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Karina GibertView profile

Professor

• Data Science
• Artificial Intelligence
• Ethics in AI
• +7 more

Karina Gibert is a Full Professor at Universitat Politècnica de Catalunya (UPC), specifically at the Faculty of Computer Science of Barcelona (FIB) in the Department of Statistics and Operations Research. With a permanent teaching position since 1990, she contributes to research and education with a focus on Data Science, Artificial Intelligence, Explainable AI, and Ethics in AI. Full Professor, UPC (since 1990) PhD in Informatics Engineering Postgraduate in Higher Education Teaching Director and co-founder of IDEAI research center Dean of the Official Professional College on Informatics Engineering of Catalonia Active in bridging the gender gap in STEAM through multiple initiatives Her research focuses on extracting strategic knowledge from data and intelligent systems with ethical and explainable perspectives. She has led various projects including Diet4You for personalized diets, INSESS-COVID19 for social vulnerability analysis, Top Rosies Talent for female AI development, and ciutadanIA for AI culture promotion. Her work spans health, environment, sustainability, tourism, and social technology applications. As an editor of Environmental Modeling and Software journal and active academic, she contributes to international conferences, working groups, and research collaborations. Her service includes membership on various ethics and AI strategy committees and advisory boards. Women Tech Award 2023 National Informatics Engineering Award in Digital Dissemination 2022 Ada Byron Award 2022 Honorific Mention of Creu Casas award 2021 donaTIC 2018 Award Finalist at AMETIC Awards 2021 Finalist of European Social Services Awards 2021

• Statistical Modeling
• Biostatistics
• Biomedicine
• +7 more

Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.

• High-dimensional statistics
• Bayesian statistics
• Statistical analysis of complex data
• +4 more

David Rossell is an Associate Professor at the Department of Economics, Universitat Pompeu Fabra (UPF) in Barcelona, Spain. He is affiliated with the Statistics@UPF research group and directs the Master in Data Science at the Barcelona School of Economics (BSE). Previously, he held positions at IRB Barcelona as head of the Biostatistics Unit and at the University of Warwick's Statistics Department. He obtained his PhD in Statistics from Rice University, Houston (USA), and conducted postdoctoral research at M.D. Anderson Cancer Center under Professors Valen Johnson and Veera Baladandayuthapani. Research Interests: Rossell specializes in high-dimensional statistical inference, Bayesian methods, computational statistics, and applications in biomedicine and social sciences. His work emphasizes methodology for complex data integration, variable selection, graphical models, and experimental design. Key areas include non-local priors, scalable Bayesian computation, and the development of R packages for statistical analysis (e.g., casper , chroGPS , gaga ). Publications: His recent work focuses on advancing Bayesian variable selection, graphical models with external data, and causal inference. Themes include leveraging external datasets for improved model accuracy, robustness to model misspecification, and applications in healthcare and complex mixture analysis. His contributions span methodological innovation and computational tools for high-dimensional problems. Funding & Grants: Rossell has secured funding through Spanish and European grants, including Juan de la Cierva Fellowships, AGAUR fellowships, and Marie Slodowska-Curie Actions. He supports PhD and postdoctoral researchers through programs like La Caixa InPhD and Beca Beautriu de Pinós. Labs & Teams: He leads the BSE Data Science Center and contributes to interdisciplinary collaborations at UPF and IRB Barcelona, bridging statistical theory and practical applications in genomics, epigenomics, and health data analysis.

• Innovation Management
• Organizational Innovation
• Quality Management
• +12 more

Josep Llach is a Senior Lecturer at the Barcelona School of Management, Universitat Pompeu Fabra, and a part-time Professor Agregat in the Department of Business Administration, Management and Product Design at the University of Girona. He is an active researcher in innovation management, organizational innovation, sustainability, and operations, with extensive involvement in European and national research projects. University: University of Girona Department: Department of Business Administration, Management and Product Design Secondary Affiliation: Barcelona School of Management, Universitat Pompeu Fabra Academic Rank: Senior Lecturer Part-Time: Yes His research focuses on the intersection of innovation, quality, and sustainability in manufacturing and service firms. He studies how lean practices, digital transformation, circular economy strategies, and green technologies impact firm performance. His work often employs advanced statistical methods such as structural equation modelling and fuzzy-set qualitative comparative analysis. The recent publications highlight a strong trend toward sustainability, digital transformation, and methodological robustness in empirical models. His work spans industries including manufacturing, hospitality, and education, with a methodological emphasis on survey-based research and configurational analysis. Scientific Awards: Emerald Literati Awards – Most Outstanding Paper in 2018 for 'Creating value through the balanced scorecard: how does it work?' Top Downloaded Paper 2018–2019: 'Socially responsible companies: Are they the best workplace for millennials?' Josep Llach has directed five doctoral theses and actively mentors PhD students. He collaborates with the Catalan University Quality Assurance Agency (AQU) as a methodological secretary in accreditation processes. His research is supported by multiple national and European grants, particularly through participation in the European Manufacturing Survey. He is a member of the Grup de Recerca Avançada sobre Dinàmica Empresarial i Impacte de les Noves Tecnologies a les Organitzacions (GRADIENT), a research group focused on organizational dynamics and the impact of new technologies.

Frank NIELSENView profile

Professor

• Data science
• Geometric science of information
• Information geometry
• +4 more

Frank NIELSEN is a Professor at École Polytechnique with expertise in information geometry, data science, and machine learning. He holds a PhD (1996) and HDR (2006) in computer science and has established himself as a leading researcher in geometric approaches to information science. His educational background includes a PhD in computer science (1996) followed by a Habilitation à Diriger des Recherches (HDR) in 2006, the highest academic qualification in France that qualifies one to supervise doctoral candidates. Dr. NIELSEN's research focuses on the Geometric Science of Information , where he develops theoretical frameworks for understanding data through geometric and information-theoretic lenses. His work bridges Computational information geometry Statistical manifold theory Bregman divergences and their applications Machine learning with geometric foundations High-dimensional data analysis He aims to address the challenge of inappropriate data representation in current Data Science by building a theory of Computational Information Geometry to enable Intrinsic Data Science with principled distances. His extensive publication record shows a clear trend toward developing geometric frameworks for understanding statistical divergences, with recent work focusing on Bregman geometry, Fisher-Rao metrics, and their applications in machine learning. His research spans theoretical developments in information geometry to practical implementations like the pyBregMan Python library, demonstrating both theoretical depth and practical relevance. Dr. NIELSEN has made significant contributions through his teaching and publications. He has taught courses at École Polytechnique including INF442, INF517, and INF591. His authored textbooks include Introduction to HPC with MPI for Data Science (2016), A Concise and Practical Introduction to Programming Algorithms in Java (2009), and Visual Computing: Geometry, Graphics, and Vision (2005). He has also edited influential volumes such as Computational Information Geometry for Image and Signal Processing (2016) and Geometric Theory of Information (2014). He actively organizes and participates in academic events, serving on program committees for major conferences including GSI (Geometric Science of Information), CVPR, and ICCV. His work has established him as a key figure in the growing field of geometric approaches to information science.

Diego Hernan Villarejo GarciaView profile

Adjunct Professor

• Sports Science
• Rugby Performance Analysis
• Futsal Tactics
• +3 more

Diego Hernan Villarejo Garcia is an Adjunct Professor at the University of Murcia's Faculty of Sports Sciences, Department of Physical Activity and Sport, working part-time. He holds a PhD from the University of Murcia (2012) focused on technical and tactical differences between winning and losing teams in international men's rugby. His research interests include sports performance analysis, rugby tactics, futsal player evaluation, and gender studies in sports science. He is affiliated with the HUMSE (Human Movement and Sport Science) research group and previously contributed to UMUSPORT. Education: PhD in Sports Science (2012), Universidad de Murcia. Supervised by Dr. Enrique Ortega Toro and Dr. José Manuel Palao Andrés. Research focuses on multivariate statistical techniques applied to sports performance, positional roles in rugby, and the effectiveness of training programs. His work bridges theoretical and applied aspects of sports science, with a strong emphasis on elite team sports like rugby and futsal. Publications analyze game dynamics through statistical models, explore gender disparities in sports research funding, and evaluate stretching programs for athletes. He has contributed to systematic reviews on sensor technology in volleyball and youth basketball competition systems. No scientific awards are explicitly listed, though his prolific publication record reflects sustained academic engagement. He has advised on research projects related to sports education and graduate competencies across EU countries.

Pedro GaleanoView profile

Associate Professor

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +3 more

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

Andrea Meilán-VilaView profile

Assistant Professor

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +3 more

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Political Sociology
• Urban Sociology
• Social and Legal Sciences
• +10 more

Clemente Jesus Navarro Yañez is a Professor at Pablo de Olavide University , affiliated with the Centre for Urban Political Sociology and Policies and the SPL-UPO Political Sociology and Local Policies Research Group . He contributes to Political Sociology , Urban Sociology , and Comparative Politics through extensive publications and international collaborations. Key research domains include: Urban Regeneration: Methodological frameworks for evaluating urban policy impacts, notably in Andalusia Cultural Scenes: Analyzing how cultural consumption patterns drive local development Political Contention: Comparative studies of protest mechanisms in Southern Europe and Latin America His methodological expertise spans: Quasi-experimental area comparisons Multivariate regression analysis Protest event analysis protocols Cluster analysis of temporal dynamics Scientific contributions appear in Political Studies , European Urban and Regional Studies , and Social Forces , with a focus on: Political participation mechanisms Creative class localization Contextual determinants of municipal governance Comparative housing regime analysis

Alberto Maydeu OlivaresView profile

Professor

• Psychometrics
• Structural Equation Modeling
• Item Response Theory
• +3 more

Alberto Maydeu Olivares is a Professor at the Department of Clinical Psychology and Psychobiology, Universitat de Barcelona, within the Faculty of Psychology. He leads the Violence, Behavior, Individual differences and Technology Studies (WITS) research group. His expertise spans psychometrics, structural equation modeling (SEM), and item response theory (IRT). He holds a Licenciatura in Psychology from the Universitat de Barcelona (1988), a Doctorat in Quantitative Psychology from the same institution (1991), and advanced degrees from the University of Illinois, including a Ph.D. in Quantitative Psychology (1997) and masters in Mathematical Statistics and Quantitative Psychology. His research focuses on improving psychological measurement methods, evaluating model fit in SEM/IRT, and addressing methodological challenges in behavioral data. Notable projects include NSF-funded work on SEM fit indices (2017–2020) and AGAUR-supported studies on IRT applications (2014–2017). He teaches advanced courses like Multivariate Analysis and Structural Equation Models at the master’s level. Maydeu Olivares has authored influential papers on goodness-of-fit assessment, causal effect estimation via instrumental variables, and forced-choice test design. His work bridges statistical rigor with practical applications in psychology, addressing issues like faking in assessments and common method bias. He actively contributes to methodological advancements, emphasizing transparent and robust statistical practices.

• Survival Analysis
• Spatio-Temporal Modeling
• Bayesian Statistics
• +7 more

Maria Antonia Barceló Rado is a Professor at the Department of Economics, University of Girona, and a member of the Research Group on Statistics, Econometrics and Health (GRECS). She is also affiliated with the Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP) and collaborates with the Centre de Recerca en Economia i Salut (CRES) at Pompeu Fabra University. Her research spans methodological advancements in survival analysis, spatio-temporal modeling, Bayesian statistics , and applied work in clinical epidemiology, environmental epidemiology, cardiovascular risk factors, chronic diseases , and cancer epidemiology . She has developed a mixed-methods line combining Real World Data with Real World Evidence for epidemic prevention and detection. Licenciate in Mathematics (Universitat Autònoma de Barcelona, 1995) PhD in Mathematics (University of Girona, 2001) Accredited as Catedràtica d’Universitat (ANECA, 2018) Her recent publications focus on environmental stressors , air pollution effects , and spatio-temporal modeling across Spain. Articles address mental health in adolescents , vaccination equity , Covid-19 impacts , and chronic disease epidemiology . Collaborations include institutions like Johns Hopkins University, Pompeu Fabra University, and hospitals in Bellvitge and Barcelona.

• Time Series Analysis
• Energy Analytics
• Statistical Clustering
• +3 more

Dr. Andrés Modesto Alonso is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Energy Analytics research group and Flores de Lemus Institute. His work spans computer science, economics, and statistics through advanced time series analysis and energy forecasting methodologies. Primary affiliation: Department of Statistics, UC3M Research groups: Energy Analytics, Flores de Lemus Institute His research focuses on time series analysis , energy forecasting , and statistical clustering with applications in electricity markets, smart grids, and environmental data. Recent publications emphasize deep learning models for energy prediction, dynamic factor models, and market-based distance metrics. Scientific output trends show 15 recent articles (2018-2024) covering topics like: Electricity market price forecasting Smart grid optimization through clustering Adaptive control charts for industrial processes Precision matrix estimation in high-dimensional statistics Extreme value analysis for environmental monitoring Dr. Alonso has supervised multiple theses on time series modeling and classification techniques, while collaborating on grants related to stochastic optimization and responsible AI applications in economic forecasting.

• Financial Econometrics
• Macroeconomic Forecasting
• Stochastic Volatility Models
• +3 more

Maria Helena Lopes Moreira da Veiga is a Full Professor at Universidad Carlos III de Madrid, affiliated with the Macroeconomic and Financial Prediction and Analysis Research Group within the Statistics Department. Her work bridges economics, statistics, and financial engineering with focus on volatility modeling and macroeconomic forecasting. Principal investigator for geopolitical risk and financial contagion projects (2024-2026, 2019-2020) Key research areas: stochastic volatility models, energy markets, Bayesian methods, outlier detection Collaborates with institutions across Denmark, Portugal, France, Spain, and Canada Her publications demonstrate expertise in financial econometrics, particularly asymmetric volatility models and their applications in energy and stock markets. She employs advanced computational techniques like data cloning and wavelet analysis to address model uncertainty and risk measurement. Current projects focus on climate risk indices, AI-driven economic valuation, and high-dimensional time series prediction. She has received continuous funding from national agencies for uncertainty modeling in macroeconomic and financial contexts since 2006.

María Teresa Gómez MarcosView profile

Researcher

• Statistics
• Operations Research
• Artificial Intelligence Ethics
• +3 more

María Teresa Gómez Marcos serves as a Contracted Doctor within the Faculty of Insurance, Legal and Business Sciences and the Institute for European Studies and Human Rights, focusing on interdisciplinary research bridging quantitative methods and ethical governance frameworks. She earned her PhD from the University of Salamanca in 2021 with the thesis Multidimensional and multivariate quality of the Spanish University , supervised by Dr. Purificación Vicente Galindo and Dr. Helena Martín Rodero. Her academic foundation integrates advanced statistical modeling with institutional quality assessment. Her research spans Statistics, Operations Research, and AI ethics within European Union contexts, with specific emphasis on ethical governance of artificial intelligence and moral-legal theory. She actively contributes to two research groups: Moral, legal and political thought: foundations and projections The European Union and the ethical governance of artificial intelligence Her work demonstrates consistent focus on data-driven policy analysis and institutional quality frameworks in academia.

José E. ChacónView profile

Professor

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +3 more

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.