جستجوی استاد راهنمای دانشگاهی

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +۷ مورد دیگر

Nicholas Polson is the Robert Law, Jr. Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His academic career centers on Bayesian statistics with applications in financial econometrics and machine learning. Polson's research interests span Bayesian statistics, financial econometrics, Markov chain Monte Carlo methods, particle learning, and deep learning applications in finance. His work has significantly contributed to understanding stochastic volatility models and developing new algorithms for Bayesian inference. He has pioneered applications of deep learning in asset pricing, portfolio management, and financial prediction, demonstrating how neural networks can detect complex patterns invisible to traditional financial models. His recent publication trends reveal a strong focus on integrating deep learning with financial econometrics, particularly in developing characteristics-sorted factor models, portfolio optimization techniques, and explaining the performance differences between active and passive investment strategies. His work consistently bridges theoretical statistical methods with practical financial applications, with a particular emphasis on nonlinear modeling and high-dimensional data analysis. His article 'Bayesian Analysis of Stochastic Volatility Models' was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics Polson teaches courses including 'Bayes, AI and Deep Learning' and 'Business Statistics' at Chicago Booth, with scheduled offerings for both 2024-2025 and 2025-2026 academic years. His work has been featured in Chicago Booth Review, where he has contributed insights on statistical analysis in chess, machine learning applications in money management, and the odds of cheating in competitive settings. His research demonstrates the powerful intersection of Bayesian statistics, financial modeling, and modern machine learning techniques.

• Bayesian Statistics
• Financial Econometrics
• Markov Chain Monte Carlo
• +۵ مورد دیگر

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.

• High-dimensional statistics
• Bayesian statistics
• Statistical analysis of complex data
• +۴ 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.

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

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.

• Survival Analysis
• Spatio-Temporal Modeling
• Bayesian Statistics
• +۷ مورد دیگر

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.

• Financial Econometrics
• Macroeconomic Forecasting
• Stochastic Volatility Models
• +۳ مورد دیگر

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.

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +۳ مورد دیگر

Hamish Flynn is a Researcher in the Department of Engineering Artificial Intelligence and Machine Learning. His work focuses on developing advanced algorithms for sequential decision-making systems, with a strong emphasis on theoretical guarantees and practical implementations. His research interests span several core areas of machine learning: Bandit algorithms (contextual, linear, and multi-armed variants) PAC-Bayesian theory and applications Reinforcement learning systems Online optimization under uncertainty Statistical learning theory Flynn's recent publications (2022-2025) demonstrate consistent focus on improving sequential learning methods. Key research themes include: non-iid noise modeling in bandits, confidence bound optimization for sequential regression, sparse nonparametric methods, and hardware-efficient machine learning implementations. His work frequently combines theoretical frameworks (PAC-Bayes, martingale theory) with practical applications in reinforcement learning and adaptive systems. No scientific awards, students advised, grant activities, or lab affiliations are mentioned in the available information.

• Machine Learning
• Bandit Algorithms
• Reinforcement Learning
• +۲ مورد دیگر

Dr. Rocio Álvarez Aranda is an Assistant Professor in the Department of Applied Economics at the University of the Balearic Islands' College of Economics and Business. With a PhD in Economics (University of Alicante, 2012) and a BSc in Mathematics (University of Malaga, 2004), she has extensive teaching experience across multiple institutions including Universidad Autónoma de Barcelona, Universidad de Santiago de Chile, and Universidad Adolfo Ibáñez. PhD in Economics, University of Alicante (2012) BSc in Mathematics, University of Malaga (2004) Her research focuses on econometric analysis of time series, unobservable factor modeling, and forecasting methodologies. She has published in top-tier journals like International Journal of Forecasting , Journal of Business & Economic Statistics , and Economics and Human Biology , with methodological work on Markov-switching models and empirical studies examining seismic activity impacts on fetal development. Recent publications reveal trends in dynamic factor models, Markov-switching autoregressive frameworks, and interdisciplinary applications of econometrics to public health and macroeconomic forecasting. She contributes to the Econometría y Ciencia de Datos (ECD) research group while maintaining expertise in MATLAB, Stata, Eviews, and LaTeX. Fondecyt Iniciación Fellowship (2018-2021) CIP16013 Research Fellowship (2016-2018) FPI Scholarship (2008-2012) Doctoral Fellowship (2006-2008) She supervises final degree projects across economics, tourism, and business programs while teaching courses like Introduction to Econometrics and Massive Data Econometrics. Her academic collaborations span institutions in Spain, Chile, and the UK.

• Time Series Analysis
• Econometric Modeling
• Forecasting
• +۳ مورد دیگر

Argimiro Alejandro Arratia Quesada is a Professor at the Universitat Politècnica de Catalunya-BarcelonaTech (UPC), affiliated with the IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group and the SOCO - Soft Computing sub-group . He works in the Department of Computer Science under the College of Industrial, Aerospace and Audiovisual Engineering (ESEIAAT) . Research Interests: Time series analysis, machine learning, descriptive computational complexity, and financial engineering. Projects: Developed the clustAnalytics R package for network clustering assessment, contributed to Bayesian modeling of pandemic data, and designed entropy-based portfolio optimization techniques. Conference Participation: Actively involved in the International Conference on Time Series and Forecasting (2021-2023) and the Catalan Association for Artificial Intelligence conferences. Scientific Output Trends: Recent publications span finance (portfolio optimization, cryptocurrency forecasting), epidemiology (Covid-19 burden estimation), network science (clustering evaluation), and mathematical finance. Methodologies emphasize statistical inference, entropy optimization, and machine learning.

• Time series analysis
• Machine learning
• Descriptive computational complexity
• +۱ مورد دیگر

Roberto Javier López Sastre is an Associate Professor of Signal Theory and Communications at the University of Alcalá. He holds a Ph.D. from the same institution (2010), focusing on 'Visual vocabularies for category-level object recognition' under Dr. Saturnino Maldonado Bascón's supervision. His research spans machine learning, computer vision, robotics, and astrophysics. Key areas include Bayesian neural networks for stellar dating, semantic navigation in robotics, and AI-driven medical assistive technologies. Affiliation: Department of Signal Theory and Communications, University of Alcalá Research Group: GRAM (Multisensorial Recognition and Analysis Group) His work interfaces computational methods with real-world applications, such as assistive robotics for neurodevelopmental disorder patients and deep learning models for astrophysical parameter estimation. Recent contributions include semantic segmentation-driven navigation systems and Bayesian approaches to black hole solutions. Publications emphasize interdisciplinary applications of AI, including live video processing, continual learning frameworks, and physics-informed neural networks. Ongoing research explores the intersection of robotics, healthcare, and astrophysics through collaborative projects. López Sastre's GRAM group focuses on multisensory data analysis for recognition and navigation challenges. His technical expertise spans signal processing, probabilistic modeling, and embedded AI systems.

• Machine Learning
• Computer Vision
• Robotics
• +۵ مورد دیگر

Álvaro Briz-Redón is an Assistant Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. He is affiliated with the PROMEDyA research group, focusing on prediction and optimization under uncertainty using dynamic stochastic models. Research Interests: Statistics and Operations Research Spatio-temporal modeling of public health data Mechanistic modeling of infectious diseases, particularly COVID-19 Bayesian inference and stochastic processes His recent work demonstrates a strong focus on applying advanced statistical methods to model the spatio-temporal dynamics of epidemics. The trend in his publications centers on integrating mechanistic models with real-world epidemiological data to improve forecasting and policy evaluation. Scientific Contributions: Published in journals such as Biometrical Journal , Stat , and Statistica Neerlandica Active collaborator with researchers in spatial statistics and epidemiology He received his PhD from the University of Valencia in 2020 with a thesis on spatio-temporal analysis of crime and traffic safety data. He has no listed scientific awards or advisees in the provided information. His work is supported by his affiliation with the University of Valencia and the PROMEDyA research group.

• Statistics
• Spatio-temporal Modeling
• Epidemiological Modeling
• +۵ مورد دیگر

Mario Sendra Pina is a Titular de Universidad (Associate Professor) in the Department of Statistics at the Universitat de València. His academic work is centered in the field of Statistics and Operations Research. He earned his doctorate from the Universitat de València in 1984 with a thesis on Bayesian analysis of discrete data, supervised by Dr. José-Miguel Bernardo, a prominent figure in Bayesian statistics. His research interests include Bayesian inference , statistical theory , and mathematical statistics , with a focus on foundational problems such as the Fieller-Creasy problem concerning inference about the ratio of two normal means. His publication record includes a key 1982 paper on the final reference distribution for the Fieller-Creasy problem, which applies Bayesian methods under unequal variances. The work contributes to theoretical statistics and has been cited in mathematical literature. While only one publication is indexed in zbMATH, it reflects deep engagement with core statistical methodology. No scientific awards, grants, or student supervision details are publicly listed in the available sources. There is no mention of lab affiliations, collaborative teams, or current research projects in the provided material.

• Statistics
• Operations Research
• Bayesian Inference
• +۲ مورد دیگر

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.

• Statistics
• Computer Science
• Economics
• +۶ مورد دیگر

Guillem Alenyà is a Researcher at the Spanish National Research Council (CSIC) and Director of the Perception and Manipulation Group at the Institut de Robòtica i Informàtica Industrial (IRI) , a joint center of CSIC and UPC. His work focuses on robot adaptation for assistive robotics , particularly in garment manipulation and human-robot interaction (HRI) . He coordinates major EU projects like SWEET and ARISE , and national initiatives including CHLOE-Map and FRAILWATCH . His research interests include: Explainable AI for robot decision-making Personalization in social robotics 3D reconstruction of clothed humans Benchmarking deformable object manipulation Privacy/safety in public-space HRI Knowledge representation for collaborative tasks The 15 most recent publications show strong trends in deformable object manipulation (62% of articles), explainable AI (54%), and human-robot collaboration (46%). Subfields span from dynamic reinforcement learning to ontology-based reasoning and privacy-preserving interaction . Scientific recognition: 2023 Georges Giralt PhD award (euRobotics) 2024 Special Doctoral award UPC 2022 AIHUB.CSIC Prize for AI excellence As advisor, he mentors 6 PhD students and 3 postdocs , with educational impact through 17 undergraduate and master's theses supervised. His 19 technology transfer projects demonstrate industrial applicability in sectors like packaging, healthcare, and smart environments.

• Human-Robot Interaction
• Assistive Robotics
• Garment Manipulation
• +۷ مورد دیگر