more

Emilio Incerto is an Assistant Professor at the IMT School for Advanced Studies Lucca, Italy. His research focuses on performance engineering, cloud computing, and formal methods applied to systems optimization. He specializes in developing frameworks for performance prediction, autoscaling solutions for microservices, and integrating machine learning techniques into performance modeling. Key research areas include: Performance modeling and optimization of distributed systems Autoscaling algorithms for dynamic cloud workloads Formal verification of real-time software systems AI-driven performance control mechanisms He has organized international workshops such as AIPerf (Artificial Intelligence for Performance Modeling), showcasing leadership in bridging AI and performance engineering. His work frequently explores reproducibility in performance studies and quantitative evaluation of complex systems like flocking behaviors in biological simulations. Notable contributions include μOpt (an efficient autoscaler for microservices) and μP (a performance prediction framework). His research emphasizes practical implementation of theoretical models through frameworks like SystemC-based environments for HW/SW co-design.

• Bayesian Inference
• Spatial Statistics
• Biostatistics
• +5 more

Maura Mezzetti is a Full Professor of Statistics at the Department of Economics and Finance, Università degli Studi di Roma II (Tor Vergata), Rome, Italy. Her career spans institutions such as Harvard School of Public Health, Università Bocconi, and European Institute of Oncology. She holds a PhD in Methodological Statistics from Università di Trento (1997) and a Laurea in Mathematics from Università di Milano (1992). Current Position: Full Professor (2021–Present) Former Roles: Associate Professor (2010–2021), Assistant Professor (2004–2010, Tor Vergata; 2000–2005, Bocconi) Research Interests: She specializes in Bayesian statistical methods for spatially correlated data, hierarchical modeling, survival analysis, and biostatistics. Her work addresses public health challenges like cancer incidence modeling, socioeconomic disparities in disease burden, and psychometric/motor behavior analysis. She has developed Bayesian compartmental models for chemical exposure studies and contributed to epidemiological research on breast and colorectal cancer risk factors. Scientific Awards: Yamagiwa-Yoshida Fellowship (2014) ICRETT Fellowships (2012) Excellence in Research (2005) from Bocconi AIRC Fellowships (1998, 1993) Teaching Contributions: She has taught Statistics at undergraduate and graduate levels across Tor Vergata and Bocconi, including specialized courses in medicine, European economy, and data analysis. Her pedagogical efforts include authoring educational materials like "Statistica Descrittiva: Esercizi risolti e guida alla soluzione usando Excel" (2002).

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.

Luisa BernardinelliView profile

Professor

• Bayesian Statistics
• Epidemiology
• Genetic Epidemiology
• +4 more

Luisa Bernardinelli is a Full Professor of Medical Statistics at the Faculty of Medicine and Surgery, University of Pavia since 2003, concurrently serving as Head of the Medical and Genomics Statistics Unit within the Department of Brain and Behavioural Sciences. Her academic engagements include Visiting Professorships at the University of Manchester (2013–present) and prior appointments at the University of Cambridge (2001–2012) through the MRC Biostatistics Unit and Statistical Laboratory. Education Degree in Biological Sciences, University of Pavia (1978) Post-graduate in Medical Statistics, University of Pavia (1983, 50/50 summa cum laude) Research Interests Dr. Bernardinelli specializes in Bayesian analysis of spatial-temporal disease risk variation, Monte Carlo methods, and graphical models for descriptive epidemiology. Her genetic epidemiology work focuses on measurement error correction in family/case-control studies, microarray/methylation/proteomic data analysis, pedigree studies, and causal inference for Multiple Sclerosis. Recent research integrates mindfulness-based meditation through population studies on sleep disorders and mental health, exemplified by her 2016 "Scienza e Meditazione" workshop and Neuroscience of Meditation PhD course. Scientific Awards No scientific awards were documented in the provided text. Advising and Grants As coordinator of Pavia's second-level postgraduate course in Medical Statistics and Genomics (2012–present) and SAFD member (2014–present), she supervises PhD candidates in the Data Science in Biomedicine curriculum. Current projects include meditation-based interventions for sleep disorders and body-soul lightness, alongside Multiple Sclerosis heritability studies utilizing Mendelian Randomization for causal gene identification. Labs and Teams She directs the Medical and Genomics Statistics Unit within Pavia's Department of Brain and Behavioural Sciences, driving research in statistical methodology development for genomic epidemiology and meditation-based mental health interventions.

• Cognitive Sciences
• Psychology
• Multisensory Perception
• +3 more

Chiara Valzolgher is a Research Fellow at the Interdepartmental Center for Mind and Brain Sciences (CIMEC) of the University of Trento , Italy. Her research focuses on spatial hearing , metacognition , and multisensory perception , particularly in contexts involving hearing loss and age-related auditory decline . She employs virtual reality and motor strategies to investigate perceptual adaptation and cognitive resource allocation during auditory tasks. Education: 2021: Doctorate in Cognitive Sciences (cum laude), University of Trento & Université Claude Bernard Lyon 1 2016: Master's in Psychology (110/110 cum laude), University of Trento 2014: Bachelor's in Psychological Sciences and Techniques (108/110), University of Milan-Bicocca Her recent work explores how active listening and spontaneous behaviors (e.g., head movements, postural adjustments) enhance auditory spatial confidence and reduce perceived listening effort . She also investigates the impact of face masks on sign language metacognition and develops VR-based auditory training for cochlear implant users. Collaborations include researchers like Francesco Pavani , Elena Gessa , and Elena Giovanelli . Her publications highlight interdisciplinary approaches combining cognitive psychology , neuroscience , and virtual reality to address communication barriers in healthcare and educational settings. Key methodologies include behavioral experiments , metacognitive evaluations , and computational modeling of sensory integration.

• Bayesian Statistics
• Probability Theory
• Mathematical Physics
• +3 more

Emanuele Dolera is an Associate Professor at the University of Pavia, affiliated with the Probability and Statistics research group. His work focuses on Bayesian nonparametric methods, probability theory, and mathematical physics, with a particular emphasis on statistical mechanics and optimal transport frameworks. He has contributed to foundational research in posterior contraction rates, stochastic processes, and the analysis of complex models like the Boltzmann equation and Ewens-Pitman sampling schemes. His research interests span Bayesian predictive inference, asymptotic theory, and the interplay between probability and partial differential equations. Notable contributions include studies on credible intervals in nonparametric estimation, exponential convergence in kinetic equations, and counterexamples in stochastic operator theory. Dolera has also edited special issues honoring prominent statisticians like Eugenio Regazzini. His publications reflect a strong theoretical orientation, addressing topics such as optimal transport applications, count-min sketches in data streams, and the asymptotic behavior of statistical estimators. While no specific grants, awards, or advisees are listed, his extensive bibliography underscores his role as a leading figure in advanced statistical methodology and mathematical analysis.

• Structural Health Monitoring
• Dynamics of Structures
• Civil and Structural Engineering
• +9 more

Marco Civera is a Fixed-term Assistant Professor at the Department of Structural, Building and Geotechnical Engineering (DISEG), Polytechnic University of Turin, where he also contributes to the Interdepartmental Center SISCON – Safety of Infrastructures and Constructions. He is actively involved in teaching and research, with a strong focus on structural health monitoring, dynamics of structures, and civil infrastructure safety. His research interests include Structural Health Monitoring , Dynamics of Structures , Civil and Structural Engineering , Aerostructures , Critical Infrastructures , and Mechanics of Solids and Structures . He applies advanced techniques such as operational modal analysis, machine learning, and nonlinear modeling to assess structural integrity and performance. The recent publications highlight a strong trend in data-driven structural assessment, including the use of generative adversarial networks, meta-learning, clustering algorithms (e.g., DBSCAN), and Bayesian inference for real-time monitoring and damage detection. His work bridges civil and aerospace engineering, with applications in bridges, aircraft, and underground infrastructures. Scientific Awards: Top 2% most-cited scientist conferred by Stanford University published by Elsevier, Italy (2023) Marco Civera advises several PhD students and contributes to commercial research projects such as ISABHEL - Integrated Satellite and ground-based monitoring for BridgeHealth Lifetime assessment (2024–2026). He has also co-developed two patented medical devices: T_RESAP (Tendon Repair Surgical Applicator) and T–REM3DIE (Tendon Repair Medical Device) . His teaching spans multiple disciplines and includes courses in Structural Collapse and Monitoring, Dynamics of Structures, and Computational Mechanics across Civil, Petroleum, Biomedical, and Architectural Engineering programs. He is a member of research teams focused on Structural and Seismic Monitoring and Structural Engineering , contributing to both academic and industrial advancements in infrastructure safety.

Nina DeliuView profile

Assistant Professor

• Bayesian Inference
• Reinforcement Learning
• Multi-armed Bandits
• +6 more

Nina Deliu is a Tenure-track Assistant Professor in Statistics at Sapienza University of Rome's MEMOTEF Department, with joint appointments as a Visiting Faculty Researcher at Google and Visiting Researcher at the MRC-Biostatistics Unit, University of Cambridge. She holds editorial roles at Trials journal and YoungStatS, and maintains active collaborations with institutions including the University of Toronto, National University of Singapore, ISTAT, NADO Italia, and FAO. Education: PhD in Methodological Statistics, Sapienza University of Rome (2021) MSc in Statistics and Decisions, Sapienza University of Rome (2017) MSc in Mathématiques, Informatique, Décision et Organisation, Université Paris Dauphine (2016) Research spans Bayesian inference, reinforcement learning, multi-armed bandits, adaptive experimental design, copula models, and uncertainty quantification, with applications in healthcare, education, and public health. Her work bridges theoretical statistics with real-world challenges in biostatistics, mobile health interventions, and digital education platforms. Publications focus on adaptive experimentation frameworks, response-adaptive clinical trials, reinforcement learning in healthcare, and copula-based statistical methods. Recent work emphasizes finite-sample error control, zero-inflated count data modeling, and multivariate dependency analysis. Awards: XPRIZE $1M Digital Learning Challenge (2023) for the Adaptive Experimentation Accelerator project Research Projects: The role of self-reported health outcomes in cancer risk prediction using UK Biobank data Contextual Multi-armed Bandits for Developing Personalized Mobile Health Interventions Leads collaborations through the IAI Lab (University of Toronto) and coordinates interdisciplinary teams for projects in statistical methodology, health interventions, and official statistics innovation.

Alessandro CasaView profile

Associate Professor

• Mixture Models
• Modal Clustering
• Graphical Models
• +5 more

Alessandro Casa is an Associate Professor of Statistics at the Faculty of Economics and Management, Free University of Bozen-Bolzano (Italy). His research focuses on developing statistical methodologies for high-dimensional and complex data, including latent variable models, density-based clustering, and applications in spectroscopy. He holds a PhD in Statistics from the University of Padova (2016-2020), preceded by master's and bachelor's degrees in Statistical Sciences and Statistics, Economics, and Finance, both from the University of Padova. Education: PhD in Statistics, University of Padova (2016-2020) Master Degree in Statistical Sciences, University of Padova (2014-2016) Bachelor Degree in Statistics, Economics, and Finance, University of Padova (2011-2014) Research Interests: Mixture models, modal clustering, graphical models, variable selection, latent variable modeling, high-dimensional data inference, computational statistics, and chemometrics. His work often addresses challenges in spectroscopic data analysis and applications in agriculture and physics. Teaching: Statistical Methods for Business Analysis (MSc programs) Research Methods and Experimental Design (Tourism Management) Statistics for Tourism, Sport, and Event Management (BSc) Professional Experience: Associate Professor (2024–Present), Free University of Bozen-Bolzano Senior Assistant Professor (2024), University of Bergamo Postdoctoral Research Fellow (2019–2021), University College Dublin Labs/Teams: Collaborations with Vistamilk SFI Research Centre and Insight SFI Research Center during his postdoctoral tenure. Active in model-based clustering research communities, contributing to conferences and workshops globally.

Ayokunle Anthony OsuntuyiView profile

Assistant Professor

• Financial Econometrics
• Computational Econometrics
• Bayesian Inference
• +3 more

Ayokunle Anthony Osuntuyi is an Assistant Professor (Econometrics) at the Department of Economics, Ca' Foscari University of Venice. His research focuses on Financial and Computational Econometrics, with expertise in Bayesian Inference, Monte Carlo Methods, GARCH Models, Risk Management, and Portfolio Theory. PhD Economics (2014), University Ca' Foscari Venice, supervised by Monica Billio and Roberto Casarin Erasmus Mundus Master in Quantitative Economics (2009), University Ca' Foscari Venice, University of Paris 1, and University of Bielefeld BSc Statistics (2004), Obafemi Awolowo University, Nigeria Osuntuyi's research spans Bayesian nonparametric methods, Markov-switching GARCH models for financial volatility, climate risk analysis, EEG spectral dynamics, and optimization algorithms. He has contributed to energy futures hedging, financial cycles, and biomedical signal processing. His recent work trends include integrating Bayesian inference with panel data analysis for climate economics, advancing computational finance through nonparametric GARCH models, and applying statistical methods to neuroscience data. Collaborative projects with Roberto Casarin and Mauro Costantini highlight his focus on methodological innovation. Riccardo Faini award for best Master Thesis (2012) Erasmus Mundus Master Scholarship (2007-2009) Multiple research grants at Ca' Foscari University (2012-2020) Osuntuyi has received federal and institutional scholarships in Nigeria (2002-2003) and contributes as a referee for journals like Economic Modeling and Journal of the Nigerian Mathematical Society. His current role includes teaching and leading research projects on financial econometrics.

• Macroeconometrics
• Time Series Analysis
• Health Economics
• +3 more

Milena Lopreite is an Assistant Professor of Political Economy at the University of Calabria, currently on the tenure track in the Department of Economics, Statistics and Finance 'Giovanni Anania'. She holds a Laurea in Economics (2004), a PhD in Applied Economics (2012), and a second PhD in Economics (2016), along with a Master’s in Applied Econometrics (2013). Her research focuses on Macroeconometrics, Time Series Analysis, Health Economics, Ageing, Policy Analysis, and Causality. Laurea in Economics, University of Calabria (2004) PhD in Applied Economics, University of Calabria (2012) PhD in Economics, University of Parma (2016) Master in Applied Econometrics, School of Economics-Ministry of Economics and Finance (SSEF), Rome (2013) Her work spans health economics, financial crisis dynamics, and macroeconometric modeling, often leveraging social media data and word-embedding techniques. She has contributed to studies on age-related healthcare expenditure, pandemic sentiment analysis, and global health networks. Recent publications explore themes like tuberculosis prevention, social media integration in health surveillance, and the emotional impact of pandemics, reflecting her interdisciplinary approach combining economics, public health, and AI-driven data analysis. She participates in national and European research projects but has not explicitly listed scientific awards.