جست‌وجوی استاد و پژوهشگر

• Bayesian Statistics
• Financial Econometrics
• Markov Chain Monte Carlo
• +۵ 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.

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

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.

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

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.

Sebastian Tornil Sinمشاهده پروفایل

دانشیار

• Automatic Control
• Fault Detection and Diagnosis
• Water Distribution Networks
• +۶ مورد دیگر

Sebastian Tornil Sin is an Associate Professor in the Department of Automatic Control at Universitat Politècnica de Catalunya (UPC), affiliated with the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of UPC and CSIC. His work focuses on fault detection, diagnosis, and monitoring in industrial and water distribution systems. His research interests lie at the intersection of automatic control , data-driven modeling , and industrial applications . He specializes in leak detection and localization in water networks using sensor data, classifiers, and reasoning frameworks like Dempster-Shafer and Bayesian methods. His work also extends to sensor placement optimization and human-robot task design in academic environments. The recent publications (2017–2022) show a consistent focus on applying machine learning and control theory to real-world infrastructure problems, particularly in water networks. Trends include hybrid data-expert systems, classifier-based fault localization, and incremental sensor deployment strategies. Scientific Awards: No awards mentioned in the provided text. Sebastian Tornil Sin actively contributes to research through collaborations with experts such as V. Puig, J. Blesa, and A. Soldevila. While no formal advising or grant information is available, his extensive publication record in journals like IEEE Transactions on Control Systems Technology and Computers and Chemical Engineering indicates strong research leadership and project involvement. He is part of the research team at the Institut de Robòtica i Informàtica Industrial (IRI) , where he contributes to projects in fault detection, monitoring systems, and robotics applications in industrial and urban infrastructure.

Sanjay K Dharمشاهده پروفایل

استاد

• Strategic Marketing Management
• Brand Management
• New Product Development
• +۱۲ مورد دیگر

Sanjay K. Dhar, the James M. Kilts, Jr. Professor of Marketing at the University of Chicago Booth School of Business, is a leading scholar in strategic marketing management and retail dynamics. His work bridges empirical economic modeling with actionable business insights. Education: B.Tech (IIT), MBA (IIM), PhD (UCLA Anderson School) Affiliation: Faculty member at Chicago Booth since 1992 Research interests span brand management , consumer behavior , and retail strategy , with a focus on how economic factors and marketing mix variables influence household budget allocations and brand performance . His empirical work leverages massive datasets like Nielsen Homescan to uncover geographic patterns and long-term consumer trends . Recent publications examine on-screen cigarette product placement effects, WIC program impacts on cereal consumption, and retail format competition . His 2025 under-review paper extends these insights to multi-category budget reallocation during macroeconomic shifts. Scientific Recognition includes: William O’Dell Award Finalist (2012) Paul Green Award (2008) John D. C. Little Award (1995) McKinsey Teaching Excellence Award (2000) Hillel Einhorn Teaching Awards (2008, 2012) Dhar’s teaching philosophy emphasizes mastering fundamentals to solve complex marketing problems. He advises firms on strategic marketing and new product development while maintaining active research in consumer packaged goods (CPG) industries.

• Behavioral Science
• Management
• Judgement and Decision Making
• +۳ مورد دیگر

Gaël Le Mens is a Full Professor at Universitat Pompeu Fabra, specializing in Behavioral Science, Management, and Judgment and Decision Making. His research focuses on organizational theory, social influence, and the psychological mechanisms underlying decision-making processes. He holds a PhD from Stanford University and has published extensively in top journals such as Psychological Review and Journal of Personality and Social Psychology . His work explores topics like information sampling, categorization heuristics, and the dynamics of collective beliefs. Key research themes include the 'simple averaging heuristic' in category evaluation, the impact of social influence on decision-making, and the application of machine learning to analyze political texts and concepts. His recent work extends to studying politician-citizen interactions via social media and the semantic analysis of cultural concepts using AI tools like GPT-4.

• 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.

• Compositional Data Analysis
• Econometrics
• Financial Ratios
• +۶ مورد دیگر

Dr. Germà Coenders is a Professor of Quantitative Methods for Economics and Management at the Department of Economics of the University of Girona . He teaches statistics, econometrics, research methods, and survey methodology at the Facultat de Ciències Econòmiques i Empresarials and other institutions like ESADE (Ramon Llull University) and the University of Ljubljana. His research focuses on compositional data analysis with applications in marketing, accounting, finance, and tourism studies. PhD in Management Sciences (1996), Universitat Ramon Llull Director of the Compositional and Spatial Data Analysis (COSDA) research group (2021SGR01197) His work includes developing compositional structural equation models , spatiotemporal models , and generalized linear models with compositional predictors. He co-founded the Association for Compositional Data Analysis and has published 7 books and over 80 journal articles. Recent publications address topics like: Compositional approaches to financial statement analysis Impact of the Ukraine-Russia war on agricultural company resilience Air pollution effects on children's mental health Bayesian spatiotemporal modeling He maintains affiliations with: Universitat de Girona (1997–present) ESADE (Universitat Ramon Llull) University of Ljubljana His research infrastructure includes: Compositional Data Analysis Lab for financial statement analysis Data resources for wine sector ( vinicolas.xls ) and beer industry ( cervecera.xls ) studies With 5 research sexennia (1995–2024) and h-indices of 29 (Web of Science), 29 (SCOPUS), and 44 (Google Scholar), Dr. Coenders demonstrates sustained research productivity.

• 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.

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.

Ian Dew-Beckerمشاهده پروفایل

دانشیار مدعو

• Asset Pricing
• Macroeconomics
• Uncertainty
• +۴ مورد دیگر

Ian Dew-Becker serves as an Adjunct Associate Professor of Finance at the University of Chicago Booth School of Business and as a Senior Economist at the Federal Reserve Bank of Chicago. His academic work centers on theoretical and empirical asset pricing and macroeconomics, with particular emphasis on uncertainty, skewness, and tail risk in economic systems. He received his PhD in economics from Harvard University and has held prior positions at Northwestern University, Duke University, and the Federal Reserve Bank of San Francisco. His educational background includes: PhD in Economics, Harvard University Dew-Becker's research explores how agents form beliefs about economic fundamentals and how these beliefs translate into asset prices and macroeconomic outcomes. He investigates the dynamics of uncertainty and skewness across business cycles, develops novel measures using options data, and examines how production networks propagate economic shocks. His work demonstrates that firm-level uncertainty does not significantly forecast aggregate output, challenging existing models, while revealing how interconnectedness can reduce sensitivity to small shocks while amplifying vulnerability to large ones. Recent studies focus on real-time skewness measurement and tail risk transmission through input-output structures. Analysis of his publication record shows a consistent focus on risk measurement and pricing across financial and macroeconomic domains. Collaborating frequently with Stefano Giglio, he has pioneered methodologies using options data to construct cross-sectional uncertainty indices and measure conditional skewness. His research demonstrates that while exchange-traded options earn negative alphas implying rising risk aversion during downturns, synthetic options show constant risk aversion, suggesting intermediary frictions drive pricing anomalies. Key contributions include establishing empirical regularities about macro skewness and demonstrating how production networks generate left-skewed economic activity. No scientific awards were mentioned in the available information. Information regarding graduate student advising, research grants, or specific funding sources was not provided in the source materials. Dew-Becker teaches the Investments course at Chicago Booth during Autumn quarters but no details about mentored students or grant-supported projects are available. Dr. Dew-Becker actively collaborates with researchers across institutions including Stefano Giglio (Yale University), Andrea Vedolin (London School of Economics), and Bryan Kelly (Yale University), forming a network focused on financial economics and macro-finance linkages. His work bridges theoretical modeling with empirical analysis of market data, particularly options markets, to address fundamental questions about risk and uncertainty in economic systems.

Christian Igelمشاهده پروفایل

استاد

• Deep Neural Networks
• Kernel-Based Methods
• Evolution Strategies for Single- and Multi-Objective Optimization
• +۶ مورد دیگر

Christian Igel is a Professor at the Department of Computer Science (DIKU) and Director of the SCIENCE AI Centre at the University of Copenhagen. His academic journey includes a Computer Science degree from the Technical University of Dortmund, a Doctoral degree from Bielefeld University, and a Habilitation degree from Ruhr-University Bochum. He has held academic positions since 2003, including a W1 Professorship at Ruhr-University Bochum before joining DIKU in 2010 as a Professor with Special Duties in Machine Learning and becoming a Full Professor in 2014. Research Interests: Deep learning, kernel methods, evolutionary optimization, reinforcement learning, PAC-Bayesian analysis, and ML applications for sustainability. Affiliations: SCIENCE AI Centre (Director), European Lab for Learning and Intelligent Systems (ELLIS Fellow). Editorial Roles: Editor of KI – Künstliche Intelligenz , Associate Editor of Evolutionary Computation Journal and Artificial Intelligence Journal . Education: Technical University of Dortmund (Computer Science), Bielefeld University (Doctorate), Ruhr-University Bochum (Habilitation).

• Quantitative Methods
• Computational Finance
• Bayesian Statistics
• +۹ مورد دیگر

Cristian González Martel is a full Professor at the Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria. He leads multiple research projects analyzing tourism markets, financial systems, and complex network applications through big data and Bayesian statistical techniques. Current research focuses on maritime connectivity, international trade, and environmental emissions (2024) Developing tourism intelligence platforms for Gran Canaria (2023) Maintaining tourism data dashboards for Lanzarote (2022) His work integrates machine learning with economic modeling, particularly in foreign exchange markets and health economics. Projects demonstrate consistent application of spatial interaction models, complex network analysis, and optimization techniques across regional and international contexts. Key collaborations include agreements with the Inter-American Development Bank and Canary Island regional governments. Current email contact: christian.gonzalez@ulpgc.es

• Quantitative Methods for Economics and Business
• Bayesian Statistics
• Decision Analysis
• +۳ مورد دیگر

Rafael Ricardo Suárez Vega is a University Professor at the University of Las Palmas de Gran Canaria (ULPGC) affiliated with the Institute of Tourism and Sustainable Economic Development and the Department of Quantitative Methods in Economics and Management. His research focuses on Bayesian statistics, decision-making statistical techniques, and quantitative methods for economic and business applications. Doctoral Thesis Advisor: 2014 (José Luis Gutiérrez Acuña) Doctoral Thesis: "El (rlXp) - medianoide incorporando criterios de atracción" (2001) He is a member of the GIR TIDES research group, specializing in Bayesian and decision-making statistical techniques. His activity indicators include ORCID 0000-0002-1926-3121, Scopus ID 56606361200, and ResearcherID K-9982-2014.