جست‌وجوی استاد و پژوهشگر

• Statistical Machine Learning
• High-Dimensional Statistics
• Likelihood-Free Inference
• +۳ مورد دیگر

Summary Ann B. Lee is a Professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University (CMU). She serves as Co-Director of the Ph.D. Program in Statistics. Previously, she was the J.W. Gibbs Assistant Professor at Yale University and a visiting researcher at Brown University. Her research focuses on developing statistical methods for complex data in physical sciences, including trust-worthy uncertainty quantification, likelihood-free inference, and applications in astronomy, climate science, and hurricane dynamics. Education: Ph.D. in Physics from Brown University (2002); M.Sc./B.Sc. in Engineering Physics from Chalmers University of Technology (Sweden). Key Research Interests: - Scientific Machine Learning - Uncertainty Quantification (UQ) - Likelihood-Free Inference - Tropical Cyclone Analysis - High-Dimensional Data Modeling She leads the STAMPS (STAtistical Methods for Physical Sciences) research group, which bridges classical statistics and machine learning. Recent work includes methods for estimating ocean thermal responses to hurricanes, probabilistic forecasting, and diagnostics for generative models. Her team collaborates with climate and astrophysics communities, hosting public webinars and symposia. Advising: Supervised over 15 PhD students, including graduates now in academia and industry. Current advisees include Luca Masserano and Alex Shen. Labs/Teams: Co-directs the STAMPS Research Center at CMU, launching in Fall 2024 as a university-wide initiative.

• Econometrics
• Applied Econometrics
• Empirical Likelihood Methods
• +۴ مورد دیگر

Dr. Xing Wang is an Assistant Professor in the Department of Economics at Durham University Business School since 2010. He holds a PhD from the University of Southampton, where he also served as a teaching assistant and lecturer. His research focuses on econometric methodologies, particularly empirical likelihood techniques and applications of large deviations theory in statistical inference. Key areas include environmental policy analysis, corporate finance dynamics, and carbon market impacts. Education: PhD in Economics from University of Southampton Previous Affiliation: Teaching roles at University of Southampton Research interests span econometric theory, applied empirical analysis of corporate governance (e.g., CSR and mergers), and environmental economics with case studies in carbon market mechanisms. He has published extensively in journals like International Review of Financial Analysis and Journal of Mathematics and Statistics . Publications reflect a dual emphasis on methodological innovation (e.g., empirical likelihood efficiency) and applied topics like carbon productivity and merger outcomes. His work integrates statistical rigor with real-world policy implications, particularly in China’s industrial contexts. Advising: Supervises PhD students in Economics focusing on econometric applications Affiliations: Department of Economics, Durham University

Jerry Lawlessمشاهده پروفایل

استاد

• Survival Analysis
• Event History Data
• Biostatistics
• +۴ مورد دیگر

Dr. Jerry Lawless is Distinguished Professor Emeritus in Statistics and Actuarial Science at the University of Waterloo. He received his Ph.D. from Waterloo in 1969 and served on faculty from 1972-2007, including terms as department chair. His research spans survival analysis, event history data, warranty claims modeling, and incomplete data methods, with applications in medicine, reliability engineering, and public health. Research foci include: Multistate models for complex disease processes Estimands in clinical trials Analysis of interval-censored and truncated data Warranty claims modeling COVID-19 host genetics and testing strategies Recent methodological work addresses estimand specification for regulatory trials, regularized regression for failure time studies, and efficiency improvements in two-phase sampling designs. Applied research includes COVID-19 host genetics collaborations and epidemic testing strategies in Ontario. Author of influential textbooks including 'Statistical Models and Methods for Lifetime Data' and 'Multistate Models for the Analysis of Life History Data'. Honors include the Statistical Society of Canada Gold Medal (1999), Shewhart Medal (2012), and election to the Royal Society of Canada (2000). He has supervised over 30 graduate students and consulted widely with industry and government.

• Bayesian Statistics
• Computational Statistics
• Probabilistic Machine Learning
• +۵ مورد دیگر

Professor Sumeetpal Singh holds the Tibra Foundation Chair in Mathematical Sciences at the University of Wollongong's Faculty of Engineering and Information Sciences, School of Mathematics and Applied Statistics (since 2023). Previously, he served as Professor of Engineering Statistics at the University of Cambridge (2007–2023) and as a Fellow and Director of Studies at Churchill College, Cambridge. His research focuses on Bayesian Statistics, Computational Statistics, Probabilistic Machine Learning, and Time-series analysis, with notable contributions to particle filtering, Monte Carlo methods, and statistical inference. He has supervised research on topics like 'American options and their application in political science'. His work has been recognized with awards such as the M. Barry Carlton award (2014). Singh is actively involved in editorial roles, including serving as Associate Editor for the Annals of Applied Probability and Senior Associate Editor for ACM Transactions on Probabilistic Machine Learning . Current research includes grants like the Air Force Office of Scientific Research-funded project on Bayesian Spatio-Temporal Analysis (2023–2025). Teaching includes modules like Statistical Modelling and Analysis of Time-series Data and Statistical Inference and Introduction to Model Building . His publications span computational statistics, signal processing, and machine learning, emphasizing methodological innovation and theoretical rigor.

Megan Romerمشاهده پروفایل

عضو هیئت علمی

• Statistics Education
• Incomplete Data Analysis
• Multivariate Analysis
• +۳ مورد دیگر

Megan Romer is an Associate Teaching Professor (Online Programs) in the Department of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. She earned a Ph.D. (2009), M.S. (2002), and B.S. (2000) in Statistics and Mathematics from Penn State and SUNY Oswego, respectively. Her primary focus is on teaching statistics, particularly in online education since 2009. Her research emphasizes incomplete data analysis, with contributions to multivariate statistical methods and applications in clinical trials. Her teaching spans courses including Applied Time Series Analysis (STAT 510), Elementary Statistics (STAT 200), and Statistical Consulting (STAT 580). Her publications address statistical methodologies for handling incomplete data, neurodegenerative disease biomarkers, and Parkinson's-related clinical trials. She has contributed to studies on Huntington's disease progression and CAG repeat length correlations. Romer's work bridges theoretical statistical analysis with practical applications in healthcare and education. Notable contributions include finite-sample inference techniques for multivariate normal data and kurtosis tests under monotone missingness. Her teaching philosophy emphasizes student engagement with statistical problem-solving, leveraging her prior experience in clinical trial research support. She has designed curricula for online programs, ensuring accessibility and rigor in statistical education.

Ronald Gallantمشاهده پروفایل

استاد

• Econometrics
• Financial Econometrics
• Bayesian Inference
• +۱ مورد دیگر

Ronald Gallant is the Liberal Arts Professor of Economics at Pennsylvania State University. He earned his PhD from Iowa State University in 1971. His research specializes in econometric theory, Bayesian methods, and financial econometrics, with applications to asset pricing, industrial organization, and computational statistics. Gallant develops novel estimation techniques for complex economic models, including Bayesian nonparametrics, Markov chain Monte Carlo methods, and dynamic game theory. His recent work examines asset pricing under ambiguity aversion and high-frequency financial data analysis. He has extensive editorial experience in leading econometrics journals.

Stuart Gemanمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Statistical Theory
• +۳ مورد دیگر

Stuart Geman is the James Manning Professor of Applied Mathematics at Brown University's Division of Applied Mathematics. He holds a Ph.D. from MIT (1977), focusing on stochastic differential equations. His research spans machine and natural vision, statistical theory, neuroscience, financial modeling, and computational linguistics. He has advised numerous students, including Asohan Amarasingham, Lo-Bin Chang, and Ya Jin. His work explores hierarchical models for visual recognition, neural spike train analysis, market dynamics, and generative image modeling. Key contributions include the 'compositionality' framework for efficient learning in biological systems, probabilistic image models with hierarchical structure, and statistical methods for neurophysiological data. He co-developed the Gibbs sampling method for image restoration and pioneered nonparametric statistical estimation via sieves. His recent work addresses scale invariance in natural images and transsaccadic neural coding in macaque V1. Publications span journals like Journal of Neuroscience , Neural Computation , and Proceedings of the National Academy of Sciences . His research integrates computational, statistical, and biological perspectives to address challenges in vision, neuroscience, and financial systems.

• Robust Statistics
• Data Mining
• Outlier Identification
• +۴ مورد دیگر

Dr. Xin Dang is a Professor of Mathematics at the University of Mississippi, affiliated with the Department of Mathematics within the College of Liberal Arts. His research focuses on robust statistics, data mining, outlier identification, and high-dimensional statistical analysis, with a particular emphasis on developing novel methods like Gini correlations and robust statistical computing techniques. He has contributed extensively to areas such as correlation analysis, classification, and clustering algorithms. Dr. Dang holds a B.S. in Applied Mathematics from Chongqing University (1991), an M.S. in Natural Sciences from the University of Texas at Dallas (2003), and a Ph.D. in Statistics from the University of Texas at Dallas (2005). He teaches a wide range of courses including Elementary Statistics, Calculus, and Advanced Statistics, reflecting his expertise in both theoretical and applied statistical methodologies. His recent work emphasizes methodological advancements in Gini-based correlations, high-dimensional feature screening, and computational statistics. Notable contributions include the development of the Maximum Projection Gini Correlation (MaGiC) and the Gini covariance matrix. His research bridges theoretical statistics with practical applications in economics, data science, and computational algorithms. Dr. Dang’s publications span over two decades, with a focus on robust statistical techniques, dependence modeling, and algorithmic efficiency. He has collaborated on projects involving economic growth prediction, nonparametric testing, and optimization methods. His work often addresses challenges in handling complex data structures and improving predictive accuracy through novel statistical frameworks.

• Categorical Data Analysis
• Group Testing
• Bayesian Estimation
• +۳ مورد دیگر

Christopher McMahan is a Full Professor at Clemson University's School of Mathematical and Statistical Sciences within the College of Science. His academic roles include serving as Associate Director for Graduate Studies and co-leading the Clemson-MUSC AI Hub. McMahan holds a Ph.D. in Statistics from the University of South Carolina (2012), M.S. in Mathematics from Western Kentucky University (2008), and B.S. in Mathematics from Austin Peay State University (2006). His research focuses on developing statistical methodologies for complex data problems, with interdisciplinary applications spanning epidemiology, agriculture, and biomedical sciences. Core research areas include Bayesian modeling, group testing designs, high-dimensional regression, and spatio-temporal analysis. Current projects involve genomic prediction in crop science, infectious disease modeling, and machine learning applications for public health surveillance. McMahan's publication record demonstrates consistent contributions to statistical theory and applied interdisciplinary research. Recent work exhibits strong emphasis on: Bayesian computational methods for large-scale problems Epidemiological modeling of COVID-19 transmission Genomic prediction of agricultural phenotypes Statistical learning approaches for public health data Biomedical applications of group testing designs Environmental risk assessment methodologies His scholarly recognition includes: Fellow of the American Statistical Association (2022) Multiple ASA Outstanding Statistical Application Awards ENAR Distinguished Student Paper Awards College of Science Dean's Associate Professor (2021-2023) McMahan maintains an active research program with substantial grant funding, including NIH R01 awards totaling over $2 million. He currently advises four postdoctoral scholars and has mentored eight doctoral graduates. His collaborative work involves interdisciplinary teams across computational biology, plant genomics, and public health epidemiology. Laboratory and team leadership includes directing graduate research through the Clemson-MUSC AI Hub and coordinating multi-institutional consortia like the NCAA-DoD CARE Consortium. Future research directions emphasize developing computationally efficient statistical methods for high-dimensional biomedical data and expanding applications in precision agriculture.

• Mathematical Statistics
• Asymptotic Statistics
• Nonparametric Statistics
• +۸ مورد دیگر

Prof. Dr. Arnold Janssen is a retired professor (emeritus) at the Department of Mathematics, Heinrich-Heine-Universität Düsseldorf, affiliated with the Faculty of Mathematics and Natural Sciences. He specializes in mathematical statistics, asymptotic theory, nonparametric methods, and survival analysis. His research spans statistical methodology, including bootstrap techniques, false discovery rate control, and applications in biostatistics and financial mathematics. Janssen has contributed extensively to statistical theory through numerous peer-reviewed publications and has collaborated with researchers across disciplines. Research interests include: Mathematical statistics and asymptotic methods Nonparametric and semiparametric models Survival analysis and clinical trial design Extreme value theory and high-dimensional data analysis Bootstrap methods and permutation tests Applications in biostatistics and financial risk modeling His recent work emphasizes methodological advancements in multiple testing procedures, with applications to genomics and medical research. Janssen has also explored statistical foundations in functional data analysis and stochastic processes. Key contributions include theoretical developments in false discovery rate (FDR) control, nonparametric hypothesis testing frameworks, and the application of Le Cam's theory to modern statistical challenges. His collaborations span interdisciplinary projects with biologists, clinicians, and financial researchers.

Mohammad Sepehrifarمشاهده پروفایل

دانشیار

• Reliability Analysis
• Nonparametric Statistics
• Life Testing
• +۲ مورد دیگر

Dr. Mohammad Sepehrifar is an Associate Professor in Statistics at Mississippi State University's Department of Math and Statistics. His research develops statistical methods for reliability engineering and life testing. Research focuses on nonparametric modelings for reliability analysis, with applications in engineering systems and clinical trials. Current work includes collaborations on medicinal chemistry and environmental risk assessment projects. Recent publications showcase methodological innovations in statistical testing, software tools, and efficiency analysis across engineering domains.

• Evolutionary Biology
• Mathematical Modeling
• Experimental Evolution
• +۳ مورد دیگر

Dr. Sarah P. Otto is a Professor at the Department of Zoology , University of British Columbia , holding the Killam University Chair and Canada Research Chair . Her research integrates mathematical theory, statistical inference, and experimental evolution to explore fundamental questions in evolutionary biology. Affiliation : Department of Zoology, Faculty of Science, University of British Columbia Contact : otto@zoology.ubc.ca Research interests focus on evolutionary transitions in reproduction modes, genome structure, and mating systems. She uses mathematical models to assess evolutionary plausibility and yeast experimental evolution to validate hypotheses, particularly on ploidy level dynamics and adaptation constraints in changing environments. Scientific contributions include developing stochastic models for sexual reproduction advantages and likelihood-based tools for inferring trait-dependent speciation/extinction rates. Her 2017 co-authored paper on the evolutionary hydra effect examines adaptation rates in yeast under environmental stress. Awards : Killam University Professor Canada Research Chair Teaching : Co-author of A Biologist's Guide to Mathematical Modeling in Ecology and Evolution Courses: Biology 301 (Biomathematics), Biology 336 (Fundamentals of Evolution) Advising spans 30+ graduate students and postdocs, with projects ranging from microbial coevolution to macroevolutionary patterns. Her lab (Biodiversity Research Center) emphasizes interdisciplinary approaches and experimental validation of theoretical models.

• Bayesian Modeling
• Statistics in Artificial Intelligence
• Statistical Omics
• +۳ مورد دیگر

Dr. Sherry Wang is a Jenkins-Garrett Professor of Mathematics at The University of Texas at Arlington. She holds affiliate professorships at Southern Methodist University. Her research focuses on Bayesian Modeling, Statistical Omics, and Meta-Analysis. She earned her PhD from The University of Texas at Austin (2002) and has held roles at SMU from 2003 to 2022. Her work includes federal grants on deep learning for neoantigen analysis and T-cell receptor binding. Key awards include being an Elected Fellow of the American Statistical Association (2024). She advises numerous PhD students and chairs dissertation committees in Data Science and Biostatistics. Dr. Wang teaches courses like Bayesian Data Analysis and collaborates on projects like infrastructure equity and normalization of genomic data. Her service roles include College Director for Research in Data Science.

Daniel Gerviniمشاهده پروفایل

استاد

• Functional Data Analysis
• Robust Statistics
• Nonparametric Statistics
• +۳ مورد دیگر

Daniel Gervini is Professor and Data Science Undergraduate Coordinator in the Department of Mathematical Sciences at University of Wisconsin-Milwaukee. His research centers on functional data analysis, robust statistics, and point process modeling, with applications to spatial statistics and multivariate analysis. Educational credentials include a Ph.D. in Mathematics from University of Buenos Aires (1999) and M.S. from the same institution (1995). He held postdoctoral positions at University of Zürich Biostatistics Department (2001-2004) and research fellowship at University of Buenos Aires (1996-1999). Research develops novel methods for functional data including warped regression, outlier-resistant estimation, and point process modeling. Recent NSF-funded projects focus on spatio-temporal covariation in replicated point processes and statistical methods for point-process time series. Applications include bike-sharing demand analysis and neuroimaging. Publication trends showcase progression from foundational robust functional methods to advanced spatio-temporal point process modeling. Recent work emphasizes computational statistics applications to urban systems and environmental data. Develops extensive open-source statistical software packages in MATLAB for functional data analysis, point process modeling, and spatio-temporal prediction. Maintains active research blog on statistical analysis of COVID-19 pandemic data.

• Time-series econometrics
• Bootstrap methods
• Nonlinear models
• +۱ مورد دیگر

Overview Prof. Zacharias Psaradakis is a Professor of Econometrics at Birkbeck Business School, part of the University of London. He holds administrative roles as Admissions Tutor for MSc Finance and MSc Banking and Finance programs, as well as for PhD Economics and Finance. He also serves as Ethics Lead and Associate Editor for Econometrics and Statistics . His professional memberships include the International Association of Applied Econometrics and the Higher Education Academy. Education Dr. Psaradakis earned his Ph.D. (Econometrics) from the University of Southampton. Research Interests His research focuses on time-series econometrics , bootstrap methods , and nonlinear models . He explores topics such as regime-switching models, dynamic factor models, and the application of machine learning in econometric analysis. His work has been recognized through prestigious awards, including the Distinguished Author designation from the Journal of Time Series Analysis (2020) and the Fellow distinction from Econometric Reviews (2019). Publications Prof. Psaradakis has published extensively on econometric methodologies, including articles on bootstrap inference, regime-switching models, and time-varying coefficient models. His recent work emphasizes applications in financial markets and macroeconomic dynamics, reflecting his expertise in bridging theoretical econometrics with practical economic analysis. Awards & Recognition Distinguished Author, Journal of Time Series Analysis (2020) Fellow, Econometric Reviews (2019) Distinguished Author, Journal of Applied Econometrics (2005) Teaching & Supervision He teaches modules such as Econometrics of Financial Markets and Applied Statistics and Econometrics . Since 2013, he has supervised 7 doctoral students, focusing on topics like bootstrap inference, nonlinear regime-switching models, and dynamic factor models. Current students work on machine learning applications in econometrics. Affiliations He is affiliated with the Birkbeck Centre for Applied Macroeconomics and the Economics Subject Group , contributing to collaborative research initiatives in applied econometrics and macroeconomic modeling.