جست‌وجوی استاد و پژوهشگر

• Statistics
• Econometrics
• Panel Data Analysis
• +۵ مورد دیگر

Assoc. Prof. Elif Tuna serves in the Department of Statistics at Yildiz Technical University's Faculty of Arts & Science, where she advanced from Research Assistant (2002) to Assistant Professor (2012) before promotion to Associate Professor. Her doctoral training in Econometrics at Marmara University established her interdisciplinary approach bridging statistical theory and socioeconomic applications. Her educational qualifications include: Doctorate in Econometrics, Marmara University (2005-2010) Postgraduate in Statistics, Yildiz Technical University (2002-2005) Undergraduate in Econometrics, Istanbul University (1997-2001) Tuna's research centers on entropy-based statistical methodologies, panel data modeling, and nonlinear econometric analysis. She pioneers applications in environmental economics (Environmental Kuznets Curve validation), happiness economics during crises, and market concentration metrics. Her work consistently integrates information theory with real-world socioeconomic phenomena, particularly migration patterns and pandemic impacts. Recent publications (2021-2024) reveal intensified focus on dynamic modeling of pandemic effects using Markov switching frameworks, while maintaining her foundational work on entropy applications. She demonstrates methodological versatility across time series, panel data, and categorical analysis, with geographic emphasis on Turkish socioeconomic contexts. Scientific Awards: No awards documented in source materials She has supervised 2 graduate theses and contributed to two major funded projects: a 2014-2016 basin-level water resource study and a 2005-2007 nationwide evaluation of Turkish statistics education. Her collaborative work often involves cross-disciplinary teams from economics and environmental science departments. No dedicated laboratory facilities or permanent research teams are specified, though her project involvement suggests participation in university-wide research consortia.

Tomás Del Barrio Castroمشاهده پروفایل

استاد

• Time Series Analysis
• Seasonality
• Applied Econometrics
• +۲ مورد دیگر

Dr. Tomás Del Barrio Castro is a Full Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB) , where he has been affiliated since 2007. He holds a PhD in Economics (University of Barcelona, 1998) and a Degree in Economics (Autonomous University of Madrid, 1992). PhD in Economics, University of Barcelona (1998) Degree in Economic and Business Sciences, Autonomous University of Madrid (1992) His research focuses on Time Series Analysis , Seasonality , and Applied Econometrics , with methodological contributions to seasonal unit root testing and periodic integration. He has published in top journals including Econometric Theory , The Econometric Journal , and Journal of Time Series Analysis . A significant portion of his work examines temporal aggregation effects, persistent cycles, and mixed-frequency data challenges. Scientific Recognition : Econometric Theory Multa Scripsit Award (2014) As Principal Investigator since 2007, he leads competitive research projects in econometrics. His teaching spans undergraduate and graduate courses in Statistics and Econometrics, including Macroeconometrics and Time Series Analysis at UIB and University of Barcelona. Recent appointments include: 2025-26: Macroeconometrics (Double Degree in Economics & Tourism) 2025-26: Time Series Analysis (Master's in Big Data Analysis)

Marek Omelkaمشاهده پروفایل

دانشیار

• Asymptotic Statistics
• Nonparametric Statistics
• Copula Models
• +۱۰ مورد دیگر

Assoc. Prof. Marek Omelka is a faculty member at the Department of Probability and Mathematical Statistics , Faculty of Mathematics and Physics, Charles University in Prague. His research focuses on copula models, dependence modeling, and nonparametric statistical methods with applications in multivariate analysis, permutation tests, and conditional copula structures. Current teaching: NMST424 Mathematical Statistics 3 and JEM019 Statistical Methods for Data Analysis Research interests: Asymptotic statistics, copula estimation, permutation methods, multivariate association, and functional data depth His recent publications analyze copula parameter estimation, covariate effects in conditional models, and multivariate tail coefficients, with implementations in R. The PMSE program he guarantees connects theoretical statistics with real-world applications in data science, finance, and medical studies.

• Bayesian Statistics
• Statistical Modeling
• Data Science
• +۴ مورد دیگر

Gianluca Mastrantonio is an Associate Professor at the Department of Mathematical Sciences (DISMA) of the Polytechnic University of Turin , Italy. He is a member of the Interdepartmental Center SmartData@PoliTO and actively contributes to the Statistics and Data Science research group. His academic roles include teaching in PhD programs (Mathematical Sciences, 2023-2025) and master's courses such as Statistical Methods in Data Science and Statistical Models/Statistical Learning . His research focuses on Bayesian Statistics and Hierarchical Models , with applications spanning Biostatistics , Environmental Monitoring , Machine Learning , and Computational Statistics . Key keywords include RNA Velocity , Sea Climate Analysis , Animal Movement Modeling , and Bayesian Software Development . Selected Scientific Awards Steering Committee Member, Royal Statistical Society (Emerging Applications, 2021-) Effective Member, International Statistical Institute (ISI, 2019-) Effective Member, Graspa (Italy, 2017-) Associate Editor, Journal of Statistical Computation and Simulation (2020-) Key Research Themes Bayesian Modeling of RNA Dynamics Environmental and Wildlife Behavior Analysis Statistical Software Development (Julia/R packages) Climatic Change-Point Detection Integration of Linear/Circular Data in Ecology Applications in Prostate Cancer Diagnostics

Federico Bumbacaمشاهده پروفایل

استادیار

• Bayesian Hierarchical Models
• Target Marketing
• Loyalty Programs
• +۶ مورد دیگر

Federico Bumbaca serves as an Assistant Professor of Marketing at the Leeds School of Business, University of Colorado Boulder. His academic journey spans engineering, operations research, and business disciplines. Education Highlights: PhD in Marketing - Paul Merage School of Business, University of California Irvine MBA - Sloan School of Management, Massachusetts Institute of Technology MS in Operations Research - Virginia Tech MS & BS in Electrical Engineering - University of Toronto Bumbaca's research pioneers big data methodologies in marketing, specializing in Bayesian hierarchical models and distributed computing techniques for target marketing optimization. His work bridges complex statistical modeling with practical marketing applications, particularly in loyalty programs and customer segmentation. The evolution from his early computer vision research to contemporary marketing analytics demonstrates interdisciplinary expertise. His publication portfolio reveals a distinct transition from 1980s engineering research (laser rangefinders, color vision systems) to modern marketing analytics, with recent focus on scalable algorithms for Bayesian modeling. This trajectory reflects his unique ability to apply computational rigor to marketing challenges. Bumbaca teaches Marketing Research and Analytics in undergraduate programs and Data and Decisions in MBA courses, leveraging his industry experience from technology startups in the San Francisco Bay Area where he developed expertise in marketing, business development, and sales.

Archer Gong Zhangمشاهده پروفایل

مدرس

• Statistics
• Data Integration
• Distribution Shift
• +۶ مورد دیگر

Dr. Archer Gong Zhang serves as a Lecturer in Statistics and Data Analytics within the Department of Statistics at the University of Glasgow, specializing in theoretical and methodological statistics with applications in machine learning. Education PhD in Statistics, University of British Columbia (2022) Departmental Postdoctoral Fellow, University of Toronto (supervised by Dr. Nancy Reid and Dr. Qiang Sun) His research centers on data integration and distribution shift , developing efficient inference methods for multi-source data with interconnected populations. Key contributions include advancing the Density Ratio Model (linking biased sampling, exponential tilting, and proportional likelihood ratios) and Empirical Likelihood techniques. He actively explores causal inference and statistical machine learning, particularly transfer learning challenges under distributional shifts. Recent publications (2021-2025) reveal a cohesive trajectory: foundational work on density ratio models evolved into applications for clustering resistance, mixture model reduction, and quantile testing. This demonstrates his focus on bridging semiparametric theory with practical machine learning solutions for distributional heterogeneity. Dr. Zhang maintains active collaborations with institutions including the University of Toronto and University of British Columbia, evidenced by co-authored publications with leading statisticians like Jiahua Chen and Qiang Sun. His research group integrates theoretical rigor with computational innovation to address real-world data integration challenges.

Boris Berangerمشاهده پروفایل

مدرس ارشد

• Extreme Value Theory
• Environmental Extremes
• Spatial Statistics
• +۳ مورد دیگر

Boris Beranger is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, UNSW Sydney . He is also a member of the UNSW Data Science Hub (uDASH) and previously served as an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . His research spans theoretical and applied statistics, focusing on Extreme Value Theory (environmental, financial, and insurance applications) and Symbolic Data Analysis (complex/non-standard data structures). Education: PhD in Statistics (Université Pierre and Marie Curie & UNSW, 2016), MSc in Mathematics (Université Pierre and Marie Curie, 2011) Research Trends are evident in: High-dimensional extremal dependence modeling (ExtremalDep package) Spatial extremes and max-stable processes Symbolic/histogram/interval-valued data analysis Composite likelihood and aggregated data methods Tail density estimation via kernel methods Scientific Awards & Grants include: J.B. Douglas Award for Postgraduate Excellence (2014) Multiple ARC ACEMS Research Support Schemes Discovery Project DP220103269 ($405,000) for modeling real-world extremes Supervision covers PhD, Masters, and Honours students in areas like Symbolic Data Analysis, Spatial Extremes, and Statistical Computing. He also co-organized workshops and served as Vice-President (2025-26) of the Statistical Society of Australia's NSW Branch.

Roland Albert Matsouakaمشاهده پروفایل

دانشیار

• Causal Inference
• Biostatistics
• Clinical Trials
• +۸ مورد دیگر

Roland Albert Matsouaka is an Associate Professor in the Department of Biostatistics and Bioinformatics at Duke University. He serves as a Faculty Statistician at the Duke Clinical Research Institute (DCRI) and holds leadership roles as Associate Chair for Diversity and Inclusion and Co-Leader of the Outcomes, Endpoints, and Estimands (O2E) program at DCRI. His work spans methodological and applied research in biostatistics, with a focus on causal inference and comparative effectiveness. Education: Ph.D. in Biostatistics (2012), M.S. in Biostatistics (2007) from Harvard University. Research Interests: Matsouaka specializes in nonparametric and semiparametric methods , causal inference , and statistical techniques for clinical trials and observational studies. His applications span cardiovascular diseases , transplant medicine , and health equity . Publication Trends: Recent work includes statistical methods for social determinants of health , hierarchical endpoints in clinical trials , and transplant eligibility models . Collaborations leverage large registries like the Get With The Guidelines (GWTG) and STS-ACC Transcatheter Valve Therapy Registry . Mentorship: He mentors students and clinical fellows across Duke programs, including UrogynCREST and REACH Equity , with a focus on health disparities and data science . Grants: Current projects include studies on anticoagulant risks in stroke patients (NIH, UT Southwestern), transplant inequities (Vanderbilt), and stroke care quality (UCLA, MGH).

• Asymptotic Statistics
• Nonparametric Inference
• High-Dimensional Statistics
• +۳ مورد دیگر

Davy Paindaveine is a Professor of Mathematical Statistics at the Université Libre de Bruxelles (ULB), affiliated with ECARES and the Department of Mathematics. He is also an Associate Member of the Toulouse School of Economics. His academic career includes visiting professorships at Université Pierre-et-Marie Curie (2009-2014), KU Leuven (2015), and the Toulouse School of Economics (2015-2016, 2019-). Research Interests: Paindaveine’s work focuses on asymptotic statistics , nonparametric inference , and high-dimensional statistics . He explores Le Cam’s theory of asymptotic experiments, contiguity, and optimal statistical procedures. His contributions to directional statistics address inference on high-dimensional spheres, while his research on statistical depth and multivariate quantiles develops robust methods for complex data structures. Recent Publications highlight advancements in principal component directions under weak identifiability, spatial quantiles in high dimensions, and optimal tests for rotational symmetry. These works span journals like the Annals of Statistics , Bernoulli , and Journal of the American Statistical Association , reflecting his expertise in nonparametric and high-dimensional inference. Scientific Awards: Fellow of the Institute of Mathematical Statistics (2018) Fellow of the American Statistical Association (2019) Gottfried E. Noether Young Scholar Award (2007) Prix Adolphe Wetrems (2007) Prix MJLD (2003) Elected member of the International Statistical Institute Editorial Roles: Paindaveine served as Editor-in-Chief of the Bernoulli (2022-2024) and former editorial boards of the Annals of Statistics and Journal of the American Statistical Association . His teaching portfolio includes courses in Mathematical Statistics , Graduate Statistics , Probability , and Regression Models .

• Microeconomic Theory
• Decision Theory
• Game Theory
• +۵ مورد دیگر

Christopher P. Chambers is a Professor of Economics and Director of Graduate Studies (PhD) at Georgetown University. He holds a Ph.D. and M.A. in Economics from the University of Rochester and a B.S. in Mathematical Economics from the University of Maryland College Park. His primary appointment is in the Department of Economics where he conducts research at the intersection of economic theory and mathematical methods. Chambers' research spans multiple domains of economic theory with particular expertise in microeconomic theory, decision theory, and social choice. His work integrates rigorous mathematical approaches with economic reasoning to address fundamental questions about individual and collective decision-making. Key research areas include: Revealed preference theory and its empirical applications Decision theory under uncertainty and risk Aggregation of preferences and social welfare Game theory and mechanism design Welfare economics and distributive justice Mathematical foundations of economic behavior His recent publications demonstrate continued productivity and relevance, with papers appearing in top journals like Econometrica, Journal of Economic Theory, and American Economic Review. Chambers has made significant contributions to understanding the theoretical underpinnings of economic behavior, particularly through his work on revealed preference theory, including the influential book co-authored with Federico Echenique. His research often bridges theoretical insights with empirical applications, contributing to both methodological advances and policy-relevant analysis. Chambers has established a robust collaborative network, frequently working with leading economists including Federico Echenique, Takashi Hayashi, Alan Miller, and PJ Healy. These collaborations have produced influential work across multiple subfields of economic theory, demonstrating the interdisciplinary nature of his research.

Jean-Yves Bruaمشاهده پروفایل

مدرس

• Nonparametric Statistics
• Heteroscedastic Regression
• Ergodic Diffusion Processes
• +۲ مورد دیگر

Jean-Yves Brua is a Lecturer at the University of Rouen, affiliated with the Raphaël Salem Mathematics Laboratory (UMR 6085 CNRS). He holds administrative roles, including Director of the IREM of Rouen, Vice-President of ADIREM (Assembly of IREM Directors), and Head of the AIMAF Actuarial Master's program (Actuarial Science and Mathematical Engineering for Insurance and Finance). Expertise: Nonparametric statistics, heteroscedastic regression, ergodic diffusion processes, and actuarial science. Research Focus: Developing asymptotically efficient estimators and adaptive methods for complex stochastic models. Publications: Contributions to statistical methodology and nonparametric estimation in diffusion processes. Location: Office M.2.21, 2nd floor, Raphaël Salem Mathematics Laboratory, University of Rouen Normandy, Saint-Étienne-du-Rouvray, France.

Mohamed Amezzianeمشاهده پروفایل

استاد

• Model Selection
• Nonparametric Statistics
• Semiparametric Modeling
• +۳ مورد دیگر

Mohamed Amezziane is a Professor in the Department of Statistics, Actuarial and Data Sciences at the University of Central Florida, College of Science and Engineering. His work bridges statistical theory with computational methodologies. Ph.D. in Mathematics (2004), University of Central Florida M.S. in Statistical Computing (2000), University of Central Florida M.S. in Simulation Modeling and Analysis (2000), University of Central Florida Diplôme d’Ingénieur d’Etat en Génie Industriel (1996), Ecole Mohammadia d’Ingénieurs His research focuses on model selection , nonparametric statistics , and semiparametric modeling , with applications in computational statistics and data mining. His recent publications highlight innovative approaches to density estimation and distribution generation using characteristic functions. Best Paper Award, 8th International Conference on Digital Telecommunications Advancement Prize Award, 10th International Conference on Management Science and Engineering Management Professor Amezziane teaches courses in applied statistics, computational statistics, data mining, and statistical learning. He is a member of the International Statistical Institute.

Chin-I Chengمشاهده پروفایل

استاد

• Bayesian Statistics
• Nonparametric and Semiparametric Models
• Bayesian Smoothing Spline ANOVA
• +۱ مورد دیگر

Chin-I Cheng is a Professor and Chairperson in the Department of Statistics, Actuarial & Data Sciences at the College of Science & Engineering. His research focuses on Bayesian statistics, nonparametric models, and their applications across biomedical and social sciences. Ph.D., University of Missouri-Columbia (2009) His work spans public health disparities, cardiovascular education, textile engineering, and clinical pharmacology, with methodological contributions to smoothing spline ANOVA and Bayesian estimation. Scientific awards include the Arnold P. Gold Foundation Outstanding Doctoral Examination Award and multiple Quality Excellence Awards from Taiwan’s Industrial Development Bureau.

• Extreme Value Theory
• Environmental Statistics
• Climate Modeling
• +۳ مورد دیگر

Dr. Emma Eastoe is a Senior Lecturer in Statistics at the School of Mathematical Sciences, Lancaster University. Her research focuses on Extreme Value Theory and Environmental Statistics, with applications in climate modeling, oceanography, and environmental risk assessment. Current research projects include: Improved Models for Multivariate Metocean Extremes (IMEX, 2020–2021) STORi: Multivariate Oceanographic Extremes in Time and Space (2019–2023) Her work spans disciplines such as: Extreme value analysis of bivariate and multivariate processes Statistical downscaling of climate models Modeling non-stationary extremes in environmental data Applications to oceanographic, atmospheric, and glaciological systems She supervises PhD students including Kajal Dodhia and Aiden Farrell, and collaborates with research groups like the Data Science Institute (DSI) - Environment, Environmental and Ecological Statistics, and the STOR-i Centre for Doctoral Training.

• Optimisation
• Operational Research
• Applied Probability
• +۴ مورد دیگر

Jamie Fairbrother is a Lecturer in Operational Research (Optimisation) at the Data Science Institute , Lancaster University , within the Department of Management Science . His research integrates optimisation, applied probability, and statistics to solve real-world problems in logistics and telecommunications, with a particular focus on stochastic programming and scenario generation. Education: Jamie Fairbrother completed his PhD research in scenario generation for stochastic programming, which laid the foundation for his subsequent work in optimisation under uncertainty. Research Interests: His expertise spans several domains: Optimisation & Operational Research: Development of mathematical models and algorithms for complex decision-making problems. Applied Probability & Statistics: Use of probabilistic methods to model uncertainty in planning and operations. Logistics & Telecommunications: Practical applications in mail centre staffing, airport slot scheduling, and wireless communication networks. Stochastic Programming: Scenario generation and robust optimisation techniques for strategic planning under uncertainty. Research Funding & Projects: Dr Fairbrother has led or contributed to multiple collaborative projects, including: Coordination of Strategic and Tactical Interventions for Reducing Air Traffic Delays (2023–2024) – a case study at Heathrow Airport. STOR-i: Optimising In-Store Price Reductions (2022–2025) – with PhD student Katie Howgate. Resource Allocation under Uncertain Demand in Royal Mail Mail Centres (2020–2023). Industrial Mathematics KTP with BT Research (2013) – modelling TV whitespace interference. PhD Supervision: He currently supervises Katie Howgate , a PhD student in Bayesian and Computational Statistics within the STOR-i Centre for Doctoral Training . Professional Affiliations: Dr Fairbrother is affiliated with the STOR-i Centre for Doctoral Training , the Centre for Transport & Logistics (CENTRAL) , and the OR-MASTER project on airport resource allocation.