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Giovanni Di FrancoView profile

Professor

• Social Research Methodology
• Electoral Sociology
• Attitudes and Values of Young People
• +3 more

Giovanni Di Franco serves as Full Professor in the Department of Social and Economic Sciences at Sapienza University of Rome's Faculty of Political Science, Sociology and Communication. He directs the book series "La cassetta degli attrezzi" (Franco Angeli Milano) and holds affiliations with CIRES at Roma Tre University and Bonanno publishing house's scientific committee. His research centers on Social Research Methodology , emphasizing data collection/analysis techniques, historical evolution of methods, qualitative-quantitative integration, and micro-macro data synthesis. Specialized in Electoral Sociology , he examines polling methodologies in digital contexts, while his work on Youth Attitudes explores values during economic crises. Additional expertise spans Europeanization Processes , Social Representations of eating disorders, and Social Cohesion frameworks. Recent publications reveal methodological innovation through machine learning applications and critical analysis of intergenerational inequality, reflecting his commitment to advancing sociological research in contemporary digital landscapes. Professor Di Franco has led significant projects including immigrant family-school interactions (2009-10), food education integration (2009), Campanian legality culture (2008), and European identity formation (2007-09), alongside European Commission-funded e-inclusion research. He supervises doctoral candidates through Sapienza's PhD program in Sociology and Applied Social Sciences. He contributes to interdisciplinary research through LARFAD laboratory and CIRES center, developing methodological tools for social analysis while directing academic publications that shape methodological discourse in Italian sociology.

• Financial Risk Management
• Portfolio Optimization
• Mathematical Finance
• +7 more

Rosella Giacometti is a Full Professor in Mathematical Methods for Economics and Actuarial and Financial Sciences at the Department of Management, University of Bergamo, Italy. Since February 2022, she has served as Dean of the School of Economics and Management at the University of Bergamo. Her academic career spans several decades with extensive teaching experience in financial mathematics, risk measurement, and portfolio theory. Professor Giacometti earned her degree in Computer Science from the University of Milan in 1990, followed by an M.Sc. in Statistics and Operational Research from the University of Essex, UK (1993-94), and a PhD from the University of Brescia (1992-95). Her research focuses on financial risk management, particularly in market risk, credit risk, operational risk, and mortality risk. Her work combines mathematical modeling with practical applications in portfolio optimization and risk measurement. She has led several major research projects including the GACR 19-11965S "A network approach to portfolio optimization and tracking problems" (2018-2021). Professor Giacometti's recent publications demonstrate a strong focus on advanced portfolio optimization techniques, network analysis in banking systems, and the measurement of tail risks in financial markets. Her work increasingly addresses contemporary challenges such as climate risk exposure in banking and ESG-integrated risk measures. 2017 Winner of the national special funding for distinguished researchers (FFABR 2017) 1992 Winner of the scholarship funded by Banca Popolare di Bergamo 1995 Grant from IMI-SIGECO Professor Giacometti has advised numerous students and has been actively involved in professional training courses for banks and government institutions, including the Prime Minister's Office in 2012. She has held visiting positions at prestigious institutions including the University of Washington Bothell, Stony Brook University, and the University of Cyprus. Her research has been supported by multiple grants from MIUR (PRIN projects) and other funding bodies. She leads international collaborations through Erasmus+ agreements with Charles University, Prague, and Texas Tech University, and has served in various leadership roles including as Member of the University Board of Directors (2016-2018) and Director of the Master's Degree in "The Educational operator and Autism" (2015-2016).

• Statistics
• Environmetrics
• Air Quality
• +5 more

Alessandro Fasso is a full professor of Statistics at the School of Engineering, University of Bergamo, Italy, where he has been teaching since 2000. He serves as Editor in Chief of Environmetrics (2019-) and has held various editorial positions for prestigious journals including Stochastic Environmental Research and Risk Analysis and Advances in Statistical Analysis. His international recognition includes serving as President of The International Environmetrics Society (TIES) from 2017-2019 and as a member of the Council of the International Statistical Institute (ISI) from 2013-2017. Professor Fasso's research focuses on statistical methods and applications to environmetrics, air quality, climate variables, and spatio-temporal data analysis. His work spans functional data analysis for atmospheric profiles, multivariate spatio-temporal modeling of air pollution, and statistical approaches for environmental monitoring networks. He has made significant contributions to understanding collocation uncertainty using heteroskedastic functional regression models and studying vertical smoothing mismatch uncertainty when comparing satellite and radiosonde data. His recent publications (2023-2025) demonstrate a strong focus on PM2.5 pollution modeling, particularly examining livestock-related emissions in the Lombardy region using advanced spatio-temporal techniques. His work increasingly integrates functional data analysis, regularization methods, and uncertainty quantification in environmental applications. The articles show a progression from theoretical statistical developments to practical environmental problem-solving with policy implications. President of The International Environmetrics Society (TIES) (2017-2019) Member of the Council of the International Statistical Institute (ISI) (2013-2017) Elected member of the International Statistical Institute (ISI) Founder and previous Coordinator of GRASPA (2013-2015) Member of WG-GRUAN, Working Group on Atmospheric Reference Observations (2013-) Professor Fasso has successfully supervised numerous PhD students including Emilio Porcu, Michela Cameletti, and Francesco Finazzi. His research has been supported by significant grants including EU Horizon 2020: GAIA-CLIM (budget €500,000), Project AQ2009-EN17 (budget €850,000), and PRIN-2006 (budget €260,000). He has served on evaluation committees for the Italian Research Quality Exercise (VQR 2015-2019) and as a referee for international research councils. His international lecturing activities include PhD courses at Peking University and the University of Bolzano-Bozen.

Marco RianiView profile

Professor

• Robust Statistics
• Regression Analysis
• Time Series Analysis
• +5 more

Marco Riani is Full Professor of Statistics at the Department of Economic and Business Sciences, Faculty of Economics, University of Parma since 2006. He directs the Interdepartmental Center Ro.S.A. (Robust Statistics Academy) and has held academic positions including Post Doctoral Fellow (1996-1998), Lecturer (1998-1999), and Associate Professor (1999-2006) at the same institution. Education: Bachelor of Science in Economics (cum laude), University of Parma (1986-1990) PhD in Statistics, University of Florence (1992-1995) His research centers on robust statistics with methodological focus on robust regression , time series , and multivariate analysis . Applied work spans risk management , quality evaluation , international trade monitoring , and medical statistics (neonatal health parameters). He pioneers outlier detection without arbitrary thresholds and robust machine learning. Recent publications highlight robust transformations for regression, explainable AI, and applications in environmental/medical fields. Key trends include automated fraud detection in trade data, soil moisture estimation through time series, and biomarker analysis for birth anomalies. Scientific awards: Best Italian PhD thesis in Statistics (1997) Best contribution at MATLAB expo 2016 (Milan) As advisor, he has supervised five PhD theses and over 100 degree theses. His grant leadership includes Horizon 2020 PrimeFish (2014-2019) and multiple Italian Ministry projects on robust methods. External expertise spans EU Automated Monitoring Tool projects (AMT3-AMT5) for trade fraud detection. Principal Investigator: 11 national/EU projects (2000-2019) Steering roles: ICORS, SIS CLADAG, Ro.S.A. center He co-developed the FSDA toolbox (MATLAB/R) with the European Commission, enabling robust multivariate analysis for institutions like the Joint Research Centre. The Ro.S.A. center drives his collaborative work on outlier detection and data mining.

Alex LainiView profile

Assistant Professor

• Ecology
• Environmental Biology
• Zoology
• +4 more

Alex Laini is an Assistant Professor at the Department of Life Sciences and Systems Biology, University of Turin. His research focuses on ecological modeling, biodiversity assessment, and invertebrate community dynamics, with particular emphasis on dung beetles, aquatic ecosystems, and climate change impacts. Department: Life Sciences and Systems Biology University: University of Turin Laini’s work integrates DNA metabarcoding, statistical ecology, and functional trait analysis to study ecosystem services, invasion biology, and conservation strategies. His recent publications explore topics such as n-dimensional hypervolume modeling, behavioral syndromes in insects, and climate-driven shifts in riverine communities. His research leverages advanced methodologies like environmental DNA analysis and ecohydrological modeling to address challenges in biodiversity monitoring and aquatic ecosystem health. Key collaborations span European rivers, hypersaline lakes, and Mediterranean valleys, reflecting a transnational approach to ecological studies.

Pietro BelloniView profile

Researcher

• Epidemiology
• Medical Statistics
• Social Statistics
• +7 more

Pietro Belloni is a Researcher (RTDa) at the Department of Statistical Sciences, University of Padua, Italy. His primary role involves advancing statistical methodologies for drug safety and public health research through interdisciplinary projects like the Age-IT initiative. He holds a PhD in Statistics from the University of Padua, completed under Professors Giovanna Boccuzzo and Nicholas P. Tatonetti, with visiting research at Columbia University's Department of Biomedical Informatics. His academic journey includes a Master’s and Bachelor’s in Statistical Sciences from the University of Padua, complemented by an Erasmus exchange in Mathematics and Statistics at Århus University. Belloni’s research focuses on epidemiology, medical statistics, and social statistics, with particular emphasis on text mining, spatial epidemiology, and composite indicators for public health assessment. He actively contributes to research teams such as GLASS (a young Italian social statistics laboratory) and the Age-IT project addressing aging society challenges. Teaching responsibilities span epidemiology, medical statistics, and data analysis methods at both University of Padua and H-Farm College. His software contributions include R libraries like perla for mortality cluster analysis and MARMoT for statistical balancing techniques. Belloni’s work bridges theoretical statistics with practical applications in healthcare and social sciences, with publications in journals like Statistical Methods & Applications and Acta Otorhinolaryngol Italica , and book chapters on pharmacovigilance and text mining. His research has addressed topics ranging from cancer staging through NLP to pandemic sentiment analysis during the first wave of the COVID-19 crisis.

• Computational Chemistry
• Physical Chemistry
• Materials Science
• +3 more

Alfonso Pedone is an Associate Professor at the Department of Chemical and Geological Sciences , University of Modena and Reggio Emilia. His research focuses on computational chemistry approaches to understanding oxide glasses, including silicates, borosilicates, and nuclear waste materials, through molecular dynamics simulations and machine learning potentials. Research Interests : Computational materials science, spectroscopy, glass structure-property relationships, quantum chemistry of minerals, and machine learning applications in material modeling. Teaching : Offers advanced courses in Physical Chemistry I , Energetics and Chemical Equilibrium , and Molecular Spectroscopy for chemistry and sustainable chemistry degree programs. Technical Expertise : Specializes in Density Functional Theory (DFT) calculations, metadynamics, and reactive molecular dynamics for surface interactions and crystallization studies. Collaborative Work : Co-author on studies involving ceria polishing mechanisms, iron toxicity modeling, and machine learning potential benchmarking for industrial applications. Publications : Recent work examines pressure-induced glass densification, dispersion interactions in machine learning potentials, and predictive modeling of glass crystallization pathways.

Augusto FerranteView profile

Professor

• Control Theory
• Quantum Computing
• Linear Algebra
• +5 more

Augusto Ferrante is a Full Professor at the Department of Information Engineering, University of Padova. His research spans control theory, quantum computing, and linear algebra, with a focus on optimal control, spectral estimation, and matrix analysis. University: University of Padova Department: Information Engineering Research Interests: Ferrante's work addresses theoretical and applied problems in control systems, including quantum control, Riccati equations, and covariance matrix estimation. His contributions bridge mathematical rigor with engineering applications, particularly in spectral analysis and optimization. Publications Trends: His recent work (2014-2012) emphasizes quantum channel estimation, entropy-based spectral methods, and generalized Riccati equations, reflecting a synthesis of control theory and quantum information science. Book Chapters: He has co-authored works on modeling, estimation, and control, including a 2007 Modeling, Estimation and Control volume dedicated to Giorgio Picci.

Giorgio PicciView profile

Professor

• Stochastic Systems
• System Identification
• Control Theory
• +2 more

Giorgio Picci is a Full Professor in the Department of Information Engineering at the University of Padova, Italy, holding this position since 1980. His academic career includes prior roles as Lecturer (1970-1975) in Statistics, Researcher (1973-1980) at LADSEB-CNR, and Associate Professor (1975-1980) in Electrical Engineering at the same institution. International appointments include Brown University, MIT, University of Kentucky, Arizona State University, Kyoto University, and Washington University. He received his Dr. Engineering degree (cum laude) in Electronic Engineering from the University of Padova in 1967. Professor Picci's research pioneered a geometric framework for stochastic systems centered on Markovian Splitting Subspaces , providing probabilistic analogs of state-space concepts. This work revolutionized understanding of minimality in stochastic models and solved noncausal estimation problems. Current research focuses on: Subspace Identification : Addressing closed-loop systems and statistical properties of multivariable system identification Reciprocal Processes : Modeling finite-support signals (e.g., images) to eliminate border effects of traditional methods Mechanical Systems Identification : Using variational integrators for well-conditioned recovery of continuous-time parameters His publication record (2002-2014) demonstrates deep integration of control theory, linear algebra, and statistical estimation. Key trends include unifying subspace identification algorithms, advancing covariance extension for reciprocal processes, and developing robust methods for ill-conditioned identification problems in mechanical systems. Professor Picci leads the Padova research unit that developed breakthrough closed-loop subspace identification algorithms. His collaborations span global institutions including the Royal Institute of Technology (Stockholm) and MIT, with ongoing work on recursive identification and statistical properties of subspace estimates.

FRANCESCO FERRACUTIView profile

Researcher

• Control Systems
• Robotics
• Brain-Computer Interfaces
• +6 more

Prof. Francesco Ferracuti is a Researcher at the Department of Information Engineering (Università Politecnica delle Marche, Ancona, Italy). His work focuses on advanced control systems, robotics, brain-computer interfaces, and energy management. Key research themes include fault-tolerant control, autonomous navigation, and human-machine interaction through wearable sensors. Research interests span across: Control Engineering (MIMO systems, PID control, Model Predictive Control) Robotics (mobile robotics, UAVs, smart wheelchairs) Neuroengineering (EEG-based BCI, cognitive workload assessment) Energy Systems (renewable integration, home energy management) Data-Driven Methods (machine learning, system identification) Publications emphasize innovative solutions like EEG-driven obstacle avoidance and thermal stress monitoring for smart wheelchairs, demonstrating interdisciplinary approaches combining robotics with biometrics. His work on fault detection in multirotors and energy optimization algorithms highlights practical applications in both industrial and healthcare domains. Current projects include the E-MOTIVE initiative for mobility solutions via affective computing and BCI. His lab develops cutting-edge algorithms for cyber-physical systems security and predictive maintenance in industrial equipment.

• 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.

• Financial Mathematics
• High-Frequency Financial Econometrics
• Stochastic Volatility Modeling
• +2 more

Giacomo Toscano is a Tenured Assistant Professor of Financial Mathematics at the University of Florence, Department of Economics and Management. His research focuses on high-frequency financial econometrics, stochastic volatility modeling, and applications of Fourier methods in economics. He holds a Ph.D. in Financial Mathematics from Scuola Normale Superiore. Research interests include climate risk measurement and econometric methodologies for analyzing financial markets. His work bridges statistical theory with practical applications, such as volatility forecasting using graph attention networks and developing the FMVol MATLAB library for volatility estimation. Recent studies address microstructure noise, Hawkes-driven volatility models, and historical financial systems like 15th-century Barcelona's foreign exchange markets. Publications span 2020–2025, emphasizing volatility dynamics, estimation techniques, and algorithmic trading strategies. His research often combines advanced mathematical tools with empirical validation using S&P500 and other financial datasets. No scientific awards are explicitly listed, though his active publication record suggests potential recognition in econometrics or financial mathematics. Advising and grants details are not provided in the source text.

• Machine Learning
• Cyber-Physical Systems
• Intelligent Embedded Systems
• +5 more

Cesare Alippi is a Professor at Politecnico di Milano, Italy, and Università della Svizzera italiana, Switzerland, with visiting appointments at the University of Kobe (Japan) and Guangzhou (China). His research focuses on machine learning in nonstationary environments, intelligent embedded systems, and cyber-physical systems. Alippi holds IEEE Fellow status and has received notable awards, including the 2016 Gabor Award and the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award. He has authored a monograph, 6 edited books, and over 200 papers. Alippi's academic roles include Vice-President for Education of the IEEE Computational Intelligence Society and Distinguished Lecturer (2014-2016). He has organized major conferences like IJCNN and IEEE SSCI and contributed to initiatives promoting education in computational intelligence. His research addresses challenges in adaptive systems, embedded intelligence, and fault diagnosis. Key projects include deploying wireless sensor networks for environmental monitoring and developing machine learning algorithms for real-world applications like landslide prediction and network security. Alippi's awards reflect his contributions to neural networks and adaptive learning, including the IBM Faculty Award (2013) and IEEE Young Engineer Award (2004).

• 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.