جست‌وجوی استاد و پژوهشگر

Lang Wuمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Mixed Effects Models
• Biostatistics
• +۵ مورد دیگر

Lang Wu is a Professor in the Department of Statistics at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on biostatistical methods for analyzing complex health science datasets, particularly longitudinal data with missing values, censored observations, and measurement errors. He has expertise in constrained statistical inference, joint modeling of longitudinal and survival data, and applications in HIV/AIDS and cancer studies. Education: B.Sc. in Mathematics from East China Normal University (China), M.Sc. in Mathematics from Tulane University (USA), Ph.D. in Statistics from the University of Washington (USA), followed by a postdoctoral fellowship at Harvard University's Biostatistics department. Research Interests include: Longitudinal data analysis Mixed effects models Joint models linking survival and longitudinal outcomes Missing data imputation techniques Constrained hypothesis testing Biostatistical applications in infectious diseases Current advisees include Sihaoyu Gao and Qian Ye. Contact information includes lang@stat.ubc.ca (primary) and langwuubc@outlook.com (alternative). No scientific awards are listed, though his extensive publication record indicates impactful contributions to statistical methodology in health sciences.

Wei Liuمشاهده پروفایل

دانشیار

• Biostatistics
• Longitudinal Data Analysis
• Survival Analysis
• +۴ مورد دیگر

Wei Liu is an Associate Professor in the Department of Mathematics and Statistics at York University, Faculty of Science. His research focuses on advanced statistical methodologies including longitudinal data analysis, survival analysis, missing data imputation, and constrained statistical inference with applications in biostatistics. He holds a joint appointment in the Biostatistics sub-discipline and contributes to the UN Sustainable Development Goal of Good Health and Well-being through his work. Research Interests: Analysis of longitudinal and survival data, missing data mechanisms (including dropouts and measurement errors), change point detection, order-restricted hypothesis testing, and multiple imputation techniques. Contact Information: Office located in Ross Building N601B. Reachable via phone at (416) 736-2100 ext. 33767 or email liuwei@yorku.ca.

Yongseok Parkمشاهده پروفایل

استادیار

• Bioinformatics
• Cancer Epigenetics
• Genomic Data Analysis
• +۴ مورد دیگر

Yongseok Park is an Assistant Professor in the Department of Biostatistics at the University of Pittsburgh. His research focuses on epigenetic and high-throughput genomic data analysis in cancer, including the development of statistical methods for DNA methylation analysis, RNA sequencing, and clinical trial design. He has created computational tools like methylSig for whole-genome methylation analysis and developed methodologies for cancer immunotherapy trial design. Education Ph.D. from University of Michigan-Ann Arbor (2011) M.S. from University of Michigan-Ann Arbor (2007) Master of Engineering from Tsinghua University, Beijing Bachelor of Engineering from Shanghai Jiao Tong University, Shanghai Research Focus Dr. Park's interdisciplinary research integrates biostatistics, bioinformatics, and oncology. Primary areas include: Epigenetic analysis : Developing pipelines for whole-genome DNA methylation data Genomic methodologies : RNA-seq analysis and integrative omics approaches Clinical applications : Statistical designs for cancer immunotherapy trials Theoretical statistics : Survival analysis, nonparametric methods, and stochastic ordering Teaching Experience BIOST 2083: Linear Models (2014-2020) BIOST 2036: Introduction to Health Data Science (2020) BIOST 2063: Bayesian Data Science (2021) BIOST 2025: Biostatistics Seminar (2014-2015) Research Publications Dr. Park's publications demonstrate consistent focus on statistical methodology development for genomic data analysis and clinical trial design. Recent work emphasizes DNA methylation analysis pipelines (methylSig, MethylSeqDesign), cancer immunotherapy trial methodologies, and integrative omics approaches. Theoretical contributions include innovations in survival analysis and nonparametric estimation under order restrictions.

Linnaea Stockallمشاهده پروفایل

استاد

• Morphology
• Lexical Semantics
• Event Semantics
• +۵ مورد دیگر

Professor Linnaea Stockall is a Professor of Linguistics and Cognitive Neuroscience at Queen Mary University of London, affiliated with the School of Languages, Linguistics and Film and the Department of Linguistics. She directs the Morphemes and Meaning Lab, focusing on morphological and semantic processing mechanisms. Her research integrates experimental, neurolinguistic, and cross-linguistic approaches to understand word formation, lexical access, and syntactic-semantic integration. Stockall holds a PhD from MIT and specializes in psycholinguistics, neurolinguistics, and morphology. Her work explores how morphological constituents are stored, decomposed, and recombined during language processing, with particular attention to irregularity, event semantics, and the interaction of roots with functional morphemes. She has conducted studies using MEG neuroimaging and masked priming paradigms in languages like Tagalog, Mandarin, and Arabic. Her research emphasizes understudied languages and developing resources for cross-linguistic research. She supervises PhD/postdoctoral researchers in areas connected to her expertise, requiring clear alignment with her focus on morpho-syntactic and semantic processing. She is also involved in public engagement initiatives, such as the Teach Real English! project. Key contributions include investigations into mass/count distinctions, morphological decomposition in neuroimaging studies, and sociolinguistic effects of taboo language. She collaborates widely, with recent projects addressing lexical semantics, aspectual interpretations, and the impact of grammatical gender on societal perceptions.

Yazhen Wangمشاهده پروفایل

استاد

• Financial statistics
• financial data science
• quantum computing
• +۸ مورد دیگر

Yazhen Wang is a Professor & George Box Distinguished Chair in the Department of Statistics at the University of Wisconsin–Madison. He is affiliated with the School of Computer, Data & Information Sciences. His primary research focuses on advanced statistical methodologies and interdisciplinary applications. Research interests include financial statistics, quantum computing, high-dimensional inference, machine learning, and various statistical techniques like wavelets and nonparametric smoothing. These areas emphasize both theoretical development and practical data science challenges. Awards: Holder of the prestigious George Box Distinguished Chair. No specific grants or advising details are listed here. His work is centered at the 1220A Medical Sciences Center in Madison, WI.

Franz Baaderمشاهده پروفایل

استاد

• Knowledge Representation
• Logical Reasoning
• Description Logics
• +۵ مورد دیگر

Franz Baader is a Professor of Automata Theory at the Faculty of Computer Science, Technical University of Dresden. He is affiliated with the International Center for Computational Logic (ICCL) and serves as Chair of Automata Theory within the Institute of Theoretical Computer Science. Research areas: Knowledge representation, logical reasoning, description logics, and automata theory Projects: HAEC B02, HAEC B08, QuantLA Committee roles: Steering Committee member of CADE (2002-2013) and DL Workshop (2000-2002; 2008-2011) His recent work focuses on description logics with concrete domains, analyzing their expressive power, decidability, and computational complexity. He has extensively studied cardinality constraints in knowledge representation systems. Key themes in his publications include: formal verification, logical inference, knowledge graphs, and ontological reasoning with access restrictions. Many papers examine the intersection of automata theory and description logics. Contact: baader@tcs.inf.tu-dresden.de Office: APB 3021, Dresden

Martin Singullمشاهده پروفایل

استاد

• Mathematical Statistics
• Multivariate Statistical Analysis
• Statistical Inference
• +۷ مورد دیگر

Martin Singull is a Professor and Head of Division in Applied Mathematics at the Department of Mathematics, Faculty of Science and Engineering, Linköping University. He has held academic positions at Linköping University since 2012, progressing from Assistant Professor to full Professor in 2020, and serving in leadership roles since 2016. His research is centered on mathematical statistics, particularly statistical inference for complex, high-dimensional data with repeated measurements. His research interests include: Statistical inference for repeated measurements and growth curve models Classification and discriminant analysis Multivariate statistical analysis High-dimensional data modeling Applications in public health, cardio-oncology, and development contexts Martin Singull's recent publications focus on likelihood-based classification, Edgeworth-type expansions for distribution approximation, residual analysis in GMANOVA-MANOVA models, and the estimation of misclassification probabilities. His work often involves collaboration with Dietrich von Rosen and other researchers, applying advanced statistical techniques to both theoretical and applied problems. A strong trend in his research is the development of efficient classifiers using temporal and spatial information in longitudinal data. His scientific service includes: Director for the Research School in Interdisciplinary Mathematics (2024–) Chair of the organizing committee for IWMS 2023 and LinStat2014 Team Leader for Sida-funded bilateral programs in mathematics with universities in Africa Member of the ISP Mathematics Reference Group Board member of the Faculty of Science and Engineering Martin Singull has supervised numerous PhD students, both at Linköping University and in collaborative programs in Rwanda, Uganda, Tanzania, Mozambique, and Cambodia. His advising spans theoretical statistics and applied interdisciplinary research. He leads research collaborations aimed at strengthening mathematical capacity in low-income countries, particularly in Africa. His work is supported by funding from Sida and ISP, and he is actively involved in international academic networks such as the International Workshop on Matrices and Statistics (IWMS). He is also associated with research initiatives in computational cardio-oncology, where statistical models are used to predict cardiovascular complications in cancer survivors, and has contributed to projects on fistula prevention and health outcomes in low-resource settings.

• Bayesian Statistics
• Computational Statistics
• Categorical Data Analysis
• +۵ مورد دیگر

DZOUFRAS IOANNIS is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), affiliated with the School of Information Sciences and Technology. He has held this position since December 2015 and has previously served as Assistant Professor (2004-2011) and Lecturer at multiple institutions. Education: PhD in Statistics (AUEB, 1999) Master's in Statistics (University of Southampton, 1995) BSc in Statistics & Actuarial Science (University of Piraeus, 1994) His research focuses on Bayesian and computational statistics, emphasizing categorical data analysis, model building, variable selection methodology, and applications to medical research, psychometrics, and sports data. His work has appeared in top journals like Bayesian Analysis , Statistics and Computing , and Psychometrika . Scientific Awards & Recognition: Lefkopoulio Prize for Best Doctoral Thesis in Statistics (1999-2000) Honorable Mention at AAP's PROSE Awards (2010) He has contributed to international collaborations through conferences with Italian universities and co-organized academic workshops in Greece.

• Model Selection
• Continuous-Time Models
• Dynamic Modelling
• +۷ مورد دیگر

Dr. Rebecca Kuiper is an Associate Professor at the Department of Methods and Statistics within the Social and Behavioural Sciences faculty at Utrecht University. She specializes in model selection (via GORIC/GORICA), continuous-time dynamic modeling , and evidence synthesis across heterogeneous studies. Key Roles: PhD supervisor, software developer (CTmeta, GORICA), coordinator of summer school courses Methodological Focus: Lagged-effects models, Bayesian updating, theory-based hypothesis evaluation Research Interests Her work bridges statistics and social sciences , emphasizing: Development of GORICA for order-constrained model selection Continuous-time meta-analysis (CTmeta) for longitudinal studies Time-series analysis of intensive longitudinal data Bayesian evidence synthesis across conceptually replicated studies Scientific Contributions Notable achievements include: Receiving the NWO Aspasia grant (2022) for evidence-aggregation methods VENI-grant (2016-2020) for lagged-effects modeling Developing open-source R packages (CTmeta, ChiBarSq.DiffTest, ICweights) Advising & Collaborations Current PhD students: Chuenjai Sukpan (2019–present) Oisín Ryan (2018–present) Yasin Altinisik (2014–present) She also provides statistical consulting to students and staff at Utrecht’s Faculty of Social and Behavioral Sciences.

Antar Bandyopadhyayمشاهده پروفایل

استاد

• Probability Theory
• Theoretical Statistics
• Random Graphs
• +۷ مورد دیگر

Antar Bandyopadhyay is a Professor at the Theoretical Statistics and Mathematics Division of the Indian Statistical Institute (ISI), currently serving as Head of the Delhi Centre (May 01, 2023 - present). He has previously served as Professor-in-Charge of the Theoretical Statistics and Mathematics Division (September 18, 2020 - September 17, 2022). His academic journey includes a Ph.D. in Statistics from the University of California, Berkeley (2003), postdoctoral studies at the Institute for Mathematics and Its Applications (University of Minnesota) and Chalmers University of Technology (Sweden). Professor Bandyopadhyay's research focuses on theoretical and applied probability, with emphasis on discrete problems arising from combinatorics, statistical physics, and computer science. His specific interests include random graphs, probability on trees, combinatorial optimization, recursive distributional equations, branching random walks, percolation theory, interacting particle systems, Markov chains, random walks in random environments, and urn models. His work demonstrates a strong connection between theoretical developments and applications in various scientific domains. Analysis of his recent publications reveals a consistent focus on probability theory with particular emphasis on urn models, branching random walks, and random processes on graphs and trees. His research shows a progression from foundational work on recursive distributional equations to more complex applications in network theory and statistical physics. The publications demonstrate significant contributions to theoretical probability with practical implications in fields such as epidemiology, combinatorial optimization, and stochastic geometry. Outstanding Graduate Student Instructor Award from UC Berkeley (2002) Teaching Effectiveness Award from UC Berkeley (2002) Professor Bandyopadhyay has supervised multiple Ph.D. students including Deborshi Das (expected 2026), Partha Pratim Ghosh (2022), Gursharn Kaur (2018), Debleena Thacker (2015), and Farkhondeh Sajadi (2013). He has also guided M.Stat. dissertation students including Somak Laha (2021-2022) and Subhabrata Sen (2012-2013). His collaborative work spans numerous institutions including UC Berkeley, Chalmers University, and various Indian Statistical Institute centers.

Adam M Rosenمشاهده پروفایل

استاد

• Econometrics
• Instrumental Variable Methods
• Tobit Models
• +۴ مورد دیگر

Adam M Rosen is a Professor of Economics at Duke University's Trinity College of Arts & Sciences. His research focuses on econometric methodology, particularly instrumental variable (IV) methods, partial identification, and discrete choice modeling. He holds a Ph.D. from Northwestern University (2006). Rosen's work spans theoretical advancements in IV models, Tobit models, and structural estimation of economic games, with applications to policy evaluation and applied microeconomics. His recent publications emphasize extending IV frameworks to nonlinear settings and addressing identification challenges with imperfect instruments. He has contributed to software tools for econometric analysis, including Stata packages for intersection bounds and imperfect instrument applications. Rosen also serves as Director of Graduate Studies in Economics at Duke and teaches advanced courses in econometrics. Key affiliations: International Research Staff at CeMMAP, International Research Fellow at IFS. Notable contributions include 'Generalized Instrumental Variable Models' (Handbook of Econometrics) and 'Partial Identification in Applied Research.' Research interests include bounding wage distributions, robust analysis of short panels, and counterfactual analysis. His work often bridges econometric theory and practical applications, with a focus on causal inference under incomplete information.

Joshua M. Tebbsمشاهده پروفایل

استاد

• Categorical Data Analysis
• Biostatistics
• Group Testing Methods
• +۲ مورد دیگر

Dr. Joshua M. Tebbs is a Professor in the Department of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds a BS in Mathematics, an MS in Statistics, and a PhD in Statistics from the University of Iowa and North Carolina State University, respectively. His research focuses on categorical data analysis, statistical methods for group testing, order-restricted inference, and applications in public health and biostatistics. He is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute. Dr. Tebbs has served as Editor of the American Statistician (2020–2023) and contributed to numerous academic courses, including STAT 513 (Theory of Statistical Inference), STAT 512 (Mathematical Statistics), and STAT 110 (Introduction to Statistical Reasoning). His work emphasizes methodological advancements in group testing for disease prevalence estimation and has been supported by NIH funding. Key awards include recognition from the ASA and ISI, reflecting his scholarly contributions. His research outputs span statistical methodology, computational tools (e.g., binGroup2 ), and applications in infectious disease surveillance and public health decision-making.

Dewei Wangمشاهده پروفایل

دانشیار

• Biostatistics
• Epidemiology
• Group Testing
• +۲ مورد دیگر

Dewei Wang is an Associate Professor in the Department of Statistics at the University of South Carolina's McCausland College of Arts and Sciences. His research program focuses on public health challenges through advanced statistical methods. Primary interests include pooled biomonitoring, group testing, and regression techniques for epidemiological and environmental health data. Recent publications emphasize disparities in chemical exposures and novel approaches for censored survival data. His methodological articles consistently address real-world health applications, with recurring themes in exposure assessment and stochastic modeling across 2021-2024 publications.

• Econometrics
• Financial Economics
• Causal Inference
• +۵ مورد دیگر

Alessio Sancetta is a Professor of Economics at the Department of Economics, Royal Holloway, University of London, and Chair of the Department. He co-founded and co-directs the Centre for Robust Inference in a Digital Economy (RIDE). His research focuses on econometrics, causal inference, financial economics, and machine learning applications in finance. He earned his PhD in Econometrics from the University of Cambridge in 2002. Education: PhD in Econometrics, University of Cambridge (2002). Professional experience includes roles as a University Lecturer at the University of Cambridge and a trader in investment banks and proprietary trading firms, specializing in algorithmic trading strategies. Research interests span high-dimensional estimation, market microstructure, asset pricing, and forecasting methods. He has co-authored influential papers on topics like price drift before macroeconomic news and empirical asset pricing with functional factors. His work has been featured in major financial media such as Bloomberg and the Financial Times. He previously directed the MSc Finance program and co-directed the joint MSc Computational Finance with the Computer Science Department. Current projects include research funded by the Leverhulme Trust on information content in high-frequency trading and market manipulation in cryptocurrencies. His academic contributions include over 35 peer-reviewed articles in journals like the Journal of Econometrics, Journal of Financial Econometrics, and IEEE Transactions on Information Theory.

Calvin Berryمشاهده پروفایل

دانشیار

• Decision Theory
• Bayesian Statistics
• Linear Models
• +۳ مورد دیگر

Calvin Berry is Associate Professor of Mathematics at the University of Louisiana at Lafayette, specializing in statistical decision theory, Bayesian methods, and multivariate analysis. He holds a PhD in Statistics from Cornell University and teaches across the statistics curriculum. Research focuses on: Decision-theoretic approaches to estimation Bayesian inference in restricted parameter spaces Improvements to classical estimators Multivariate distribution theory Publications frequently address minimax estimation, equivariant estimators, and enhancements to established methods like the James-Stein estimator. Berry developed original course materials including textbooks on probability and statistics used in undergraduate and graduate instruction. He provides statistical consulting services to university researchers while maintaining an active research program in theoretical statistics.