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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.

• Financial Econometrics
• Statistics
• Empirical Asset Pricing
• +5 more

Dacheng Xiu is the Joseph Sondheimer Professor of Econometrics and Statistics at the Booth School of Business , University of Chicago, and an Affiliated Faculty in the Department of Statistics. He serves as a Research Associate at the National Bureau of Economic Research and holds editorial roles at journals like Journal of Business & Economic Statistics and Journal of Financial Econometrics . PhD and MA in Applied Mathematics from Princeton University BS in Mathematics from University of Science and Technology of China His research focuses on statistical methodologies for financial data , including risk measurement , portfolio management , and empirical asset pricing using high-frequency data and machine learning . Recent work analyzes text data and large language models for economic forecasting. Editorial leadership includes Co-Editor and Associate Editor roles at top journals like Journal of Finance and Annals of Statistics . His lab ( Risk Lab ) specializes in systemic risk assessment through transaction-level data analysis. 2024 Dimensional Fund Advisors Prize 2023 GSU-RFS FinTech Conference Best Paper Award 2022 Society for Financial Econometrics Fellow 2018 Swiss Finance Institute Outstanding Paper Award

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.

• Nuclear Fusion
• Plasma Physics
• Magnetohydrodynamics (MHD)
• +3 more

Jose Miguel Reynolds Barredo is an Associate Professor and Director of the Doctorate in Plasmas and Nuclear Fusion at Carlos III University of Madrid. His research focuses on plasma physics, magnetohydrodynamics (MHD), and energy systems resilience. He leads studies on stellarator reactor design, plasma confinement optimization, and the integration of renewable energy into power grids. His work spans advanced MHD equilibrium solvers (e.g., SIESTA, FLIPEC) and fusion device optimization for ITER and Wendelstein 7-X. He also investigates climate impacts on renewable energy efficiency and power grid stability under high renewable penetration. Notable contributions include HVDC grid segmentation strategies and non-axisymmetric plasma transport modeling. Key Areas: Fusion reactor design, MHD stability, power grid resilience, climate-energy interactions Tools: SIESTA, FLIPEC, GENE, OPA cascading blackout model Projects: Doctorate in Plasmas and Nuclear Fusion, W7-X bootstrap current studies, climate-energy system interdependencies Research emphasizes computational plasma physics and interdisciplinary energy solutions, blending theoretical, numerical, and applied engineering approaches.

Salvador NayaView profile

Professor

• Statistics
• Operations Research
• Data Analytics
• +7 more

Salvador Naya is a Professor of Statistics and Operations Research at the University of A Coruña, affiliated with the Polytechnic School of Engineering (EPEF). He is a researcher in the Modeling, Optimization and Statistical Inference Group (MODES) and linked to the CITIC Research Center. His work focuses on statistical methodologies applied to maritime technology, energy efficiency, materials science, and interlaboratory studies. Recent activities include a keynote lecture on data and AI applications across domains (land, sea, air, space) at the Ferrol Industrial Campus. Research interests span statistical quality control, predictive modeling for naval engineering, thermal degradation analysis of biomaterials, and anomaly detection in energy systems. He collaborates with industry partners like Navantia Seanergies and has contributed to projects on Panama Canal vessel transit optimization, renewable energy, and waste-to-resource initiatives. His academic contributions include R package development (e.g., TTS, ILS) for statistical analysis in materials science and interlaboratory studies. He actively engages in education and industrial partnerships, emphasizing Industry 4.0 applications in maritime and construction sectors.

• 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.

• Extremal Combinatorics
• Probabilistic Combinatorics
• Discrete Probability
• +6 more

Patrick Morris is a postdoctoral researcher in the GAPCOMB group at Universitat Politècnica de Catalunya (UPC) in Barcelona, funded by a Marie Curie fellowship. He holds a PhD in Combinatorics and Graph Theory from Freie Universität Berlin (2021), a Master's from the same institution, and a 4-year MSci from the University of Bristol. His research focuses on Extremal and Probabilistic Combinatorics with applications to Discrete Probability, Number Theory, and Algebraic Group Theory. Notably, his work on canonical Ramsey theorems earned a Best Paper Award at LAGOS 2023. His studies often explore structural properties of graphs and hypergraphs under random perturbations, with emphasis on Hamiltonicity, bootstrap percolation, and transversal problems. Collaborations include projects with Prof. Guillem Perarnau and Prof. Tibor Szabó, yielding impactful results in combinatorial theory. Education: PhD in Mathematics (2021), Freie Universität Berlin Master's in Combinatorics & Graph Theory (Freie Universität Berlin) MSci in Mathematics (University of Bristol, 2015) Research Interests: Extremal Combinatorics Probabilistic Combinatorics Bootstrap Percolation Hypergraph Theory Random Graphs Ramsey-Turán Theory Key Contributions: Over 15 peer-reviewed papers in top journals (e.g., Random Structures & Algorithms , Journal of Combinatorial Theory ), with focus on Hamilton cycles, clique factors, and canonical Ramsey-type theorems. His work bridges combinatorial structure and probabilistic methods, often addressing open problems in graph theory.

• Statistics
• Computer Science
• Economics
• +6 more

Pedro Galeano San Miguel is an Associate Professor at the Department of Statistics, Universidad Carlos III de Madrid. He is affiliated with the Nonparametric Inference for Complex Data and its Applications (NICDA) research group and contributes to the Flores de Lemus Institute and UC3M-Santander Big Data Institute. His work bridges statistics, computer science, and economics with a focus on financial and high-dimensional data. Research Interests: Functional data analysis and outlier detection Bayesian nonparametric methods and stochastic volatility models Copula models for systemic risk and portfolio selection High-dimensional statistical inference and dynamic correlation Big data applications in economics and finance Publication Trends: His recent work (2024–2016) emphasizes copula models for financial risk, functional regression techniques with missing data, and Bayesian inference for high-dimensional time series. He explores systemic banking risks, volatility prediction, and correlation structure changes across economic and financial domains. Grants & Projects: He leads or contributes to projects on computational statistics for complex dependencies, big data customer network analysis, and multivariate asymmetric GARCH modeling, funded by institutions like the State Research Agency (AEI) and Banco Santander.

• Non-parametric Inference
• Regression Models
• Missing Data Analysis
• +2 more

Ana Perez Gonzalez is a researcher at the Department of Statistics and Operational Research, Faculty of Business Sciences and Tourism, University of Vigo. She earned her PhD from the University of Santiago de Compostela in 2003, focusing on non-parametric inference for regression models with missing responses. Her research centers on robust statistical methods for missing data, functional regression, and non-parametric estimation. Key collaborations with Wenceslao Gonzalez Manteiga, Graciela Boente, and Ana Bianco Contributions to wild bootstrap calibration, bandwidth selection, and imputation techniques Her work addresses applications in biomedical statistics, climate modeling, and spectrometric studies. Notable methodologies include goodness-of-fit tests for missing data models, principal component-based estimators, and asymptotic variance analysis.

Tomás Del Barrio CastroView profile

Professor

• Time Series Analysis
• Seasonality
• Applied Econometrics
• +2 more

Dr. Tomás Del Barrio Castro is a Full Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB) , where he has been affiliated since 2007. He holds a PhD in Economics (University of Barcelona, 1998) and a Degree in Economics (Autonomous University of Madrid, 1992). PhD in Economics, University of Barcelona (1998) Degree in Economic and Business Sciences, Autonomous University of Madrid (1992) His research focuses on Time Series Analysis , Seasonality , and Applied Econometrics , with methodological contributions to seasonal unit root testing and periodic integration. He has published in top journals including Econometric Theory , The Econometric Journal , and Journal of Time Series Analysis . A significant portion of his work examines temporal aggregation effects, persistent cycles, and mixed-frequency data challenges. Scientific Recognition : Econometric Theory Multa Scripsit Award (2014) As Principal Investigator since 2007, he leads competitive research projects in econometrics. His teaching spans undergraduate and graduate courses in Statistics and Econometrics, including Macroeconometrics and Time Series Analysis at UIB and University of Barcelona. Recent appointments include: 2025-26: Macroeconometrics (Double Degree in Economics & Tourism) 2025-26: Time Series Analysis (Master's in Big Data Analysis)

Fernando Castedo DoradoView profile

Associate Professor

• Forest Ecology
• Environmental Science
• Biological Modeling
• +3 more

Fernando Castedo Dorado is a University Associate Professor in the Department of Engineering and Agricultural Sciences at the School of Agricultural and Forestry Engineering, University of León. He holds a PhD in Forestry Engineering from Universidade de Santiago de Compostela (2004). Academic Background : Technical Forestry Engineer (1996) Forestry Engineer (1999) Doctorate in Forestry Engineering (2004) Research Focus : His work centers on biotic/abiotic risk modeling in forest ecosystems, including: Forest fuel dynamics (canopy/ground) and fire severity Growth loss from invasive pests (e.g., Asian chestnut gall wasp) Climate-productivity relationships in eucalyptus and pine stands IoT-based forest health monitoring Article Trends : Recent publications emphasize invasive pest interactions, climate change impacts on forest productivity, fuel load modeling using LiDAR/Sentinel-2, and citizen science applications for damage reporting. Professional Roles : Currently coordinates the interuniversity MasterFUEGO program in forest fires. Previously held teaching roles at the University of León (2002-2008). International Experience : Conducted postdoctoral research at: USDA Forest Service (2004) Georg-August-Universität Göttingen (2005) Universidade de Trás-Os-Montes (2011) Universidad de La Plata (2023) Research Groups : Active member of DRACONES (modeling complex forest environments) and collaborator with UXAFORES at Universidade de Santiago de Compostela.

• Econometrics
• Nonlinear Time Series Analysis
• Chaos Theory in Economics
• +7 more

JORGE BELAIRE FRANCH is an Associate Professor in the Department of Economic Analysis at the Faculty of Economics, University of Valencia, Spain. His academic career is centered on econometric theory and its application to economic and financial time series, with a focus on nonlinearity, chaos, and structural breaks. His research interests lie primarily in nonlinear time series econometrics , including chaos testing, time reversibility, variance ratio tests, and unit root analysis in the presence of structural breaks. He has made significant contributions to the methodological development and empirical application of tests for chaos, market efficiency, and asymmetry in macroeconomic and financial data. His work spans financial markets , real estate economics , exchange rate behavior , and labor market dynamics . The most recent publications show a consistent trajectory in analyzing complex dynamics in economic data, particularly through nonparametric and nonlinear methods. Themes include chaos detection in stock markets, persistence changes in inflation and prices, and asymmetries in unemployment. His methodological rigor is evident in replication studies and power analyses of econometric tests. Jorge Belaire Franch completed his PhD at the University of Valencia in 1999 with a thesis on applied nonlinear econometrics, supervised by Dr. Dulce Contreras Bayarri. He has contributed extensively to the literature through journal articles and methodological notes. He has supervised doctoral research, as evidenced by his role as advisor to Dr. Dulce Contreras Bayarri's PhD student. While no specific grants or funded projects are mentioned, his sustained publication output suggests active research engagement. There is no information available about labs, research teams, or collaborative groups he may lead or participate in.

• Bayesian Statistics
• Statistical Modeling
• Operations Research
• +7 more

David Valentín Conesa Guillén is a Full Professor in the Department of Statistics and Operations Research within the Faculty of Mathematics at the University of Valencia. He is an active researcher and a key member of the Valencia Bayesian Research Group (VABAR), focusing on advanced statistical methodologies and their applications. His primary research interests encompass Bayesian Statistics , Statistical Modeling , Operations Research , Epidemiological Modeling , Spatial Statistics , and Ecological Statistics . His work bridges theoretical development with practical applications in public health, environmental science, and financial systems. The trends in his recent publications reveal a strong emphasis on developing and applying Bayesian hierarchical models to complex real-world problems. Key areas include dynamic forecasting of influenza outbreaks, modeling spatial distributions of ecological and bioclimatic data, correcting biases in ecological survival estimates, and analyzing efficiency in sectors like banking and higher education. His methodology frequently involves advanced computational techniques like the Integrated Nested Laplace Approximation (INLA). PhD in Statistics, University of Valencia (2000) Thesis: "Inferencia y predicción en colas con ingresos o servicios en grupos" Supervised by Dr. Carmen Armero Cervera David Conesa has supervised academic work, as indicated by his role as a thesis advisor. His research has been supported by collaborative projects, leading to publications in top-tier journals such as Bayesian Analysis , European Journal of Operational Research , and Journal of Agricultural, Biological, and Environmental Statistics . He has extensive collaborations with researchers across Spain and internationally. His research is conducted within the VABAR (Valencia Bayesian Research Group) , a collaborative team dedicated to Bayesian methodology and its applications. This group provides a platform for interdisciplinary projects and the training of future statisticians.

• Time Series Analysis
• Seasonality Modeling
• Applied Econometrics
• +2 more

Tomás Del Barrio Castro is a University Professor in the Department of Economics at the University of the Balearic Islands (UIB). He has been a faculty member at UIB since 2007, previously serving as a professor at the University of Barcelona from 1993 to 2007. His academic career includes positions as assistant professor (1993-1995), full professor at university school (1995-2001), and full professor at university (2001-2007) at the University of Barcelona. Dr. Del Barrio Castro was born in Madrid in 1968. He received his Bachelor's degree in Economics and Business Administration from the Autonomous University of Madrid in 1992 and completed his PhD in Economics from the University of Barcelona in 1998. He has also been a visiting researcher at the University of Manchester (UK, 2003) and Banco de Portugal (Portugal, 2013). His primary research interests focus on Time Series Analysis (Econometric), Seasonality Modeling, and Applied Econometrics. Dr. Del Barrio Castro has made significant contributions to the understanding of seasonal unit roots, periodic integration, and time series econometrics. His work has advanced methodologies for analyzing economic data with seasonal patterns and has provided new insights into the properties of various econometric tests under different data generating processes. Dr. Del Barrio Castro's publication record demonstrates a consistent focus on time series econometrics, particularly in the areas of seasonal unit root testing, periodic integration, and the properties of econometric tests in the presence of various data characteristics. His work spans theoretical developments and practical applications, with publications in top econometrics journals including Econometric Theory, Journal of Time Series Analysis, and Econometric Reviews. Among his notable achievements is the Econometric Theory Fine Scripsit Award received in 2014. Additionally, he has earned recognition for 4 research sections by CNEAI. These honors reflect the high quality and impact of his research contributions to the field of econometrics. Dr. Del Barrio Castro has served as principal investigator for Competitive Research Projects since 2007. He is currently the principal investigator of the "Econometría y Ciencia de Datos (ECD)" consolidated R&D group at UIB. His teaching portfolio includes courses such as Macroeconometrics, Time Series Analysis, and Economics Thesis supervision for both undergraduate and graduate programs at UIB. He is actively involved in the "Econometría y Ciencia de Datos (ECD)" research group, where he leads research efforts in econometrics and data science. This group focuses on advancing methodologies in time series analysis, econometric modeling, and the application of these techniques to economic and business problems.