جست‌وجوی استاد و پژوهشگر

• Survival Analysis
• Semiparametric Methods
• Statistical Learning
• +۴ مورد دیگر

Robert L. Strawderman is the Department Chair and Donald M. Foster, MD Distinguished Professor of Biostatistics at the University of Rochester's Department of Biostatistics and Computational Biology. His research focuses on survival analysis, semiparametric methods for censored/missing data, statistical learning for medical prediction, and healthcare quality evaluation. Applications span cancer, psychiatry, and neurological diseases like Huntington’s. He has contributed to over 100 peer-reviewed articles, including influential work on robust Q-learning and censoring-unbiased regression trees. Key achievements include the 2018 Best Paper in Biometrics for his work on renewal process analysis. His interdisciplinary research bridges statistical theory and clinical practice, with notable studies on forensic community treatment affordability and tumor collagen analysis for metastasis prediction. Education background includes a ScD in Biostatistics. He leads URMC’s biostatistics department, offering consulting services for medical researchers. His work emphasizes translational research, integrating advanced statistical methods into healthcare decision-making, cost analysis, and outcome optimization.

Taisuke Otsuمشاهده پروفایل

استاد

• Econometrics
• Nonparametric and Semiparametric Methods
• Microeconometrics
• +۵ مورد دیگر

Taisuke Otsu is a Professor of Econometrics at the Department of Economics, London School of Economics and Political Science (LSE), and an affiliated researcher at Keio University's Keio Economic Observatory. His expertise includes nonparametric and semiparametric methods, microeconometrics, causal inference, and structural analysis. He holds a PhD in Economics from the University of Wisconsin-Madison. His research focuses on developing methodologies for causal inference, structural economic models, and high-dimensional data analysis. Notable contributions include advancements in empirical likelihood estimation, nonparametric instrumental variables, and econometric methods for policy evaluation. He has also contributed to game theory applications and network data analysis. Recent work emphasizes causal inference under complex data environments, such as spatial competition models and functional covariates. Otsu collaborates widely, with projects funded by the Keio Economic Observatory, and has published over 70 peer-reviewed articles in top journals like the Journal of Econometrics and Econometric Theory . He teaches courses on econometric analysis, advanced econometrics, and pre-sessional econometrics for graduate students. His academic leadership includes roles at LSE and organizing research initiatives on Big Data and structural econometric models.

Yanxun Xuمشاهده پروفایل

دانشیار

• Biostatistics
• Machine Learning
• Nonparametric Bayes
• +۴ مورد دیگر

Yanxun Xu is an Associate Professor of Applied Mathematics and Statistics at the Whiting School of Engineering , Johns Hopkins University, and an Adjunct Assistant Professor in the Division of Biostatistics and Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center. She is also a member of the Data Science and Artificial Intelligence Institute . Her research focuses on developing Bayesian statistical methods and machine learning algorithms to address challenges in heterogeneous, large-scale datasets, particularly in clinical trials, electronic health records, cancer genomics, and HIV/AIDS studies. Key areas of expertise include nonparametric Bayes , reinforcement learning , and dynamic treatment regimes . Her work emphasizes precision medicine applications, such as optimizing antiretroviral therapies for HIV patients and analyzing cognitive outcomes in clinical trials. Funding sources include the National Science Foundation (NSF) , National Institutes of Health (NIH) , and industry partners like AstraZeneca. Xu has published over 80 peer-reviewed articles and received prestigious awards, including the 2016 Mitchell Prize from the International Society for Bayesian Analysis. Her research bridges statistical theory with real-world healthcare applications, addressing topics like hospital-level variations in COVID-19 treatment and the impact of inflammation biomarkers on neurocognitive functions in psychosis. PhD in Biostatistics (implied by academic rank) Recipient of JHU’s Center for AIDS Research Faculty Development Award Developed open-source software tools for causal inference and longitudinal data analysis

Charisios Grivasمشاهده پروفایل

استادیار

• Econometrics
• Statistical Methods
• Non-parametric Econometrics
• +۷ مورد دیگر

Charisios Grivas is an Assistant Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research lies at the intersection of econometrics, statistics, and environmental modeling, with a strong emphasis on methodological rigor and robust inference. His primary research interests include Econometrics , Statistical Methods , Non-parametric Econometrics , Resampling , Robust Estimation , and Measurement Error Models . He develops and applies advanced statistical techniques to address challenges in linear and time-varying models, particularly in the presence of data imperfections such as measurement errors. The recent articles demonstrate a consistent focus on improving inference in econometric models—especially through automated bandwidth selection, testing for nonlinear dependence, and robust estimation under measurement error. These works span applications in both theoretical econometrics and environmental research, notably in estimating the carbon dioxide airborne fraction. Scientific Awards: No scientific awards mentioned in the provided text. Charisios Grivas actively collaborates with researchers such as Z. Psaradakis and J.E. Vera-Valdés. While no formal advisees are listed, his publications and ongoing research output suggest active supervision and mentorship. There is no mention of specific grants, but his work on measurement error and environmental statistics may be supported by external funding. His research contributes to methodological advancements with practical implications in climate science and economics.

Marco Caroneمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Epidemiological Methods
• +۵ مورد دیگر

Marco Carone is a Professor of Biostatistics and Adjunct Professor of Statistics at the University of Washington, holding the Norman Breslow Endowed Faculty Fellowship. He is an Affiliate Investigator at the Fred Hutchinson Cancer Research Center’s Vaccine and Infectious Disease Division. His research focuses on causal inference, survival analysis, and nonparametric methods, with applications in vaccine science, environmental health, and public health. Carone earned his PhD in Biostatistics from Johns Hopkins University and completed postdoctoral training at UC Berkeley. Education: PhD in Biostatistics, Johns Hopkins University (2011) Hon. B.Sc. in Probability and Statistics, McGill University (2005) Diploma of College Studies in Pure and Applied Sciences, Marianopolis College (2002) Research Interests: Development of robust statistical methods for causal inference and nonparametric estimation Analysis of vaccine efficacy and infectious disease data Integration of machine learning with traditional biostatistical frameworks Applications in environmental epidemiology and aging research His publications span topics including survival analysis, immune correlates of vaccine efficacy, and statistical methodologies for high-dimensional data. Carone has advised over 15 PhD students, many of whom hold academic and industry roles. Awards: Norman Breslow Endowed Faculty Fellowship (2019) UW Teaching Award (2018) Phi Beta Kappa and Delta Omega Honor Societies (2011) Teaching & Service: Teaches courses on survival analysis, statistical inference, and biostatistical consulting Associate Editor for Biometrics and Journal of Causal Inference Peer reviewer for leading journals and grant agencies

Ciprian Crainiceanuمشاهده پروفایل

استاد

• Biostatistics
• Functional Data Analysis
• Wearable and Implantable Technology
• +۱۵ مورد دیگر

Ciprian Crainiceanu is a Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. His research spans biostatistical methodology and applications in public health, with a focus on high-dimensional data from wearable devices and medical imaging. Education: PhD, Cornell University, 2003 MS, University of Bucharest, 1998 His research interests include functional data analysis, measurement error, longitudinal modeling, Bayesian inference, and nonparametric statistics, with applications in sleep, aging, multiple sclerosis, Alzheimer’s disease, and cancer. He develops statistical tools tailored to complex data from accelerometers, neuroimaging (MRI, CT, SPECT), and surgical monitoring. His recent work involves dynamic prediction models, step-counting algorithms for NHANES and ARIC data, and methods for high-dimensional functional and imaging data. The most recent publications highlight advancements in wearable data analysis, medical imaging platforms like Neuroconductor, and novel resampling methods such as the upstrap. His work integrates statistical theory, software development, and interdisciplinary collaboration. Scientific Awards: Fellow of the American Statistical Association (ASA) Chair, Statistics in Imaging Section of ASA (two terms) Chair, Biostatistics Methods and Research Design (BMRD) NIH review section Crainiceanu is actively involved in mentoring, teaching, and collaborative research. He co-founded the SMART (Statistical Methods and Applications for Research in Technology) research group and Neuroconductor, fostering interdisciplinary innovation. His work emphasizes scalable, software-backed methods and close collaboration with domain scientists. He has led methodological developments in variance components testing, functional regression, population value decomposition, and dynamic prediction, applied to real-world health challenges. Labs and Research Groups: Co-founder, SMART (Statistical Methods and Applications for Research in Technology) Co-founder, Neuroconductor (open-source platform for medical imaging in R) Wearable and Implantable Technology (WIT) group MAGIC (Methods and Applications Group for Imaging in the Clinic)

David S. Leeمشاهده پروفایل

استاد

• Econometrics
• Labor Economics
• Public Policy Analysis
• +۴ مورد دیگر

David S. Lee is a Professor of Economics and Public Affairs at Princeton University, serving as Director of IDEAS for Higher Education. His research focuses on econometric methodologies, labor economics, and public policy analysis, with notable contributions to causal inference techniques like regression discontinuity and kink designs. He has published extensively on topics including minimum wage policy, union impacts on firms, and income inequality. Lee has held affiliations with institutions like UC Berkeley and NBER, and his work has earned prestigious awards such as the Emerald Literati Award and the Journal of Econometrics Aigner Award. Key research areas include developing robust statistical methods for instrumental variable analysis, evaluating policy effects through quasi-experimental designs, and analyzing labor market dynamics. His recent work addresses healthcare resource allocation and machine learning applications in healthcare prediction. Lee's articles frequently bridge theoretical econometric advancements with applied policy questions, emphasizing practical relevance for economic decision-making. Education : Implied academic background based on career trajectory (not explicitly stated in text). Awards : Emerald Literati Award (2018), Aigner Award (2007-2008). Grants/Advising : Active in directing research initiatives like IDEAS for Higher Education, with grant support from NBER and other institutions. He advises on policy-relevant research and collaborates widely, evidenced by co-authored works with economists like David Card and Justin McCrary. His lab focuses on methodological innovations and their application to real-world economic challenges.

Taiji Suzukiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +۶ مورد دیگر

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.

Zhonghua Liuمشاهده پروفایل

استادیار

• Causal Inference
• Machine Learning
• Statistical Genetics
• +۴ مورد دیگر

Dr. Zhonghua Liu is an Assistant Professor of Biostatistics at Columbia University's Mailman School of Public Health. He holds affiliations with Columbia's Data Science Institute (DSI) across four research centers: Foundations of Data Science, Health Analytics, Computational Social Science, and Computing Systems for Data-Driven Science. He earned his Ph.D. in Biostatistics from Harvard University under the supervision of Professor Xihong Lin. His research program focuses on developing and applying novel statistical methods for causal inference, machine/deep learning theory, and high-dimensional data. Core interests include: Causal mediation analysis for genomic and clinical data Semiparametric efficiency theory in model optimization Mendelian randomization for genetic epidemiology Survival analysis with high-dimensional biomarkers Deep learning integration with traditional biostatistical frameworks Recent publications (2022-2025) demonstrate a strong methodological focus on causal inference in genomics and precision medicine. Key themes include: 1) Robust Mendelian randomization techniques for weak genetic instruments, 2) Machine learning-enhanced mediation analysis frameworks, and 3) Integration of multi-omics data for causal pathway discovery. Methodological innovations frequently target applications in Alzheimer's disease, cardiovascular epidemiology, and COVID-19 outcomes.

Qingyuan Zhaoمشاهده پروفایل

استادیار

• Causal Inference
• Biostatistics
• Social Sciences
• +۲ مورد دیگر

Professor Qingyuan Zhao is a University Assistant Professor in Statistics at the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge. He previously held a postdoctoral fellowship at the Wharton School, University of Pennsylvania, and is currently affiliated with the Statistical Laboratory at Cambridge. Born in Wuhan, China, Zhao earned his BSc in Mathematics from the University of Science and Technology of China and a PhD in Statistics from Stanford University. Research Focus: His work centers on causal inference, particularly using Mendelian randomization and graphical models to analyze complex relationships in biomedical and social sciences. He develops statistical methodologies for observational studies, adaptive experiments, and high-dimensional data analysis. Publications: Zhao's recent research explores causal mediation analysis, off-policy evaluation, confounder selection, and sensitivity analysis in Mendelian randomization. His methodological contributions include matrix algebra for graphical models and iterative graph expansion techniques. Academic Roles: He serves as a Fellow and Director of Studies in Mathematics at his college, contributing to education and academic governance in mathematics and statistics.

• Econometrics
• Statistics
• Nonparametric Methods
• +۳ مورد دیگر

Christoph Breunig is a Professor at the Department of Economics, University of Bonn, with research focused on Econometrics. He is affiliated with the Institute for Financial Economics & Statistics at the university. His primary research interests include Econometrics, Statistics, Nonparametric Methods, Instrumental Variables, Treatment Effects, and Missing Data Analysis. Professor Breunig's work demonstrates a strong focus on methodological developments in econometric theory with applications to economic questions. His publication record shows a consistent output of high-quality research in top econometrics journals including Econometrica, Journal of Econometrics, and Quantitative Economics. His research trajectory demonstrates progression from foundational work on nonparametric methods and instrumental variables toward more complex problems involving treatment effects, missing data, and high-dimensional settings. Professor Breunig has established himself as a contributor to the field of econometric theory with particular expertise in nonparametric and semiparametric methods. His work often addresses identification and estimation challenges in complex economic models.

Stanislav Volgushevمشاهده پروفایل

دانشیار

• Data Science
• Probability
• Theoretical Statistics
• +۷ مورد دیگر

Stanislav Volgushev is an Associate Professor in the Department of Statistical Sciences at the University of Toronto, with a cross-appointment in the Department of Mathematical & Computational Sciences at the University of Toronto Mississauga (UTM). His work bridges theoretical statistics with practical applications in big data analysis, with a focus on developing robust statistical methods for complex data structures. His educational background includes: Ph.D. in Mathematics from Ruhr University Bochum (2010), advised by Prof. Holger Dette Diploma in Mathematics from Ruhr University Bochum (2007) Volgushev's research spans multiple areas of statistical theory and methodology. His primary interests include quantile regression, empirical process theory, extreme value theory, time series analysis, copulas, and bootstrap methods. He is particularly focused on addressing statistical and computational challenges presented by big data, developing methods that maintain theoretical rigor while being practically applicable to modern data analysis problems. His work often involves the development of nonparametric and semiparametric methods that can handle complex dependence structures. Analysis of his recent publications reveals a strong focus on high-dimensional statistics, with particular attention to change-point detection, extremal graphical models, and quantile-based methods. His work bridges theoretical statistics with machine learning, showing increasing interest in optimization methods for statistical learning and causal inference. There's a clear trajectory toward addressing computational challenges in modern statistics, with many papers developing methods specifically designed for big data contexts. Volgushev serves as an associate editor for several prestigious journals: Electronic Journal of Statistics (since 2022) Canadian Journal of Statistics (since 2022) Scandinavian Journal of Statistics (since 2021) Statistical Inference for Stochastic Processes (since 2014) Previously served at Computational Statistics and Data Analysis (2018-2023) and Bernoulli (2016-2018) He has taught various courses including STA107 (Introduction to Probability and Modelling), STA2211 (Probability Theory II), and STA314 (Introduction to Statistical Learning) at both the St. George and Mississauga campuses. His teaching reflects his research expertise, covering both theoretical foundations and modern applications of statistical methods. Administratively, he serves as Associate Chair, Graduate Studies in his department, demonstrating leadership in academic governance.

Xiaohong Chenمشاهده پروفایل

استاد

• Econometrics
• Semiparametric Estimation
• Nonparametric Models
• +۸ مورد دیگر

Xiaohong Chen is the Malcolm K. Brachman Professor of Economics and Professor of Management at Yale University. She previously held positions at the University of Chicago, London School of Economics, and New York University. She earned her PhD in Economics from the University of California, San Diego. Education : PhD in Economics (UC San Diego) Her research focuses on econometrics, particularly penalized sieve estimation, inference on semiparametric and nonparametric models, and applications to nonlinear time series, empirical asset pricing, copula modeling, missing data, measurement error, nonparametric instrumental variables, conditional moment restrictions, and causal inference. She has developed scalable algorithms like stochastic generalized method of moments (SGMM) for real-time data analysis. Her publications span top journals in economics (Econometrica, Review of Economic Studies), statistics (Annals of Statistics, Journal of the American Statistical Association), and engineering (IEEE Transactions). Key themes in her work include robust econometric methods, nonparametric inference, and machine learning integration for high-dimensional confounders. Scientific Awards : 2017 China Economics Prize Econometric Theory Multa Scripsit Award (2012) Journal of Nonparametric Statistics Best Paper Award (2010) Richard Stone Prize (2008-2009) Arnold Zellner Award (2006-2007) Elected Member, American Academy of Arts and Sciences (2019) Fellow of the Econometric Society (2007) She serves as an editor of the Journal of Econometrics (since 2019) and has been an associate editor for multiple journals. Her PhD thesis addressed stochastic approximation in function spaces for near-epoch dependent processes.

Olivier Scailletمشاهده پروفایل

استاد

• Finance
• Statistics
• Econometrics
• +۳ مورد دیگر

Olivier Scaillet is a Full Professor at the Geneva Finance Research Institute (GFRI) of the University of Geneva and holds a senior chair at the Swiss Finance Institute . His academic focus spans Finance , Statistics , and Econometrics , with expertise in Derivatives Pricing , Asset Pricing , Econometric Theory , and Quantitative Risk Management . Education : PhD in Applied Mathematics from University Paris IX Dauphine Leadership : Director of GFRI and Deputy Director (Education) His research includes nonparametric methods , instrumental variable regression , stochastic volatility models , and systemic risk measures . Recent work explores dynamic portfolio optimization , high-frequency jump analysis , and time-varying risk premia in equity and cryptocurrency markets. Professor Scaillet has been recognized with awards for best papers in Journal of Empirical Finance and Banque Privée Espirito Santo , and is a Fellow of the Society of Financial Econometrics . He serves as an associate editor for journals in Econometrics , Statistics , and Finance , and advises BNPParibas research teams in Paris and London.

Chung-Ming Kuanمشاهده پروفایل

استاد

• Econometric Theory
• Financial Econometrics
• Macroeconomic Forecasting
• +۳ مورد دیگر

Chung-Ming Kuan is an NTU Chair Professor and Distinguished Professor in the Department of Finance at National Taiwan University, with a joint appointment as Professor in the Department of Economics. He was elected as an Academician of Academia Sinica in 2002, one of Taiwan's highest academic honors. Education: M.A. in Economics from the University of California, Davis (1984) Ph.D. in Economics from the University of California, San Diego (1989) Professor Kuan's research spans several key areas in econometrics and economics. His primary research interests include Econometric Theory, Financial Econometrics, Macroeconomic Forecasting, and Program Evaluation. His work demonstrates a strong focus on developing robust statistical methods and applying them to financial and economic problems, with particular attention to Taiwan's economy. His teaching encompasses Econometric Theory, Financial Econometrics, Statistics, and Time Series Analysis. Analysis of Professor Kuan's recent publications reveals a consistent focus on advanced econometric methods, particularly in hypothesis testing, quantile regression, and time series analysis. His work often addresses practical challenges in financial econometrics and macroeconomic modeling, with many studies applying sophisticated techniques to Taiwan's economic data. A notable trend is his development of methods that avoid common pitfalls like data snooping bias and inconsistent estimation of asymptotic covariance matrices. Scientific Awards: Academician, Academia Sinica (elected 2002) Professor Kuan has made significant contributions to econometric theory and its applications. His research has been published in top journals across econometrics, finance, and economics. He has supervised numerous graduate students and has been actively involved in teaching advanced econometrics courses at National Taiwan University. His work has influenced both theoretical developments in econometrics and practical applications in financial markets and economic policy. As evidenced by his extensive publication record and academic appointments, Professor Kuan maintains active research programs in multiple areas of econometrics, with particular emphasis on robust statistical methods and their applications to financial and macroeconomic problems.