جست‌وجوی استاد و پژوهشگر

Pedro Galeanoمشاهده پروفایل

دانشیار

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +۳ مورد دیگر

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.

• Financial Econometrics
• Statistics
• Empirical Asset Pricing
• +۵ مورد دیگر

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

• Chronic Obstructive Pulmonary Disease (COPD)
• Respiratory Epidemiology
• Physical Activity and Mobility
• +۵ مورد دیگر

Judith Garcia-Aymerich is a Professor at Pompeu Fabra University and Secretary General of the European Respiratory Society (2022-2025), affiliated with ISGlobal (Barcelona Institute for Global Health). Her work bridges academic research, clinical practice, and global health policy in respiratory epidemiology. Her academic credentials include: Graduated in Medicine from University of Barcelona (UB), 1996 PhD in Public Health and Biomedical Research Methodology from Autonomous University of Barcelona (UAB), 2002 Dr. Garcia-Aymerich pioneers research on COPD pathophysiology through the lens of physical activity, mobility, and socio-environmental determinants. Her methodological innovations include developing activity monitors, patient-reported outcomes tools, and causal inference frameworks using life course epidemiology. She examines how urban environments, body composition, and dietary patterns interact with respiratory health across populations, emphasizing rigorous hypothesis-free disease characterization. Her 300+ publications (H-index 68) reveal consistent focus on COPD phenotyping, physical activity measurement tools (notably PROactive instruments), and urban health interventions. Recent work integrates patient voice in mobility frameworks and examines childhood body composition impacts on lung development, demonstrating translational impact through updated clinical guidelines. She has received multiple national and international awards for pioneering COPD lifestyle research, though specific honors aren't detailed in source materials. As principal investigator of numerous multicenter cohort studies and clinical trials, she secures significant research funding while supervising 12 doctoral theses and mentoring 7 postdocs. Her teaching portfolio at Pompeu Fabra spans epidemiology, research methods, and chronic disease management across undergraduate and graduate programs. At ISGlobal, she leads collaborative teams developing patient/clinician tools and advising regulatory bodies, with ongoing projects examining environmental impacts on respiratory health through European Respiratory Society initiatives.

• Mathematical Optimization
• Statistical Modeling
• Machine Learning
• +۱۳ مورد دیگر

Vanesa Guerrero Lozano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Flores de Lemus Institute and the UC3M-Santander Big Data Institute. Her work bridges mathematical optimization, statistical modeling, and data science, with applications across disciplines including biomedicine, fluid mechanics, and sustainable development. Her research focuses on developing advanced statistical methodologies using mathematical optimization. Key interests include shape-constrained regression, P-splines smoothing, sparse modeling, clustering of categorical data, and interpretable machine learning. She applies these techniques to complex datasets in turbulence modeling, biological age imputation, and pandemic forecasting. The recent publications reveal a strong trend in integrating optimization techniques with statistical learning, particularly in nonparametric and semiparametric models. There is a consistent emphasis on interpretability, robustness, and scalability, especially for high-dimensional and dynamic datasets. Applications span from fluid dynamics to public health, demonstrating interdisciplinary impact. Scientific Awards and Recognition: Ayuda adicional within the Juan de la Cierva Incorporación Program (2020), awarded by the State Research Agency (AEI) Research Leadership and Advising: She has served as principal investigator on multiple competitive research projects funded by national and regional agencies, including the State Research Agency (AEI), the Jacques Hadamard Mathematical Foundation, and the Community of Madrid. Her projects cover topics such as constrained additive models, machine learning for sustainable fishing, turbulence control, and ADHD diagnosis using data science. She has supervised at least one doctoral thesis on constrained smoothing models, indicating active mentorship in methodological statistics and optimization. Laboratories and Research Groups: She is a member of the Energy Analytics research group and conducts her work within the UC3M-Santander Big Data Institute, which supports interdisciplinary data science research. Her affiliation with the Flores de Lemus Institute further underscores her engagement with advanced statistical and computational methodologies.

Shuangning Liمشاهده پروفایل

استادیار

• Causal Inference
• Multiple Hypothesis Testing
• Selective Inference
• +۱ مورد دیگر

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Her research focuses on advanced statistical methodologies, including causal inference, multiple hypothesis testing, selective inference, and statistical reinforcement learning. Prior to joining Booth, she held a postdoctoral fellowship at Harvard University's Department of Statistics. Education: PhD in Statistics from Stanford University, advised by Emmanuel Candès and Stefan Wager Bachelor of Science from the University of Hong Kong Her academic work intersects with computational statistics and machine learning, addressing challenges in high-dimensional inference and adaptive data analysis. While specific publications are not listed in the provided text, her research trends emphasize robustness and interpretability in statistical learning frameworks.

Thomas Verdeboutمشاهده پروفایل

استاد

• Directional Statistics
• High-dimensional Statistics
• Nonparametric Methods
• +۳ مورد دیگر

Thomas Verdebout is a Full Professor of Statistics at Université libre de Bruxelles (ULB), Belgium. He received his PhD in Statistics from ULB in 2008 and served as an Assistant Professor at Université de Lille, France (2009–2014). His research focuses on directional statistics, high-dimensional statistics, nonparametric methods, and dimension reduction techniques. He has contributed significantly to the development of rank-based tests and optimal transport-based approaches in statistical inference. Verdebout’s work emphasizes asymptotic theory, with notable contributions to hypothesis testing on hyperspheres, rotational symmetry analysis, and multivariate sign-based methodologies. He has held editorial roles at journals like Bernoulli and Electronic Journal of Statistics . His recent research explores the intersection of high-dimensional data analysis and nonparametric techniques, addressing challenges in spherical uniformity testing and eigenvalue-based hypothesis testing under elliptical models. Key themes in his publications include robust statistical testing, optimality properties of directional methods, and applications of optimal transport in directional data analysis. Despite the breadth of his work, Verdebout maintains a strong focus on foundational statistical theory with practical implications for modern data analysis challenges.

• Econometrics
• Time Series Analysis
• Macroeconomics
• +۵ مورد دیگر

Carlos Velasco is a Professor of Fundamentals of Economic Analysis at Universidad Carlos III de Madrid (UC3M) since 2007, with prior academic appointments at the University of Oxford and Universitat Autònoma de Barcelona. He earned his PhD from the London School of Economics, establishing the foundation for his expertise in econometric theory. His academic career includes positions as Junior Lecturer in Statistics at Oxford, Full Professor in Statistics and Econometrics at UC3M, and ICREA Research Professor in Economics at UAB. Professor Velasco's research program centers on dynamic econometric models and economic time series analysis, with significant applications in macroeconomics and finance. His methodological contributions span time series analysis, dynamic modeling, persistence phenomena, predictability frameworks, and causality testing, addressing both theoretical challenges and practical economic questions. Analysis of his publication record reveals consistent innovation in econometric methodology, particularly in handling complex time series properties including noninvertibility, noncausality, and heteroskedasticity. His work demonstrates increasing sophistication in model estimation techniques while maintaining strong connections to substantive economic phenomena like exchange rate dynamics and panel data structures. Professional recognition: Fellow of the Journal of Econometrics (2010) Professor Velasco has supervised 8 doctoral theses and directed multiple research projects, including the MAD-ECO network. His editorial service for leading journals such as Econometric Theory and Journal of Econometrics reflects his standing in the field. He serves as Associate Editor for Econometric Theory, Journal of Time Series Analysis, Journal of Time Series Econometrics, TEST, and the Spanish Economic Review. While specific laboratory facilities aren't detailed, his leadership of the MAD-ECO network indicates active participation in collaborative research frameworks focused on economic modeling, suggesting ongoing contributions to methodological advancements with practical economic applications.

• Biomedical Engineering
• Signal Processing
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Rubén Martín Clemente is a Professor in the Department of Signal Theory and Communications at the University of Seville, Spain. His research focuses on biomedical engineering, signal processing, and artificial intelligence, with significant contributions to blind source separation (BSS), fetal electrocardiogram (ECG) extraction, and EEG analysis for cognitive neuroscience. He leads the Biomedical Engineering research group and has participated in numerous national and European projects, including IAREPRO II (AI in assisted reproduction), MARCO-BOLO (coastal biodiversity monitoring), and PID2022-138590OB-C22 (wind turbine structural monitoring). Research Themes : Machine learning for biomedical signal processing, ICA algorithms, cognitive neuroscience applications, and IoT systems for infrastructure monitoring Technical Leadership : Developed RFID-based traffic sign inventory systems and adaptive algorithms for nonlinear source separation His work spans neuroimaging data analysis (e.g., Scientific Reports 2022), energy prediction models (IEEE Access 2023), and smart city applications . Key publications include foundational work on ICA in Springer (2014) and CRC Press (2013). He holds patents for wireless highway inventory systems and has mentored doctoral theses on ICA and image processing. Selected Scientific Contributions 2011: Fetal ECG extraction techniques (IEEE Transactions on Biomedical Engineering) 2022: Reliability analysis of visual processing EEG (Behavioral Brain Research) 2017: L1-PCA/ICA connections (IEEE TPAMI)

Juan Carlos Pardo Fernandezمشاهده پروفایل

استاد

• Nonparametric regression
• Comparison of regression curves
• Goodness-of-fit tests in regression
• +۴ مورد دیگر

Juan Carlos Pardo Fernandez is a Professor at the University of Vigo, Spain, where he is affiliated with the Department of Statistics and Operational Research within the School of Industrial Engineering. He also maintains connections with the Faculty of Economic and Business Sciences and is an active member of the SiDOR (Statistical Inference, Decision and Operational Research) research group. His academic work is further supported by affiliations with the Biomedical Research Center and CITMAga (Interuniversity Research Center). His research interests span across multiple statistical domains including nonparametric regression, comparison of regression curves, goodness-of-fit tests in regression, survival analysis, bootstrap methods, and ROC curves. His work demonstrates a strong focus on both theoretical statistical development and practical biomedical applications, particularly evident in his textbook "Biostatistics for Biomedical Engineering" published in 2023. His recent publications (2023-2025) show consistent productivity with articles appearing in prestigious journals like Annals of the Institute of Statistical Mathematics, Statistical Methods in Medical Research, and TEST. Analysis of his publication trends reveals a deep commitment to advancing statistical methodology for biomedical applications, with particular emphasis on nonparametric approaches, regression analysis, and diagnostic testing methods. His work bridges theoretical statistics with practical healthcare applications, demonstrating both mathematical rigor and clinical relevance. Professor Pardo Fernandez teaches courses including Specification contrasts (for the Master's in Statistical Techniques) and Biostatistics (for the Biomedical Engineering Degree). He has developed several educational Shiny applications to enhance student understanding of statistical concepts such as ROC curves, empirical distribution functions, and statistical power. His teaching approach integrates practical data analysis with theoretical foundations, preparing students for real-world statistical challenges in biomedical contexts. His research group SiDOR and affiliation with CITMAga provide collaborative frameworks for his work, connecting him with interdisciplinary researchers across multiple institutions. His contributions to statistical methodology continue to advance both theoretical understanding and practical applications in biomedical research.

Evdokimov Kirillمشاهده پروفایل

دانشیار

• Econometrics
• Measurement Error Models
• Nonparametric Estimation
• +۴ مورد دیگر

Kirill Evdokimov is an Associate Professor of Economics at Universitat Pompeu Fabra and a Ramón y Cajal Researcher affiliated with the Barcelona School of Economics. He holds a PhD from Yale University. His research focuses on econometric methodologies addressing measurement errors, panel data modeling, and nonparametric/semiparametric estimation. Key contributions include advancements in handling endogeneity, robust inference, and instrumental variables analysis. His work spans theoretical econometrics and applied statistical techniques. Evdokimov's academic career includes roles at leading institutions, with a strong emphasis on methodological innovation. His recent research emphasizes nonstandard inference in measurement error models, heterogeneous treatment effects in instrumental variable frameworks, and robust estimation under weakly dependent data. He collaborates frequently with scholars like Andrei Zeleneev and Michal Kolesár. Notable outputs include high-impact papers in Econometrica , Journal of Econometrics , and Econometric Theory . His work integrates theoretical contributions with practical econometric tools, including software development for empirical likelihood estimators. Evdokimov maintains a Google Scholar profile and actively publishes working papers addressing frontier topics in modern econometrics.

• Statistics
• Optimization
• Biostatistics
• +۴ مورد دیگر

Jose Ameijeiras Alonso is a Professor at the University of Santiago de Compostela, affiliated with the Department of Statistics, Mathematical Analysis and Optimization within the Higher Technical School of Engineering. His research focuses on statistical methodology, optimization, and applications in health, environmental, and spatial data analysis. He holds a PhD in Statistics from the University of Santiago de Compostela (2017), specializing in density estimation and mode assessment. Key research interests include nonparametric estimation techniques, circular data analysis, and epidemiological modeling. Notable contributions include the development of the Multimode R package for mode detection and the SPQEIR model for assessing epidemic suppression strategies. His work bridges theoretical statistics with practical applications in wildfire analysis, viral disease studies, and bioinformatics. Recent publications (2021–2024) emphasize circular statistics, multimodality tests, and clinical applications in ME/CFS and multiple sclerosis. He collaborates with institutions like the Galician Mathematical Research and Technology Center (CITMAga) and leads the MODESTYA research group, focusing on decision models and statistical applications.

• Biological Dosimetry
• Statistical Modeling
• Count Data Analysis
• +۴ مورد دیگر

Pedro Puig Casado is an Associate Professor in the Department of Mathematics at Universitat Autònoma de Barcelona (UAB). He holds a Ph.D. and Degree in Mathematical Sciences from UAB (1997 and 1982, respectively). His research focuses on advanced stochastic modeling, particularly in biological dosimetry, count data analysis, Bayesian methods, and extreme event risk assessment. He has led multiple interdisciplinary projects funded by institutions like the European Regional Development Fund and the Spanish Ministry of Economy and Competitiveness. Key projects include statistical modeling of extreme events and health risks (2023-2026), and prior initiatives on environmental/technological health risks (2016-2022). His work integrates statistical methodologies with applications in radiation biology, epidemiology, and environmental science. Recent publications address population estimation in ecological studies, R-based dosimetry tools, and Bayesian approaches to non-stationary time series analysis. He has supervised 9 theses and contributed to datasets like Biodose Tools for radiation dosimetry. His collaborative networks span international institutions, reflecting his expertise in applied statistics with societal impact aligned to UN Sustainable Development Goals in education and health.

• Statistics
• Nonparametric Statistics
• Survival Analysis
• +۵ مورد دیگر

Jacobo de Uña Álvarez is a full-time University Professor at the University of Vigo, Spain. He is affiliated with the School of Industrial Engineering in the Department of Statistics and Operational Research. His research focuses on nonparametric statistics, survival analysis, and statistical methods for censored and truncated data. He leads the SiDOR (Statistical Inference, Decision and Operational Research) research group and is associated with the Biomedical Research Center and CITMAga interuniversity research center. Dr. de Uña Álvarez earned his doctorate from the University of Santiago de Compostela in 1998 with a thesis titled "Statistical Inference under Proportional Censorship Models," supervised by Dr. Wenceslao González Manteiga. His academic journey has established him as a leading researcher in statistical methodology for complex data structures. His research interests center on nonparametric and semiparametric statistical methods, particularly for survival analysis with censored and truncated data. He has made significant contributions to the development of statistical techniques for doubly truncated data, length-biased sampling, and illness-death models. His work bridges theoretical statistics with practical applications in biomedicine and engineering. Analysis of his recent publications reveals a strong focus on methodological advances in handling complex data structures, particularly doubly truncated data. His research shows consistent progression from foundational work on nonparametric estimation to sophisticated methods for high-dimensional data and complex sampling schemes. The interdisciplinary nature of his work is evident in applications to biomedical research and genomics. Dr. de Uña Álvarez has mentored numerous researchers through collaborations, with extensive co-authorship networks. His work has been supported by various research grants focused on statistical methodology development, though specific grant details are not provided in the available information. He leads the SiDOR research group at the University of Vigo, which focuses on Statistical Inference, Decision Theory, and Operational Research. The group maintains strong collaborations with international researchers and institutions, contributing significantly to the advancement of nonparametric statistical methods.

Eduardo García-Portuguésمشاهده پروفایل

دانشیار

• Directional Statistics
• Nonparametric Statistics
• Functional Data Analysis
• +۶ مورد دیگر

Eduardo García-Portugués is an Associate Professor at the Department of Statistics, Carlos III University of Madrid. He holds a PhD in Statistics and Operations Research from the Universidade de Santiago de Compostela (2014). Previously, he was a Postdoctoral Fellow at the University of Copenhagen's Department of Mathematical Sciences (2015–2016) and an Assistant Professor at UC3M (2016–2021). He serves as an Associate Editor for Journal of Computational and Graphical Statistics , Annals of the Institute of Statistical Mathematics , and other journals. His research focuses on statistical methodologies for complex and non-Euclidean data, emphasizing directional statistics, kernel smoothing, and functional data analysis. He develops nonparametric and computational tools for analyzing hyperspherical, toroidal, and circular data, with applications in fields like finance and bioinformatics. His work includes advancements in goodness-of-fit tests, diffusions on manifolds, and optimal stopping problems. He leads the Statistics Reading Club and has secured grants for projects such as STENED (Stein-based goodness-of-fit tests for non-Euclidean data). He advises multiple PhD students, including Diego Serrano, Vinícius Litvinoff, and Paul Axmann. His contributions span theoretical developments and practical implementations, including R packages like rotasym and sphunif for directional data analysis.

Juan Carlos Vidal Aguiarمشاهده پروفایل

دانشیار

• Process Mining
• Business Intelligence
• Semantic Annotation
• +۵ مورد دیگر

Juan Carlos Vidal Aguiar serves as an Associate Professor in the Department of Electronics and Computer Science at the University of Santiago de Compostela, Spain. With extensive experience in academic research and teaching, he has established himself as a prominent figure in process mining and business intelligence applications. His work bridges theoretical computer science with practical implementations across healthcare and educational domains. Dr. Vidal Aguiar earned his Bachelor Engineering degree in Computer Science from the University of La Coruña in 2000, followed by several years working as a senior IT consultant. He completed his PhD at the University of Santiago de Compostela in 2010, where he has remained as faculty since. His academic journey reflects a trajectory from foundational computer science toward specialized applications in process analytics. His research interests focus on knowledge discovery, semantic annotation, semantic modeling of workflows and services, and the application of artificial intelligence for business intelligence . Recent work demonstrates significant evolution toward healthcare applications, particularly in cardiac rehabilitation and glucose monitoring, while maintaining strong foundations in process mining techniques. His publications reveal a clear progression from theoretical workflow modeling to practical AI-driven business process solutions with real-world impact. Analysis of his publication trends shows increasing specialization in predictive process monitoring with deep learning approaches, particularly evident in his 2023-2025 work. His research spans both theoretical contributions in kernel methods and biclustering algorithms, and practical implementations like the VERONA Python library for benchmarking. The interdisciplinary nature of his work is particularly notable in healthcare applications where process mining techniques are adapted for medical contexts. Dr. Vidal Aguiar leads multiple significant research initiatives including Predictive monitoring and causality for cardiac rehabilitation, Responsible AI for Process Mining 2.0, GAMification techniques for entrepreneurial teacher development, and Soft computing for gamification analytics in cardiac rehabilitation . These projects demonstrate his ability to secure research funding across diverse domains while maintaining a cohesive research vision centered on process analytics. His research ecosystem includes collaborations with numerous colleagues including Manuel Lama, Pedro Gamallo-Fernandez, and Marcos Matabuena across various projects. The SoftLearn platform represents one of his notable contributions to educational technology, applying soft computing techniques to process mining in e-learning contexts. His work consistently bridges academic research with practical implementations that address real organizational challenges.