جستجوی استاد راهنمای دانشگاهی

Thomas C. M. Leeمشاهده پروفایل

استاد

• Nonparametric Modeling
• Semiparametric Modeling
• Statistical Learning
• +۴ مورد دیگر

Thomas C. M. Lee is a Distinguished Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis, within the College of Letters and Science. He holds a prominent position in the Department of Statistics and serves as a key academic leader at UC Davis. Education: B.App.Sc. (Math) from University of Technology, Sydney, Australia (1992) B.Sc. (Hons) (Math) with University Medal from University of Technology, Sydney, Australia (1993) Ph.D. from Macquarie University and CSIRO Mathematical and Information Sciences, Sydney, Australia (1997) Professor Lee's research spans multiple areas of statistics with a focus on developing innovative methodologies. His work particularly emphasizes nonparametric and semiparametric modeling , statistical learning , and statistical image and signal processing . He has made significant contributions to applying statistical methods across various scientific disciplines, demonstrating the versatility and power of statistical approaches in solving complex real-world problems. His research often bridges theoretical developments with practical applications, creating methodologies that are both mathematically sound and practically useful. Scientific Awards and Honors: Elected Fellow of the American Association for the Advancement of Science (AAAS, 2019) Elected Fellow of the American Statistical Association (ASA) Elected Fellow of the Institute of Mathematical Statistics (IMS) Elected Senior Member of the IEEE Professor Lee has held significant editorial roles including serving as Editor-in-Chief for the Journal of Computational and Graphical Statistics (2013-2015) and currently as Review Editor for the Journal of the American Statistical Association. From 2015 to 2018, he chaired the Department of Statistics at UC Davis. He has taught numerous statistics courses including STA 13 (Elementary Statistics), STA 131C (Introduction to Mathematical Statistics), STA 243 (Computational Statistics), and STA 401 (Statistical Consulting). His leadership extends beyond research to academic administration, where he has shaped statistics education and departmental direction at UC Davis.

Xihong Linمشاهده پروفایل

استاد

• Statistical Machine Learning and AI
• Statistical Genetics and Genomics
• Causal Inference and Mediation Analysis
• +۴ مورد دیگر

Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).

David A. Stephensمشاهده پروفایل

استاد

• Bayesian Inference
• Biostatistics
• Causal Inference
• +۳ مورد دیگر

David A. Stephens is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. He served as Chair of the Department from 2015 to 2019 and as Vice-Dean in the Faculty of Science from 2019 to 2025. His research focuses on Bayesian inference, biostatistics, causal inference, bioinformatics, and statistical genetics. He holds prestigious fellowships: International Statistical Institute (2015), American Statistical Association (2019), and Royal Society of Canada (2024). His work addresses challenges in epidemiology, HIV transmission dynamics, and clinical trial design. Key research themes include: Bayesian hierarchical modeling for infectious diseases (e.g., SARS-CoV-2, HIV) Causal inference in dynamic treatment regimes Survival analysis and censored data methods Statistical genomics and epigenetics His publications analyze public health trends, such as HIV transmission clusters in Quebec and SARS-CoV-2 seroprevalence in Canada. Methodologically, he develops novel techniques for time-series analysis, recruitment forecasting in clinical trials, and computational statistics. Notable contributions include: Advancing phylogenetic cluster inference in HIV studies Optimizing warfarin dosing strategies via SMART trials Modeling gut microbiota impacts on growth faltering in infants His academic leadership includes roles at McGill and prior experience at Imperial College London. His work bridges statistical theory and practical healthcare applications, emphasizing interdisciplinary collaboration.

Xiang Limingمشاهده پروفایل

دانشیار

• Survival Analysis
• Longitudinal/Clustered Data Analysis
• Mixture Modelling
• +۳ مورد دیگر

Assoc Prof Xiang Liming is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (Students). She holds editorial roles at *Computational Statistics & Data Analysis* and *Statistics in Medicine*. With a PhD in Statistics (City University of Hong Kong, 2002), her research focuses on survival analysis, longitudinal data analysis, and biostatistical methods. Notable contributions include methodologies for semi-competing risks, interval-censored data, and mixture models. Her work bridges statistical theory with biomedical applications, addressing challenges in clinical trials and public health. Awards include the 2009 IIE Transactions Best Paper Award and the Outstanding Research Thesis Award (2002–2003, CityU). Education: PhD in Statistics, City University of Hong Kong (2002) Postdoctoral Research: Hong Kong University of Science and Technology (2002–2003) and CityU (2003–2006) Research Interests: Survival analysis methodologies, including frailty models, cure models, and quantile regression for censored data. She develops robust statistical approaches for clustered/longitudinal data, addressing missingness and overdispersion. Applications span biomedical research, epidemiology, and quality management. Grants & Collaborations: Her grants include work on robotic-assisted stroke rehabilitation (2021) and LNG cold energy utilization systems (2017–2019). She collaborates with clinical teams on trials involving upper limb neurorehabilitation technologies. Labs & Teams: Leads statistical method development for multi-center clinical trials, particularly in biostatistics and survival analysis frameworks. Active in NTU’s School of Physical & Mathematical Sciences research initiatives.

Oliver Lintonمشاهده پروفایل

استاد

• Econometric Theory
• Empirical Finance
• Financial Econometrics
• +۲ مورد دیگر

Oliver Linton is the Chair of the Faculty and Professor of Political Economy at the University of Cambridge's Faculty of Economics. He coordinates the Empirical Analysis of Financial Markets theme at the Janeway Institute and holds a position at Trinity College. His research primarily focuses on econometric theory and empirical finance , with applications in market microstructure, asset pricing, and volatility modeling. His research interests span: Development of novel econometric methods for high-dimensional and dynamic data Analysis of financial market behavior, including liquidity and trading patterns Applications in policy-relevant contexts such as quantitative easing and pandemic forecasting Linton's recent publications demonstrate a strong focus on: Advanced time-series methodologies (e.g., GARCH, nonparametric regression) Financial market microstructure and high-frequency trading Economic impact analysis of major events (e.g., Brexit, COVID-19) He has received prestigious awards including: Humboldt Research Award (2015) Thousand Talents Plan recognition from Renmin University of China (2016) Linton actively advises doctoral students, with current supervisees including Xinyi Su, Zhaocheng Zhang, and Kilian Bachmair. He secured significant funding such as the European Commission FP7 grant for Nonparametric and Semiparametric Methods in Economics and Finance (2011–2014).

Eva Cantoniمشاهده پروفایل

استاد

• Robust statistics
• Variable and model selection
• Nonparametric regression
• +۶ مورد دیگر

Eva Cantoni is a Full Professor at the Research Center for Statistics within the Geneva School of Economics and Management , University of Geneva. Her expertise spans robust statistical methodology, model selection, and applications in ecology and medicine. Ph.D. from University of Geneva Accredited European Statistician (FENStatS) Research Interests : She specializes in Robust statistics for real-world data Variable/model selection in high-dimensional settings Nonparametric and semi-parametric regression Zero-inflated and overdispersed count models Longitudinal and spatiotemporal data analysis Her work addresses ecological challenges (fish stock assessment), medical applications (hospital congestion modeling), and housing market analysis. Recent Trends in Publications : Recent articles focus on Confidence intervals for robust mixed models Editorial leadership in robust statistics Applications to fisheries science and public health Flexible modeling frameworks for complex data Comparative studies of statistical measures Extremes modeling in healthcare Leadership & Grants : She has served as: Vice-Dean for Teaching (2020-2023) Director of Master's in Statistics (2012-2019) Director of Applied Statistics Certificate (2015-2019) President, Swiss Federal Statistics Committee (2024-2027) Specialty Chief Editor, Frontiers in Applied Mathematics (2024) Grants include projects on Robust solutions for modern data (2023-2025) Sustainable fisheries modeling (2018-2021) Advancements in state-space models (2014-2017) Software Contributions : Developed R packages for robust statistical methods: confintROB (bootstrap confidence intervals) RobSSM (robust state-space models) R2_LMM (explained variation measures)

• Survival Analysis
• Longitudinal Data Analysis
• High-Dimensional Data Analysis
• +۸ مورد دیگر

Bin Nan serves as Chancellor's Professor in the Department of Statistics at the University of California, Irvine, where he develops statistical and machine learning methodologies to advance biomedical research and improve human health outcomes through rigorous data analysis. His educational credentials demonstrate a strong quantitative foundation: Ph.D. in Biostatistics, University of Washington (2001) M.S. in Biostatistics, University of Washington (1999) M.S. in Statistics, Virginia Commonwealth University (1997) M.S. in Aerospace Engineering, Beijing University of Aeronautics & Astronautics (1987) B.S. in Aerospace Engineering, Beijing University of Aeronautics & Astronautics (1984) Nan's research program focuses on developing cutting-edge statistical methods for survival analysis, longitudinal data, high-dimensional inference, and machine learning, with direct applications to epidemiology, bioinformatics, and brain imaging. His work addresses critical challenges in biomedical data such as temporal dependence in neuroimaging sequences, estimation of large correlation matrices, and analysis of disease onset with terminal events, all aimed at identifying biomarkers for earlier disease diagnosis. Analysis of his recent publications (2015-2023) reveals a consistent trajectory toward methodological innovation in handling complex biomedical data structures, particularly through de-biased lasso techniques for survival models, neural network applications to censored data, and specialized approaches for longitudinal data with terminal events. These advances predominantly support Alzheimer's disease research and transplant outcome studies. No specific scientific awards were documented in the source material. His research program maintains continuous funding through National Science Foundation and National Institutes of Health grants, including a recent $1.8 million award for Alzheimer's disease methodology development. Nan actively collaborates with the UCI Alzheimer's Disease Research Center and UCI Center for the Neurobiology of Learning and Memory, though student advising details were not provided. His teaching portfolio includes advanced graduate courses in probability theory, survival analysis, and high-dimensional inference. Nan operates within interdisciplinary biomedical research teams focused on translating statistical innovation into clinical applications, particularly through brain imaging analysis and biomarker identification for neurodegenerative diseases.

Dylan Smallمشاهده پروفایل

استاد

• Causal Inference
• Design and Analysis of Experiments
• Observational Studies
• +۶ مورد دیگر

Dylan Small is the Universal Furniture Professor and Chair of the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. His expertise spans causal inference, observational study design, and statistical applications in public health and policy. PhD in Statistics (Stanford University, 2002) BA in Mathematics (Harvard University, 1997) His research focuses on causal inference methodology, measurement error in longitudinal studies, and health policy applications. He has advanced techniques for sensitivity analysis in observational studies and instrumental variable modeling. Recent publications analyze covariate imbalance in hormone therapy studies, zero-inflated treatment effects, and causal frameworks for global health interventions. His work bridges statistical theory with practical healthcare and policy solutions. Awards include: American Statistical Association Fellow (2013) IMS Medallion Lecturer (2022) He has served as Associate Editor for journals such as the Journal of Causal Inference and founded the journal Observational Studies. Current courses include advanced seminars on causal inference and observational study design.

• Semiparametric Estimation
• Nonparametric Regression
• Robust Statistics
• +۳ مورد دیگر

Dr. Serhan Sadikoglu is an Assistant Professor at Tilburg University's Tilburg School of Economics and Management (TiSEM), Department of Econometrics and Operations Research. He earned his PhD in Econometric Theory from Tilburg University in 2019. His research focuses on econometric theory, semiparametric estimation, robust statistics, and panel data analysis. He has published in prestigious journals such as Econometric Reviews and Econometrics Journal , with recent work addressing nonseparable panel models and robust estimation techniques. Sadikoglu teaches courses including Quantitative Methods, New Venture Creation and Growth, and Statistics for Econometrics. His research emphasizes methodological contributions to econometrics, particularly in handling complex data structures and improving estimation robustness.

Prosper Dovononمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +۲ مورد دیگر

Prosper Dovonon serves as a Full Professor in the Department of Economics at Concordia University in Montreal, Canada, where he holds a prestigious Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets. He previously held positions as Associate Professor (2015-2023) and Assistant Professor (2010-2015) at the same institution. Additionally, he maintains an adjunct professorship at the University of Adelaide's School of Economics since 2021 and previously served as a Visiting Professor at HEC Montreal's Department of Finance (2017-2018). His educational background includes a PhD in Economics from Universite de Montreal (2007), an MSc in Statistics and Economics from ENSEA, Abidjan, Cote d'Ivoire (2000), and an MSc in Mathematics from Universite Nationale du Benin, Abomey-Calavi, Benin (1996). Dovonon's research focuses on advanced econometric methodologies, particularly in time series analysis and financial econometrics. His work addresses complex identification issues, develops robust estimation techniques, and creates innovative testing procedures for economic models. He specializes in moment condition models, GMM estimation, volatility modeling, and handling identification failures in econometric frameworks. His publication record shows a consistent focus on theoretical econometrics with practical applications in finance. Recent work emphasizes mixed identification strength scenarios, instrument exogeneity testing, and specification testing under challenging identification conditions. His research demonstrates increasing sophistication in handling complex econometric problems with real-world financial data applications. His notable recognition includes the Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets, highlighting his significant contributions to the field. Dovonon has supervised numerous graduate students and collaborated extensively with leading econometricians worldwide. His research has been supported by institutional funding through his Research Chair position, enabling significant contributions to econometric theory and methodology. He maintains active research collaborations across international institutions and continues to push the boundaries of econometric theory with applications to financial markets and economic modeling.

• Mathematical Statistics
• Applied Statistics
• Probability Theory
• +۶ مورد دیگر

Holger Dette is a Professor and Chair Holder of Stochastics (specializing in Statistics) at the Faculty of Mathematics, Ruhr University Bochum. He leads the prominent Group Dette within the Institute of Statistics, overseeing a team of researchers, doctoral students, and administrative staff including Birgit Tormöhlen as team assistant. His research group is deeply integrated within the university's mathematical ecosystem, collaborating with other research groups across algebra, analysis, numerics, and topology. Dette's research spans mathematical statistics with strong applications in real-world problems. His primary interests include optimal experimental design, time series analysis, functional data, change point problems, nonparametric regression, biostatistics, special functions, goodness-of-fit tests, and random matrices . His work bridges theoretical statistics with practical applications, particularly evident in his collaborations with pharmaceutical giants Novartis and Bayer AG in biostatistics, as well as Quasol, a spin-off company from his statistics institute. His recent publications (2024-2025) reveal a research program increasingly focused on high-dimensional and functional data analysis, privacy-preserving statistics, and novel methodological approaches to longstanding statistical problems. Dette's work shows strong interdisciplinary connections, particularly with biomechanics (analyzing joint angles during fatigue phases) and data science (addressing challenges in the era of big data). His research group is actively involved in multiple DFG-funded projects including the newly established 'Small Data' collaborative research center (Sonderforschungsbereich 1597) and the Spatio-temporal Statistics for the Transition of Energy and Transport (Transregio 391). Dette has received significant recognition including the prestigious Humboldt Research Award . His paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors' achieved second place at the CSAW'24 Applied Research Competition MENA. His research group has also secured multiple significant funding awards from the German Research Foundation (DFG). As an advisor, Dette supervises numerous doctoral and master's students including Pascal Quanz, Marius Kroll, and Carina Graw. His group offers statistical consulting services for scientists and students across bachelor's, master's, and doctoral phases. The group maintains strong industrial partnerships, particularly in biostatistics applications, demonstrating Dette's commitment to translating theoretical statistics into practical solutions for real-world challenges.

Bryan S. Grahamمشاهده پروفایل

استاد

• Econometrics
• Network Analysis
• Development Economics
• +۴ مورد دیگر

Bryan S. Graham is a Professor of Economics at the University of California, Berkeley. He specializes in econometrics, focusing on network formation, social interactions, and panel data analysis. His research explores topics such as peer effects, poverty traps, and small sample properties of econometric methods. Graham holds a Ph.D. from Harvard University (2005) and has held visiting positions at Harvard, CEMFI (Spain), and NYU. He is an elected Fellow of the International Association of Applied Econometrics. Education highlights include a Rhodes Scholarship (1997–2000) at Oxford University, a Fulbright Scholarship (1997–1998) at the Australian National University, and a B.A. in Quantitative Economics from Tufts University (1993–1997). His work has been published in top journals like Econometrica and the Review of Economic Studies . Key awards include NSF grants (multiple), the Review of Economics Studies Tour, and the Daniel Ounjian Prize. Graham’s research has practical applications in policy analysis, particularly in education and social spillover effects. He also actively contributes to academic service, including editorial roles at Review of Economics and Statistics and Journal of Econometrics .

Refik Soyerمشاهده پروفایل

استاد

• Bayesian Statistics
• Reliability Modeling
• Decision Analysis
• +۴ مورد دیگر

Refik Soyer is a Professor of Statistics at The George Washington University. His research focuses on Bayesian statistics, reliability modeling, decision analysis, and time series analysis. He has made significant contributions to the application of Bayesian methods in reliability engineering, queueing systems, and adversarial risk analysis. Education: D. Sc. in Statistics (1985), George Washington University His recent publications highlight advancements in Bayesian reliability analysis, adversarial decision frameworks, and computational methods for time series and queueing systems. Areas of emphasis include dynamic INAR processes, accelerated life testing, and software failure modeling. Soyer's work bridges theoretical statistics with practical applications in call centers, healthcare fraud detection, and risk management.

Syed Ejaz Ahmedمشاهده پروفایل

استاد

• Big Data Analytics
• Statistical Machine Learning
• Predictive Modeling
• +۶ مورد دیگر

Syed Ejaz Ahmed is a distinguished Professor of Mathematics and Statistics at Brock University , with a career spanning multiple institutions including the University of Windsor, University of Regina, and University of Western Ontario. He serves as a Review Editor for Technometrics and holds editorial roles for several journals. PhD, Carleton University MSc, University of Guelph MSc in Statistics, University of Karachi BSc (Honors), University of Karachi His research focuses on big data analytics , statistical machine learning , and shrinkage estimation , with applications in healthcare, economics, and environmental science. He has organized international workshops on high-dimensional data analysis since 2011. Recent scientific contributions highlight advanced methods for high-dimensional regression, censored data analysis, and predictive modeling. His work has received international recognition through awards and fellowships. Fellow, American Statistical Association Fellow, Royal Statistical Society Elected Member, International Statistical Institute NSERC Discovery Grant (2017-2022) ISOSS Gold Medal A dedicated educator, he has supervised numerous PhD/MSc students and postdoctoral fellows. Currently, he leads the Centre for Business Analytics at Brock and collaborates with institutions worldwide through honorary professorships and visiting roles.

• 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.