Antonio Carta is an Assistant Professor in the Department of Computer Science at the University of Pisa. His primary research focuses on Continual Learning (CL) with deep neural networks, particularly addressing challenges like catastrophic forgetting and knowledge consolidation in online settings. He leads the Computational Intelligence and Machine Learning (CIML) group and contributes to the Pervasive AI Lab (PAILab) . As the lead maintainer of the Avalanche library, he develops end-to-end tools for deep continual learning. His work spans applications in computer vision, NLP, and time series analysis. Research emphasizes distributed CL, efficient knowledge transfer, and replay-free methods. Teaching includes courses on Continual Learning, Collective Machine Intelligence, and Intelligent Systems at the graduate level. Publications highlight adaptive normalization, temporal ensembles, and domain-based Bayesian approaches in CL.
Donato Posa is a Full Professor in Statistics at the Department of Economic Sciences, University of Salento. He holds the academic position of Professor Ordinario (Full Professor) in the field of Statistics (SECS-S/01) and is actively engaged in research and academic leadership. His work bridges economics, environmental science, and advanced statistical modeling. University: University of Salento Department: Department of Economic Sciences Academic Rank: Professor Email: donato.posa@unisalento.it Office: Centro Ecotekne Pal. C, S.P. 6, Lecce - Monteroni, LECCE (LE) Phone: +39 0832 29 8737 Donato Posa earned his degree in Physics, cum laude, from the University of Bari with a thesis conducted at CERN, Geneva, and completed a specialization in Physics. His international academic experience includes extended research stays at Stanford University (USA), the University of Arizona, and the University of North Carolina. His research focuses on spatial and spatio-temporal statistics, with major contributions in geostatistics, multivariate geostatistics, stochastic simulation, and time series analysis. He has developed theoretical and applied models for environmental monitoring, pollution assessment, and socioeconomic data analysis. His work has been applied in risk mapping, environmental policy, and urban planning. The 15 most recent articles reflect a consistent trend in spatio-temporal modeling and geostatistical innovation. They span theoretical developments in covariance modeling and variogram analysis to practical applications in environmental monitoring, pollution dispersion, and ecosystem well-being. The research demonstrates a strong interdisciplinary focus, integrating statistical theory with environmental, economic, and computational sciences. His scientific recognition includes multiple CNR and NATO research grants, CERN awards, and an International Diploma of Honour from the American Biographical Institute for contributions to geostatistics. He has served on editorial boards, chaired international conferences (e.g., GeoEnv 2012), and participated in national scientific qualification committees. Posa has supervised numerous research projects funded by CNR, MIUR, Fondazione Caripuglia, and EU programs. He has acted as a scientific referee for leading journals such as Stochastic Environmental Research and Risk Assessment and Computational Statistics and Data Analysis . He has also led educational initiatives in statistical methods and GIS for environmental and cultural heritage applications. He has been actively involved in academic governance, serving as President of the Research Observatory at the University of Salento (2013–2017) and as a member of national evaluation committees. He has chaired international conferences and contributed to major scientific associations including the Bernoulli Society, the International Association for Statistical Computing, and the American Statistical Association.
Enrico Bibbona is an Associate Professor at the Department of Mathematical Sciences (DISMA) at Politecnico di Torino, where he contributes to the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. His academic profile spans statistics, mathematical modeling, and data science applications across multiple engineering disciplines. His primary research interests include: Asymptotic statistics Bayesian inference Biostatistics Industrial statistics Mathematical biology and modeling in life sciences Statistical inference for stochastic processes Stochastic modeling His specific research lines focus on Reaction Networks approximation and inference, Statistical modeling of animal calls, Statistics and modeling for the SmartPad system (in collaboration with ITT), and Statistics for deterministic and stochastic dynamical models. His work bridges theoretical statistics with practical applications across various scientific domains, particularly in biological systems, industrial applications, and data science. His notable recognition includes the Elsevier/SPA Travel Award presented by Elsevier and Bernoulli Society, Denmark (2011). Professor Bibbona actively mentors PhD students across multiple programs, currently supervising Daniele Poggio and Saeed Sani in Mathematical Sciences, and has previously guided Elena Sabbioni (thesis: Statistical Methods for Complex Biological and Clinical Data) and Hiu Ching Yip (thesis: Statistical models for understanding biomedical data). His teaching spans Mathematical Engineering, Data Science and Engineering, and Communications Engineering programs, where he teaches courses including BioQuants, Statistical methods in data science, and Project: Software-defined communication systems. He is a key member of the Statistics and Data Science research group at DISMA and has contributed to practical applications through patents including "BRAKING SYSTEM TEMPERATURE ESTIMATOR" and "A method for estimating the wear of a vehicle brake element," demonstrating his ability to translate theoretical work into real-world solutions.
Giuseppe Ragusa is an Associate Professor of Economics at the Department of Economics and Law, Sapienza University of Rome, where he also coordinates the PhD in Economics program. Previously, he held positions at the University of Pisa, Rutgers University, and the University of California, Irvine. He earned his PhD in Economics from the University of California, San Diego, and an undergraduate degree from Bocconi University. His research focuses on microeconometrics, time series analysis, economic forecasting, and machine learning applications in economics and finance. He has also served as a Senior Economist at the European Central Bank's Monetary Strategy Division from 2015 to 2017. Ragusa teaches courses in econometrics, time series, and machine learning, utilizing programming tools like Julia, R, and Python. His work has been published in leading journals such as the Journal of Monetary Economics, Journal of Econometrics, and Review of Economics and Statistics. He maintains an active research agenda centered on monetary policy analysis and the application of advanced statistical methods to economic problems.
Carlo Orsi is an Assistant Professor in Statistics at IMT School for Advanced Studies Lucca since November 2023. He holds a Ph.D. in Statistics (2014) from the University of Milan-Bicocca, preceded by a Master's (2011) and Bachelor's (2005) in Statistics from the same institution. Education: Bachelor's in Statistics, University of Milan-Bicocca (2005) Master's in Biostatistics & Experimental Statistics, University of Milan-Bicocca (2011) Ph.D. in Statistics, University of Milan-Bicocca (2014) His research focuses on non-central probability distributions over the unit simplex, multi-output Gaussian process regression, change point analysis, and applied statistics in tourism, medicine, and engineering. He has held adjunct professor roles at multiple universities, including teaching Mathematical Analysis and Probability, and served as a research collaborator at institutions like the National Research Council. His recent publications explore theoretical advancements in non-central distributions, with implications for statistical computation and simulation. He has held diverse roles in academia and industry, including statistical work at pharmaceutical and market research firms, and organizational roles like membership in the Local Organizing Committee for an international statistical conference (2008).
Luigi Amedeo Bianchi is an Associate Professor in the Department of Mathematics at the University of Trento. His expertise spans stochastic processes, partial differential equations, fluid mechanics, and probability theory with a focus on stochastic partial differential equations. He has contributed to areas like turbulence modeling, stochastic fluid dynamics, and mathematical physics. His work bridges theoretical mathematics with applications in cosmology, astrophysics, and education through Olympiad mathematics resources. Research interests include stochastic differential equations under Gaussian noise, fractional processes, and modulation equations in unbounded domains. He has published extensively on topics ranging from stochastic Navier-Stokes equations to logical reasoning in mathematical competitions. His recent work (2023-2024) explores Wagner Framework systematization in graph theory and reinforcement learning, alongside improved cosmological data analysis methods. Notable contributions include textbook authorship on probability theory with exercises, and editorial work honoring prominent stochastic analysts. His publications reflect interdisciplinary engagement with both pure and applied mathematical challenges. Academic career includes teaching and research activities at the University of Trento, though specific educational background details are not provided here. No listed awards or grants are mentioned in the current data.
Daniela Marella is a Professor at the Department of Social and Economic Sciences, Sapienza University of Rome. She teaches Statistics and holds office hours on Tuesdays from 10:00 AM to 12:00 PM. Her email address is daniela.marella@uniroma1.it . Teaching: Statistics (Sociology, Economics, Development Studies) Research: Statistical matching, Bayesian networks, survey sampling, uncertainty quantification Publications: 15+ articles on non-probability sampling, measurement error, and interrater agreement Her research focuses on statistical matching methodologies, Bayesian network applications in survey data, and handling selection bias in non-probability samples. She explores measurement error modeling, empirical likelihood approaches, and resampling techniques for complex survey designs. Recent publications emphasize uncertainty analysis in statistical matching, graphical models for data integration, and Bayesian structural learning. Key areas include non-ignorable sampling, ordinal categorical data agreement, and pseudo-population resampling frameworks.
Raffaele Argiento is a Full Professor of Statistics at the Department of Economics, University of Bergamo since September 2021. His academic career focuses on advanced statistical methodologies with applications across various domains including environmental science, public health, and data analysis. His research is prominently featured in high-impact statistical journals and conference proceedings. Argiento's research interests center around Bayesian statistical methods, particularly in functional data analysis, nonparametric Bayesian modeling, and clustering techniques. His work demonstrates expertise in developing innovative statistical approaches for complex data structures, including spatio-temporal data, categorical variables, and high-dimensional datasets. His research has significant applications in environmental monitoring (particularly air pollution analysis), public health (obesity rate modeling), and seismic monitoring through crowdsourced data. His methodological contributions include advancements in mixture models, partition models, and computational algorithms for statistical inference. His recent publication record shows a strong trend toward developing computationally efficient Bayesian methods for real-world applications. The research spans from theoretical developments in nonparametric Bayesian statistics to practical implementations for environmental monitoring, health data analysis, and functional data processing. His work demonstrates a consistent focus on bridging theoretical statistical advancements with practical applications across multiple scientific domains. Professor Argiento teaches several advanced statistical courses at the University of Bergamo, including Applied Statistical Modelling , Probability and Statistics , and Statistical Models for both undergraduate and graduate programs in Economics and Data Analysis. His teaching reflects his research expertise, emphasizing modern statistical methodologies and computational approaches.
Patrizio Campisi is a Full Professor in the Section of Applied Electronics at the Department of Engineering, Roma TRE University, Rome, Italy. He leads cutting-edge research in digital signal and image processing with applications to secure multimedia communications and biometrics. He has held visiting positions at the University of Toronto, Beckman Institute (UIUC), and École Polytechnique de Nantes, and has been a Marie Curie Fellow (2010–2014). His educational background includes a Laurea (summa cum laude) in Electronic Engineering from Sapienza University of Rome and a Ph.D. in Electrical Engineering from Roma TRE University. His research focuses on secure biometric recognition (signature, keystroke, EEG, vein), digital watermarking, blind image deconvolution, HDR imaging, and privacy-preserving technologies. He has contributed significantly to template protection, multimodal biometrics, and forensic image analysis. His work bridges engineering, computer science, and security, with strong applications in mobile authentication, border control, and social media safety. The recent publications reflect a strong trend in biometrics, image forensics, and privacy-enhancing technologies, with a focus on real-world applications in mobile systems, healthcare, and law enforcement. Key themes include secure authentication, de-identification, and computational imaging. IEEE Second International Conference on Biometric Systems 2008 Best Student Paper Award IEEE Biometric Symposium 2007 Best Paper Award IEEE International Conference on Image Processing 2006 Best Student Paper Award Marie Curie Fellow (2010–2014) NATO-CNR Advanced Fellowship (2003) IEEE Senior Member Prof. Campisi has supervised numerous students and leads the BioMedia4n6 lab. He has secured major EU grants including H2020 projects AMBER, COSMOS, and ENCASE. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Information Forensics and Security (2018–2020) and Chair of the IEEE Information Forensics and Security Technical Committee (2017–2018). He is actively involved in research networks such as COST Actions on de-identification and biometrics-forensics integration, and has contributed to EU policy via the BEST Thematic Network on biometrics and fundamental rights. His lab, BioMedia4n6, focuses on biometrics, multimedia forensics, and privacy-aware systems.
Gianluca Mastrantonio is an Associate Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin , Italy. He is a member of the Interdepartmental Center SmartData@PoliTO and actively contributes to the Statistics and Data Science research group. His academic roles include teaching in PhD programs (Mathematical Sciences, 2023-2025) and master's courses such as Statistical Methods in Data Science and Statistical Models/Statistical Learning . His research focuses on Bayesian Statistics and Hierarchical Models , with applications spanning Biostatistics , Environmental Monitoring , Machine Learning , and Computational Statistics . Key keywords include RNA Velocity , Sea Climate Analysis , Animal Movement Modeling , and Bayesian Software Development . Selected Scientific Awards Steering Committee Member, Royal Statistical Society (Emerging Applications, 2021-) Effective Member, International Statistical Institute (ISI, 2019-) Effective Member, Graspa (Italy, 2017-) Associate Editor, Journal of Statistical Computation and Simulation (2020-) Key Research Themes Bayesian Modeling of RNA Dynamics Environmental and Wildlife Behavior Analysis Statistical Software Development (Julia/R packages) Climatic Change-Point Detection Integration of Linear/Circular Data in Ecology Applications in Prostate Cancer Diagnostics
Gianluigi Pillonetto is an Assistant Professor at the Department of Information Engineering , University of Padova , where he has been employed since 2005. His academic career focuses on system identification, stochastic systems, and nonparametric regularization techniques. Born: January 21, 1975 in Montebelluna, Italy Education: Doctoral degree (1998) and PhD (2002) in Computer Science/Engineering Research roles: Visiting scholar (2000), Visiting scientist (2002), Research Associate (2002-2005) His research spans system identification, stochastic processes, and deconvolution problems, with a particular emphasis on Bayesian methods and kernel-based regularization. He has contributed to areas like distributed Gaussian regression, sparse system identification, and nonlinear stochastic modeling in physiological systems. Recent publications (2016-2021) examine Gaussian regression techniques for distributed systems, entropy-based kernel design, and nonlinear stochastic deconvolution. These works incorporate machine learning principles into control theory, focusing on applications in wireless communications, robotics, and biomedical engineering.
Mattia Zorzi is an Associate Professor in the Department of Information Engineering at the University of Padova, Italy. He has held academic positions since 2014, transitioning from Assistant Professor to Associate Professor in 2020. His research focuses on system identification, machine learning, and robust control, with applications in dynamic brain networks, robotic systems, and quantum information processing. Education: Ph.D. and M.S. in Information Engineering from University of Padova International Experience: Visiting Scientist at University of Cambridge (2013-2014), Research Associate at University of Liege (2013-2014) Zorzi's research integrates robust and distributed filtering, inverse dynamics learning, and nonparametric identification of Kronecker networks. His work bridges theoretical advancements in spectral estimation with practical applications in neuroscience (e.g., effective connectivity analysis) and control systems (e.g., robust Kalman filtering under uncertainty). Recent publications (2024-2023) demonstrate expertise in ARMA graphical models, kernel-based estimation, and optimal transport for Gaussian processes. He has contributed to the IEEE and IFAC communities as Associate Editor and actively participates in editorial roles for leading journals. Scientific Awards: IEEE Senior Member (2021) Member of IFAC Technical Committee TC 1.1 (2018) Associate Editor roles in Automatica, IEEE Control Systems Letters, and major conferences Grants & Collaborations: Collaborated on projects involving quantum channel estimation, free-space quantum communication, and biomedical signal processing.
Gustavo Cevolani is an Associate Professor of Logic and Philosophy of Science at the IMT School for Advanced Studies Lucca, Italy. He leads the Models, Inference, and Decisions (MInD) group within the Molecular Mind Laboratory (MoMiLab). His research focuses on formal epistemology, cognitive science, and the philosophy of science, particularly exploring rational decision-making, truth approximation, and methodological issues in social and behavioral sciences. He holds a PhD in Philosophy of Science from the University of Bologna (2005). His work integrates conceptual analysis with formal methods (probability, decision theory) and insights from cognitive science and social sciences. Key research areas include truthlikeness, Bayesian reasoning, reverse inference in neuroscience, and the methodology of social sciences. Current projects include the PRIN project on reasoning with hypotheses and a John Templeton Foundation grant on intellectual humility in healthcare communication. He teaches courses on philosophy of science, critical thinking, and ethics at IMT Lucca, and is involved in editorial roles for journals like The Reasoner . He also chairs the Italian Association of Cognitive Sciences (AISC).
Pieralberto Guarniero is a Lecturer in the Department of Decision Sciences at Bocconi University, specializing in computational statistics and innovative educational approaches. He obtained his PhD in Computational Statistics from the University of Warwick. His academic work focuses on: Developing particle filtering methods for Bayesian inference Integrating technology to enhance higher education pedagogy Creating effective science communication strategies Designing innovative learning experiences for statistics education Dr. Guarniero teaches multiple courses including Statistics, Data Analysis, and Applied Stochastic Processes, employing creative pedagogical approaches to complex quantitative material. His research in computational statistics has produced novel algorithms for sequential Monte Carlo methods, with applications in state-space modeling.
Dr. Francesco Fontanella is an Associate Professor at the Department of Electric and Information Engineering (DIEI) at the University of Cassino and Southern Lazio. His research focuses on evolutionary computation, machine learning, and their applications in neurodegenerative disease detection (e.g., Alzheimer’s and Parkinson’s), cultural heritage preservation, and environmental monitoring. He leads the AIDA Lab and has contributed to major initiatives like the HAND project for neuromuscular disease diagnosis and the C4E environmental monitoring system. Fontanella holds editorial roles in journals such as Pattern Recognition Letters , Genetic Programming and Evolvable Machines , and Applied Soft Computing . He has organized international workshops including the XVI International Workshop on Artificial Life and Evolutionary Computation (2022) and chairs the IEEE Computational Intelligence Society’s Task Force on Evolutionary Computer Vision. His teaching responsibilities include courses on computer science fundamentals, operating systems, and cybersecurity at both undergraduate and graduate levels. Notable projects include developing handwriting analysis systems for medical diagnostics and AI-driven tools for medieval manuscript analysis. Fontanella’s work bridges computational techniques with real-world challenges, emphasizing interdisciplinary collaboration between computer science and fields like healthcare, archaeology, and environmental science.
