جست‌وجوی استاد و پژوهشگر

Stanislav Anatolyevمشاهده پروفایل

استاد

• Econometrics
• Method of Moments
• Time Series Modeling
• +۲ مورد دیگر

Stanislav Anatolyev serves as Full Professor of Economics at the New Economic School (NES) since 2009 and holds an Associate Professor position at CERGE-EI in Prague. Affiliated with NES since 2000, he teaches advanced econometrics courses including Econometrics 3, Applied Time Series Econometrics, and Selected Chapters in Econometrics. Education PhD in Economics, University of Wisconsin-Madison (2000) MSc in Economics, New Economic School (1995) Specialist Diploma in Applied Mathematics, Moscow Institute of Physics and Technology (1992) Research Focus : Professor Anatolyev's work centers on econometric theory with expertise in method of moments, time series modeling, and high-dimensional data analysis. His contributions span theoretical developments in factor models, volatility estimation, and instrumental variables methods, alongside practical applications in financial econometrics and portfolio optimization. He maintains active research collaborations across international institutions. Publication Trends : Recent work demonstrates increasing emphasis on ultra-high-dimensional econometrics, with significant contributions to copula-based portfolio allocation, many-instrument regressions, and financial market belief updating mechanisms. His publications bridge theoretical rigor with empirical applications, frequently appearing in top econometrics journals including Journal of Econometrics and Econometric Theory. Awards Econometric Theory Multa Scripsit Award (2022) for exceptional scholarly output Academic Leadership : As founding Editor-in-Chief of the Russian-language journal Quantile since 2006, he has fostered econometric research dissemination in Eastern Europe. His co-authored textbook Methods for Estimation and Inference in Modern Econometrics serves as a key reference in graduate econometrics education. Professional Activities : Regularly presents at international conferences and serves as referee for leading econometrics journals, maintaining active engagement with the global econometrics community through seminar presentations and collaborative research projects.

• Extreme Value Theory
• Graphical Models
• Multivariate Statistics
• +۳ مورد دیگر

Frank Röttger is an Assistant Professor at Eindhoven University of Technology, specializing in Mathematical Statistics. His primary research focuses on graphical models, multivariate extremes, and statistical inference in high-dimensional settings. Research Interests : Extreme value theory, probabilistic graphical models, causal inference in extremes, and data-driven risk modeling. Awards : NWO Prize (Scientific) - 2024 Organized Activities : Eurandom Workshop on Graph Laplacians, Multivariate Extremes, and Algebraic Statistics (2024) Causality in Extremes Workshop (2024) Courses Taught : Dependence Modeling Foundations of Statistics Mathematical Statistics Contact : Email: f.rottger@tue.nl

Vicky Fasen-Hartmannمشاهده پروفایل

استاد

• Extreme Value Theory
• Lévy Processes
• Risk Management
• +۶ مورد دیگر

Vicky Fasen-Hartmann is a Professor at the Karlsruhe Institute of Technology (KIT) within the Department of Mathematics, specifically affiliated with the Institute of Stochastics. She has held her W3 Professor position since October 2012, with two periods of parental leave (August 2016-August 2017 and October 2018-October 2019). Prior to her current position, she held postdoctoral research positions at ETH Zurich (RiskLab), TU Munich, Université Pierre et Marie Curie, and Cornell University. Her educational background includes: Habilitation (2010) in Heavy Tails in Finance, Insurance and Telecommunication from TU Munich Ph.D. (2004) in Extremes of Lévy Driven Moving Average Processes with Applications in Finance from TU Munich Diploma in Mathematics (2002) from Karlsruhe Institute of Technology Professor Fasen-Hartmann's research spans multiple areas of theoretical and applied statistics with a focus on extreme value theory, heavy-tailed distributions, and their applications in finance and risk management. Her work bridges theoretical probability with practical financial applications, particularly in modeling rare events and systemic risks. She has made significant contributions to the understanding of Lévy processes, continuous-time ARMA models, and multivariate extremes. Her research combines rigorous mathematical theory with practical applications in financial mathematics, insurance, and telecommunications networks. The trends in her recent publications (2020-2025) show a clear evolution toward high-dimensional extreme value theory, financial network risk contagion, and advanced modeling of continuous-time processes. Her work increasingly addresses the challenges of modern financial systems, including systemic risk measurement, high-dimensional dependency structures, and the statistical properties of extreme events in complex systems. She has developed innovative methodologies for analyzing multivariate extremes, risk contagion, and continuous-time state space models. Professor Fasen-Hartmann has served in significant editorial roles including Associate Editor for the Scandinavian Journal of Statistics since 2014, Managing Editor of Lévy Matters (2008-2014), and Editor of Bernoulli News (2009-2011). She has also been active in academic service through committee work, including the Steering Committee of the Probability and Statistics Group in Germany (2014-2016) and the Examination Board of the Department of Mathematics at KIT (since 2017). She has supervised numerous doctoral and master's students, with current PhD candidates including Lucas Butsch (since 2021) and previously Lea Schenk, Celeste Mayer, Markus Scholz, and Sebastian Kimmig. Her teaching portfolio includes advanced courses in Time Series Analysis, Continuous Time Finance, Extreme Value Theory, and Asymptotic Stochastics. She regularly organizes workshops and conferences on specialized topics in probability and statistics, demonstrating her leadership in the academic community.

Claudia Teutschbeinمشاهده پروفایل

دانشیار

• Hydrology
• Climate Change Impacts
• Hydrological Modeling
• +۶ مورد دیگر

Claudia Teutschbein is a Senior Lecturer/Associate Professor in Hydrology at Uppsala University's Department of Earth Sciences, where she leads research in the Program for Air, Water and Landscape Sciences. She also serves as a researcher at Uppsala University's Conflicting Objectives Research Nexus (UUniCORN). With over 15 years of teaching and research experience, she has established herself as a leading expert in hydrological processes in changing climates. Her educational background includes: 2022: Docent in Hydrology, Uppsala University 2013: Ph.D. in Physical Geography, Stockholm University, Sweden 2010: Ph.Lic. in Physical Geography, Stockholm University, Sweden 2008: M.Sc. in Soil Science, SLU Uppsala, Sweden 2006: B.Sc. in Water Management, TU Dresden, Germany Dr. Teutschbein's research spans interdisciplinary hydrology with a focus on understanding hydrological processes in changing climates and their connections to meteorological, topographic, and anthropogenic drivers. Her work addresses critical issues of water quantity (including floods and droughts) and water quality across various spatial and temporal scales, with attention to socio-economic consequences. She employs advanced hydrological modeling techniques to assess climate change impacts and develops solutions for sustainable water resource management. Her recent publications demonstrate a strong focus on drought risk assessment, water-energy-food-ecosystem nexus approaches, and hydroclimatic modeling in Nordic and global contexts. She has made significant contributions to understanding drought propagation in high-latitude catchments, stakeholder perceptions of drought hazards, and the integration of data-driven approaches in nexus modeling. Her CAMELS-SE dataset has become an important resource for hydrological research and education across Sweden. Dr. Teutschbein leads and contributes to numerous research projects addressing critical water challenges: 2025-2028: REACTION: Navigating the Risks of Hydroclimatic Extremes for Freshwater Ecosystem Services in Forest Landscapes (PI) 2023-2026: PredPeat: Predicting the effects of peatland rewetting on water retention and water quality (co-applicant, WP-lead) 2022-2026: SWEFE-NEXT: the Swedish Water-Energy-Food-Ecosystem Nexus and its response to hydroclimatic EXTreme events (PI) 2021-2025: NEXOGENESIS: Facilitating the next generation of water-related policies using AI and reinforcement learning (co-applicant, WP lead) 2019-2023: Impacts of recent El-Niño Southern Oscillation (ENSO) on the Water-Food-Energy Nexus in South Asia (PI) Her research bridges academic inquiry with practical applications for sustainable water management, with particular emphasis on climate change adaptation strategies and integrated resource management approaches.

Roberto Ghiselli Ricciمشاهده پروفایل

استاد

• Probability
• Mathematical Statistics
• Copula Theory
• +۳ مورد دیگر

Roberto Ghiselli Ricci is a Full Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Informatics and Statistics. His academic career includes extensive teaching and research in mathematical statistics and probability, with a focus on copula theory, aggregation functions, and econometric applications. He currently teaches courses such as Calculus, Linear Algebra, and Mathematics for Environmental Sciences. His research explores advanced topics in probability theory, including copula properties, fixed-point theorems, and axiomatic characterizations of mobility measures. Recent publications highlight contributions to fuzzy set theory, optimization penalties, and financial securities modeling. His work bridges mathematical rigor with practical applications in economics and environmental policy analysis. Publications trends emphasize interdisciplinary approaches, with notable contributions to Fuzzy Sets and Systems , International Journal of Game Theory , and Social Choice and Welfare . He actively participates in academic activities through courses, research collaborations, and advisory roles within his department.

Andrew J. Pattonمشاهده پروفایل

استاد

• Financial Econometrics
• Volatility Forecasting
• High-Frequency Financial Data Analysis
• +۴ مورد دیگر

Andrew J. Patton is the Zelter Family Distinguished Professor and Professor of Economics at Duke University's Trinity College of Arts & Sciences, holding these roles since 2016 and 2013 respectively. Previously, he served as Associate Professor of Economics at Duke from 2009 to 2013. Patton holds a Ph.D. and M.A. from the University of California, San Diego (2002 and 2000), and a B.Bus. from the University of Technology Sydney (1998). His research focuses on financial econometrics, particularly volatility forecasting, dependence modeling, high-frequency financial data analysis, and hedge fund dynamics. He has published extensively in top journals such as the Journal of Finance , Journal of Financial Economics , and Econometrica . His recent work includes advancements in realized semicovariances, k-means clustering for unobserved heterogeneity, and risk price variations. Patton’s 2016 Distinguished Professorship reflects his scholarly impact. His research emphasizes practical applications, such as improving volatility forecasts and addressing market anomalies’ trading costs.

Liqun Diaoمشاهده پروفایل

دانشیار

• Statistical Methodology
• Machine Learning
• Causal Inference
• +۷ مورد دیگر

Liqun Diao is an Associate Professor (Tenured) in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds affiliations with the Health Data Science Lab and the Waterloo Artificial Intelligence Institute. His research focuses on developing statistical methods and machine learning algorithms for applications in medicine, public health, and insurance. Education: B.Econ. in Statistics, Renmin University of China (2007) M.Math. in Statistics (Biostatistics), University of Waterloo (2009) Ph.D. in Statistics (Biostatistics), University of Waterloo (2013) Research Interests: Recursive partitioning and tree-based methods for survival and health data Causal inference and missing data methodologies Bayesian nonparametric models and copula dependence structures Mortality forecasting and actuarial science applications Awards: 2013 Pierre Robillard Award (best doctoral thesis in Canadian statistics) 2024 Outstanding Performance and Teaching Awards at UW Professional Activities: Led research groups in health data science and AI Recipient of multiple grants from NSERC and industry partners Advises graduate students in statistics and actuarial science Labs/Teams: Active contributor to the Health Data Science Lab and Waterloo AI Institute, focusing on applying statistical innovations to real-world health and insurance challenges.

Daniela Castro Camiloمشاهده پروفایل

مدرس ارشد

• Extreme Value Theory
• Environmental Risk Analysis
• Spatial Statistics
• +۳ مورد دیگر

Dr. Daniela Castro Camilo is a Senior Lecturer in Statistics at the University of Glasgow's School of Mathematics & Statistics. Her research focuses on extreme value theory applied to environmental hazards, including landslides, climate extremes, and risk assessment. She leads projects like the Mitigating Landslides Impacts in Scotland (MLIS) and Geostatistical Binary Models for Extremes (GEOBEx) , funded by the Scottish Government and EPSRC. She collaborates with institutions such as the British Geological Survey and the Met Office. Her work bridges statistical methodology and environmental applications, with notable contributions to landslide hazard modeling and spatial extremes. She actively participates in international conferences and organizes events like the ESS-sponsored session at the 2024 RSS Conference. She is a core member of GLE²N (Glasgow-Edinburgh Extremes Network), fostering interdisciplinary research in statistical risk analysis. Recent projects include developing probabilistic forecasting tools for weather-driven faults in electricity networks and advancing Bayesian methods for extreme event prediction. Her research emphasizes practical solutions for resilience to environmental disasters, combining cutting-edge statistical techniques with real-world data challenges.

• Financial Econometrics
• Volatility Modeling
• Time Series Analysis
• +۴ مورد دیگر

Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.

John Hughesمشاهده پروفایل

دانشیار

• Biostatistics
• Health Data Science
• Statistical Computing
• +۶ مورد دیگر

John Hughes, PhD, is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. With nearly 30 years of experience in higher education, he has held positions at institutions including Frostburg State University, the University of Minnesota, and Pennsylvania State University. His methodological research focuses on statistical models for dependent data, Bayesian methods, and spatial and spatiotemporal analysis. He has developed numerous software packages for R and Perl, including copCAR , ngspatial , and krippendorffsalpha . Dr. Hughes' interdisciplinary work spans environmental health, bioimaging, vaccine hesitancy, and spatial epidemiology of HPV-related cancers. He has consulted for organizations such as the Courage Kenny Research Center and Temple University. His education includes a PhD in Statistics from Penn State University and an MS in Applied Computer Science from Frostburg State University. His research emphasizes statistical computing and the application of advanced models to health data. Recent work includes methodologies for agreement coefficients, environmental noise measurement, and spatial analysis of vaccination refusal patterns. Dr. Hughes teaches courses in biostatistics, data science, and programming, reflecting his dual role as a teacher-scholar. Professional contributions include software development, collaborative research projects, and academic leadership. His work bridges computational innovation and real-world health challenges, positioning him as a key figure in modern biostatistical research.

Kahadawala Coorayمشاهده پروفایل

استاد

• Modeling continuous statistical distributions
• Statistical inferences
• Actuarial science
• +۱ مورد دیگر

Kahadawala Cooray is a Professor at the Department of Statistics, Actuarial and Data Sciences within the College of Science and Engineering at the University of Nevada at Las Vegas. He holds a Ph.D. from the same institution (2008) and a B.Sc. from the University of Colombo, Sri Lanka (1994). Education : Ph.D. (2008, University of Nevada at Las Vegas), B.Sc. (1994, University of Colombo, Sri Lanka) Cooray specializes in modeling continuous statistical distributions and applying them to actuarial science and medical research. His work includes parametric analysis of renal failure data, Bayesian estimation of composite distributions, and extensions of the Weibull and Gumbel distributions for complex datasets. His research trends focus on creating flexible statistical models for heavy-tailed data, multivariate dependence structures, and applications in insurance risk analysis. Key contributions include generalized copulas, folded logistic distributions, and hybrid Weibull-Pareto models for survival analysis. Scientific Awards : First Ph.D. graduate in mathematical sciences in Nevada (2008) Wolzinger Family Research Scholarship (2008) Cooray teaches courses in statistics and actuarial science, and he is affiliated with the American Statistical Association and its Mid-Michigan Chapter. His collaborations include researchers like M. M. A. Ananda and S. Gunasekera.

• Bayesian Statistics
• Expert Knowledge Elicitation
• Robust Analyses
• +۳ مورد دیگر

Professor John Paul Gosling is an applied statistician affiliated with the Department of Mathematical Sciences at Durham University . With a background in industry, academia, and the civil service, his work bridges statistical theory and real-world applications, particularly in criminal justice, toxicology, and climate science. Education: MMath in Mathematics (University of York, 1998–2002), PhD in Statistics (University of Sheffield, 2002–2005) Current Role: Professor in Statistics, focusing on human interaction with statistical information His research interests include: Bayesian Statistics : Developing models for complex systems Expert Knowledge Elicitation : Structuring expert input for risk assessment Uncertainty Quantification : Analyzing and communicating uncertainty in data Machine Learning Integration : Combining statistical inference with AI for decision-making Key trends from his recent articles include: Application of Voronoi Tessellations in Bayesian regression and classification Development of emulation techniques for computationally intensive models Focus on legal and public health domains Integration of copula methods for dependent variables He actively supervises PhD students in Bayesian theory and machine learning interfaces and contributes to Statistics research at Durham University.

Claudia Klüppelbergمشاهده پروفایل

استاد

• Applied Probability
• Mathematical Statistics
• Financial Risk Management
• +۳ مورد دیگر

Claudia Klüppelberg is a Professor and Chair of Mathematical Statistics at the Center for Mathematical Sciences, Technische Universität München (TUM). Her academic journey includes positions at ETH Zurich, University of Mainz, and TUM since 1997. She holds a Carl von Linde Senior Fellowship at TUM-IAS, focusing on Risk Analysis and Stochastic Modeling. Her research bridges applied probability, statistics, and their applications in finance and insurance, emphasizing extreme value theory, risk processes, and stochastic networks. She has received prestigious awards such as the New Frontiers in Risk Management Award (2007) and Cross of Merit (2001). Her work addresses real-world challenges in financial risk management, including systemic risk in networks and operational risk modeling. Her recent publications explore causal analysis of extreme risks in networks, max-linear models, and Bayesian networks for extreme events. She contributes to academic leadership as an editorial board member and advisor, promoting interdisciplinary stochastic sciences.

Alexandra Bugalho De Mouraمشاهده پروفایل

دانشیار

• Actuarial Science
• Optimal Reinsurance
• Climate Risk Analysis
• +۳ مورد دیگر

Alexandra Bugalho De Moura is an Associate Professor of Statistics and Actuarial Sciences at the Department of Mathematics, ISEG-University of Lisbon. She coordinates curricular units in Actuarial Sciences, Statistics, and Data Analysis, and leads the Master's in Actuarial Sciences program. Her academic journey includes a PhD in Mathematical Engineering from Politecnico di Milano (2007), a BSc in Applied Mathematics and Computing from IST, University of Lisbon (2001), and a Master’s in Actuarial Sciences from ISEG (2018). Her research focuses on actuarial sciences, particularly optimal reinsurance with dependencies, and climate risk analysis using climate and insurance loss data. Previously, she contributed to computational hemodynamics modeling cerebral aneurysms and blood flow dynamics. She has led an FCT-funded project on optimal reinsurance and supervised over 30 master’s theses in actuarial risk theory and data science applications. Professional experience includes roles as Post-Doctoral Fellow at CEMAT (2007–2014) and teaching positions since 2000. She has published extensively in top journals like European Actuarial Journal and Computer Methods in Biomechanics and Biomedical Engineering , with over 20 peer-reviewed articles and book chapters. Her work bridges mathematical rigor with practical applications in insurance risk modeling, climate-related financial risks, and biomedical engineering. Current research explores dependencies in reinsurance treaties and climate-driven insurance loss patterns.

Yun-Hee Choiمشاهده پروفایل

استاد

• Biostatistics
• Statistical Genetics
• Survival Analysis
• +۳ مورد دیگر

Yun-Hee Choi is a Professor at the Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University. Her research focuses on advanced statistical methodologies for complex biomedical data, particularly in genetic and cancer epidemiology contexts. BSc in Statistics MA in Statistics PhD in Biostatistics She specializes in joint modeling , dynamic prediction , correlated survival data analysis , and statistical genetics , with applications to Lynch syndrome and familial colorectal cancer . Her methodological work includes copula models , frailty models , and multistate models for analyzing competing risks and recurrent events. Recent publications demonstrate expertise in genetic risk estimation , cancer progression modeling , and family-based survival analysis . She develops computational tools like the FamEvent R package for time-to-event data in genetic studies. NSERC: Statistical methods for joint modeling (2019-2024) CIHR: Risk prediction models for HBOC (2019-2023, Co-PI) CANSSI: Genetic data analysis (2019-2022, Co-I) NSERC: Competing risks modeling (2014-2019) CIHR: Multistate models for Lynch syndrome (2013-2016) CBCF-Ontario: BRCA1/2 family screening (2014-2017) NSERC: Correlated survival data (2009-2014) Her work bridges biostatistical theory with cancer prevention applications , particularly in hereditary cancer syndromes.