جستجوی استاد راهنمای دانشگاهی

• Biostatistics
• Epidemiology
• Survival Analysis
• +۴ مورد دیگر

Gomez Melis, Guadalupe is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the GRBIO research group (Bioestadística i Bioinformàtica) and the Department of Statistics and Operations Research. She collaborates with the Institut de Recerca i Innovació en Salut (Health Research Institute) and the Faculty of Mathematics and Statistics (FME). Her work focuses on biostatistics, survival analysis, multistate models, and clinical trial design, with significant contributions to understanding disease progression and outcomes, particularly in pandemic-related studies. She has supervised doctoral theses and leads various research projects funded by EU and national grants. Her research spans statistical methodologies for healthcare data, including censored data analysis and adaptive clinical trial designs. Key activities include leading over 450 research outputs, including articles in high-impact journals like Biostatistics and BMC Medical Research Methodology , and collaborations on projects like the EU-funded Siemens Energy AI Chair initiative. Her work often integrates statistical modeling with real-world health data, addressing challenges in infectious disease dynamics, elderly patient care, and genomic analysis. Notable contributions include developing the GofCens package for goodness-of-fit methods and the MSMpred interactive tool for predicting patient trajectories via multistate models. She actively participates in international conferences and serves on scientific committees, demonstrating her role in advancing biostatistical methodologies globally.

• Stochastic Analysis
• Malliavin Calculus
• Stochastic Differential Equations (SDEs)
• +۷ مورد دیگر

Eulàlia Nualart is Associate Professor at Universitat Pompeu Fabra (UPF) under the Serra Hunter Program and an Affiliated Professor at the Barcelona Graduate School of Economics (Barcelona GSE). She serves as Deputy Director of the Barcelona GSE Master Program in Economics. Her academic affiliation is with the Department of Economics and Business at UPF, located at the Jaume I Building on the Ciutadella Campus in Barcelona. Research Interests: Her work centers on stochastic analysis and its applications, particularly in finance. Key areas include Malliavin calculus, stochastic and stochastic partial differential equations (SDEs and SPDEs), fractional Brownian motion, Lévy processes, and statistical inference for parametric models driven by stochastic dynamics. These fields are central to modern probability theory and quantitative finance. Publications and Research Trends: Her research has been published in leading journals such as Stochastic Processes and their Applications , The Annals of Probability , SPDEs: Analysis and Computations , and Journal of Functional Analysis . The body of her work reflects a deep engagement with theoretical probability and its practical modeling implications, especially in financial contexts. Marie Curie Career Integration Grant (2013) Advising and Grants: While specific students are not listed, her role as Deputy Director of a master's program and her publication record suggest active mentorship and research supervision. She secured competitive European funding through the Marie Curie program, indicating strong grant-writing ability and research leadership. No other grants are mentioned in the text. Labs and Research Teams: Though no formal lab is named, she is embedded in the research ecosystem of the Department of Economics and Business at UPF and collaborates with the Barcelona GSE, a major hub for economics and finance research in Europe. Her work likely contributes to broader research groups focused on mathematical economics and financial modeling.

• Micro-econometrics
• Labor Economics
• Dynamic Discrete Choice Models
• +۵ مورد دیگر

Jesús Carro is an Associate Professor at Universidad Carlos III de Madrid in the Department of Economics . He specializes in micro-econometrics and labor economics, focusing on dynamic discrete choice models and unobserved heterogeneity analysis. Doctorate from CEMFI Active research since 2004 Key methodological contributions in econometric modeling His research spans: Labor supply dynamics Education policy evaluation Health economics Intergenerational preference transmission Recent publications highlight: Dynamic binary choice modeling with maximal heterogeneity (2014) State dependence in health outcomes (2014) Bilingual education program impacts (2016) Preference transmission mechanisms (2017) He teaches courses in: Micro-econometrics Economics of Education Policy Evaluation

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.

• Applied Economics
• Economic Integration
• Macroeconomics
• +۴ مورد دیگر

Cecilio Tamarit is a Professor of Applied Economics at the University of Valencia, where he holds a Jean Monnet Chair and coordinates the Research Group on Economic Integration (INTECO). He is also Vice President of the Free Association of Economics (ALDE) and President of the Economics Committee of the National Commission for the Evaluation of Research Activity (CNEAI). Master's in Economics, College of Europe (Bruges) Extraordinary Doctorate Award, University of Valencia (1992) Schuman Scholar, European Parliament (1988) Visiting Scholar, University of Nottingham (1992) Senior Fellow, Wharton School, University of Pennsylvania (1996) His research focuses on applied economics, macroeconomics, and European economic integration, with strong emphasis on modeling and policy analysis. He has contributed extensively to understanding economic convergence, fiscal policy, and international trade dynamics within the EU framework. The 15 most recent publications reflect a consistent focus on empirical and policy-oriented economics, spanning macroeconomic modeling, international trade, public policy, and financial economics. His work frequently appears in high-impact journals, demonstrating sustained scholarly engagement in applied economic research. Scientific Awards: Extraordinary Doctorate Award in 1992 from the University of Valencia He has advised numerous research projects and led major evaluation initiatives in economic research. While specific grants are not listed, his leadership roles in national evaluation bodies and research groups suggest significant involvement in funded research and policy advising. He has directed the European Documentation Center at the University of Valencia and served as an advisor to the Spanish government. He leads the INTECO research group, focusing on economic integration and policy modeling, contributing to both academic and policy debates in European economics.

• 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

• Data Mining
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

Yann Pontyمشاهده پروفایل

پژوهشگر

• RNA Folding
• RNA Design
• RNA Evolution
• +۹ مورد دیگر

Yann Ponty is a tenured CNRS Researcher at the Computer Science Department (LIX) of École Polytechnique (Institut Polytechnique de Paris, France). He leads the AMIBio team and serves as Deputy Director of LIX. His work focuses on developing bioinformatics methods at the intersection of computer science, mathematics, and molecular biology, particularly for RNA structure prediction, design, and evolution. He holds leadership roles in the ISCB Board of Directors (2025-2027) and the HDR referent for the IDIA department (CS&Interactions) at IP Paris. Research Interests: RNA folding/design/evolution, RNA-RNA/RNA-protein interactions, random generation, enumerative combinatorics, discrete algorithms, parameterized complexity, RNA visualization Key Contributions: Developed algorithms for RNA inverse folding, pseudoknot modeling, and dynamic programming optimization Collaborations: Partnerships with institutions like Simon Fraser University, Boston College, and Université Paris-Saclay His recent publications (15 most recent) span RNA structure prediction, pseudoknot partition functions, linear-time inverse folding algorithms, and parameterized sampling techniques. The work emphasizes dynamic programming, combinatorial approaches, and integration of experimental data for improving RNA modeling. Scientific awards include election to the ISCB Board of Directors (2025-2027) and leadership roles in academic networks like GdR BIM. He actively contributes to software development (VARNA, RNANR, SPARCS, IncaRNAtion, RNARedPrint) and serves as Associate Editor for Bioinformatics (OUP). Teaching engagements include graduate-level courses in combinatorial optimization, RNA bioinformatics, and algorithms at Université Paris-Saclay and École Polytechnique.

Salvador Nayaمشاهده پروفایل

استاد

• Statistics
• Operations Research
• Data Analytics
• +۷ مورد دیگر

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.

• Computational Neuroscience
• Neuroscience
• Biophysics
• +۳ مورد دیگر

Gustavo Deco is a Research Professor at the Institució Catalana de Recerca i Estudis Avançats (ICREA) and holds a Professorship (Catedrático) at Pompeu Fabra University (UPF). He leads the Computational Neuroscience Group and directs the Center of Brain and Cognition at UPF. His research focuses on computational models of brain dynamics, integrating biophysics, neuroimaging, and complex systems principles. Deco’s academic journey includes a PhD in Physics (1987, thesis on Relativistic Atomic Collisions), postdoctoral work at the University of Bordeaux (France) and University of Giessen (Germany), and a Habilitation in Computer Science (1997, Technical University of Munich). He has led computational neuroscience research at Siemens Corporate Research Center (1990–2003) and pioneered whole-brain modeling frameworks like The Virtual Brain (TVB). His research interests span critical brain dynamics, non-equilibrium thermodynamics in neural systems, psychedelics’ effects on brain hierarchy, and clinical applications of computational models in disorders such as Alzheimer’s and depression. Recent work emphasizes turbulence-like dynamics in healthy and diseased brains, and biomarker discovery using AI-driven simulations. Deco’s articles (2024–2025) explore topics like entropy production in brain networks, psychedelics-induced flattening of functional hierarchies, sleep-like dynamics post-stroke, and the role of long-range connections in global brain communication. His work bridges theoretical models with clinical insights, aiming to advance personalized neurology and digital brain research.

• Survival Analysis
• Spatio-Temporal Modeling
• Bayesian Statistics
• +۷ 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.

• Time Series Analysis
• Energy Analytics
• Statistical Clustering
• +۳ مورد دیگر

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
• +۳ 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.

• Applied Analytics
• Statistical Inference
• Time Series Analysis
• +۷ مورد دیگر

Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.

José E. Chacónمشاهده پروفایل

استاد

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +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.