جستجوی استاد راهنمای دانشگاهی

Xiucai Ding is a tenured Associate Professor in the Department of Statistics at the University of California, Davis, starting in 2025. He is also affiliated with the Graduate Group in Applied Mathematics (GGAM) at UC Davis. Previously, he was an Assistant Professor in the same department from 2020 to 2025 and a Research Associate at Duke University from 2018 to 2020. PhD in Statistics, University of Toronto (2014–2018), advised by Jeremy Quastel Research Associate, Duke University (2018–2020), with Hau-Tieng Wu Assistant Professor, UC Davis (2020–2025) Associate Professor (tenured), UC Davis (starting 2025) His research focuses on mathematical statistics and statistical learning theory, particularly applied random matrix theory, high-dimensional statistics, non-stationary and functional time series analysis, statistical optimal transport, and the statistical foundations of machine learning algorithms. His methodological work emphasizes nonparametric and sieve-based estimation, inference under complex dependencies, and applications to noisy, high-dimensional data. The recent publications and software tools (such as RMT4DS, Sie2nts, SIMle) reflect a consistent trend in developing theoretically grounded, computationally feasible tools for analyzing complex time series and high-dimensional covariance structures. His work bridges theoretical statistics with practical data science. His research has been supported by the National Science Foundation (NSF). Estimation and inference for precision matrices of nonstationary time series (2020) Auto-regressive approximations to non-stationary time series (2021) On the partial autocorrelation function for locally stationary time series (2022) He advises students and researchers through his role in the Department of Statistics and GGAM. He has taught courses such as STA 108 (Regression Analysis), STA 137 (Applied Time Series Analysis), STA 135 (Multivariate Data Analysis), STA 221 (Big Data & High Performance Statistical Computing), and STA 250 (Topics in Applied and Computational Statistics) at UC Davis. He previously taught at Duke University and the University of Toronto. He has developed several open-source R packages for statistical methodology: RMT4DS : Random matrix tools for data scientists (CRAN/GitHub) Sie2nts : Sieve methods for non-stationary time series (CRAN/GitHub) SIMle : Estimation and inference for nonlinear and non-stationary regression (CRAN/GitHub) UHDtst : Two-sample tests for high-dimensional covariance matrices (GitHub)

• Mathematical Statistics
• Statistical Learning Theory
• Random Matrix Theory
• +۱۰ مورد دیگر

Dr. Tharindu P. De Alwis is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is actively engaged in teaching and research, with a focus on high-dimensional data analysis and machine learning applications. Ph.D. in Mathematics (Statistics), Southern Illinois University Carbondale M.S. in Mathematics, Southern Illinois University Carbondale B.Sc. in Statistics and Operations Research, University of Peradeniya, Sri Lanka His research centers on dimension reduction techniques, particularly Sufficient Dimension Reduction (SDR), envelope methods, and their applications in multivariate time series and spatial-temporal data. He integrates deep learning and neural networks into statistical modeling, with recent work on stacking-based deep neural networks and Fourier-based SDR methods. His work bridges statistical theory with practical machine learning applications in complex datasets. His recent publications (2021–2024) demonstrate a strong focus on developing innovative statistical and machine learning methods for time series and high-dimensional regression. Key themes include nonlinear modeling, dimension reduction, R package development, and AI-augmented reliability analysis. These works reflect interdisciplinary applications in engineering, data science, and systems safety. Dr. De Alwis has presented his research at national academic conferences in the USA and has publications in peer-reviewed journals such as Statistical Methods & Applications and Reliability Engineering & System Safety , as well as preprints on arXiv and software on CRAN. He teaches a variety of courses including Precalculus with Trigonometry, Linear Algebra, Applied Statistics, and Data Science. While no formal advisees or grants are mentioned, his active publication record suggests ongoing research mentorship and scholarly engagement. He previously served as a post-doctoral scholar at Worcester Polytechnic Institute before joining UWF.

• High-dimensional Multivariate Statistics
• Sufficient Dimension Reduction
• Dimension Reduction
• +۹ مورد دیگر

Matthias Bannert is a Lecturer at the Department of Management, Technology, and Economics at ETH Zürich, where he works at the KOF Swiss Economic Institute (Konjunkturforschungsstelle). His work focuses on the intersection of economics, software development, and data management, with particular expertise in time series analysis and official statistics. Bannert designs solutions for state-of-the-art data processing, management, and publishing of economic data and research. Bannert completed his doctoral thesis titled "Survey Based Research in Economics - Essays on Methodology, Economic Applications and Long Term Processing of Economic Survey Data" at ETH Zürich in 2016. His academic journey began when he joined KOF in late 2008, initially working as a researcher for the Business Tendency Survey group before transitioning to the institute's IT department. Dr. Bannert's research interests span several interconnected domains at the nexus of economics and data science. He specializes in developing software environments for official statistics, with particular focus on processing and managing economic time series data through open-source driven data pipelines. His technical expertise includes R programming and PostgreSQL database systems, which he applies to create robust solutions for economic data analysis. Bannert is particularly interested in survey methodology, nowcasting techniques, and the development of reproducible research workflows. His work bridges the gap between theoretical economics and practical software implementation, ensuring that economic research can leverage state-of-the-art data processing techniques. Analysis of Bannert's publication record reveals a consistent focus on the application of data science techniques to economic research problems, particularly in the domain of official statistics and survey-based economics. His work demonstrates a progression from theoretical survey methodology to practical software implementation, with increasing emphasis on real-time economic forecasting and data management systems. A distinctive feature of his research is the development of open-source R packages that make advanced economic data analysis more accessible to researchers and practitioners. As an active contributor to the R language for Statistical computing and the open source community, Bannert has developed several notable software packages including timeseriesdb, tstools, and kofdata, which are available on CRAN. These tools reflect his commitment to creating reproducible, transparent, and efficient workflows for economic data analysis. Bannert serves as a data science supervisor for multiple KOF research projects and is a co-Principal Investigator in an SNF-funded Digital Lives project in collaboration with KOF's labor market expert group. His teaching activities include "Hacking for Sciences - An Applied Guide to Programming with Data" and involvement in the Nowcasting Lab, which provides live out-of-sample forecasting and model testing capabilities for economic researchers. Dr. Bannert is affiliated with the KOF Swiss Economic Institute, where he contributes to several research groups including the KOF Macroeconomic Forecasting group and the KOF Data Science and Macroeconomic Methods group. His work at KOF bridges the institute's traditional economic research with modern data science approaches, helping to position the institute at the forefront of data-driven economic analysis.

• Software Environments for Official Statistics
• Processing and Management of Economic Time Series Data
• Open Source Driven Data Pipelines
• +۷ مورد دیگر

Audrey Fu is an Associate Professor of Family Medicine and Public Health Sciences and of Molecular Medicine and Genetics at Wayne State University School of Medicine, located at Scott Hall, 540 E. Canfield Street, Detroit, MI 48201. Her interdisciplinary work bridges computational methods with biomedical applications, particularly in genomics and medical imaging fields. Her educational background includes a PhD from the University of Washington (2008), followed by postdoctoral training at the University of Chicago (2008-2014) and a Visiting Postdoctoral Scholar position at Stanford University (2014-2015). This strong foundation in statistics and computational biology has enabled her to develop innovative approaches to complex biomedical data analysis. Dr. Fu specializes in developing statistical methods and algorithms for analyzing high-dimensional biomedical data, with particular expertise in causal network inference using Mendelian randomization, deep learning for single-cell RNA-sequencing data, and methods for identifying disease-relevant cell types through integration of genomic data. Her research group actively practices open science, distributing open-source software packages in R and Python through GitHub and CRAN. Her publication record reveals a clear progression from fundamental statistical methodology development toward increasingly translational biomedical applications. While her earlier work focused on DNA methylation patterns, statistical inference of gene expression noise, and Bayesian clustering methods, her recent publications demonstrate expansion into medical imaging analysis, particularly in space medicine applications and neurological conditions like Chiari malformation. This evolution shows her ability to adapt statistical frameworks to address diverse biomedical challenges. NIH Pathway to Independence Award (K99/R00; 2014-2019) International Society for Bayesian Analysis Travel Award (2010) Dorothy and Leon Gilford Fellowship, Department of Statistics, University of Washington (2003) Dr. Fu is currently accepting new M.S. students for 2025-2026 but not new Ph.D. students. Her NIH K99/R00 award indicates successful transition from postdoctoral research to independent investigator status. Her lab develops multiple open-source software packages including MethylHMM, MRPC, LATE, and rolypoly, reflecting her commitment to making computational tools accessible to the broader research community. Her collaborative work spans multiple institutions and disciplines, from basic molecular biology to clinical applications in ophthalmology and neurosurgery.

• Statistical methods for genomics
• Machine learning algorithms
• Causal network inference
• +۷ مورد دیگر

Lu Tang is an Associate Professor and Vice Chair for Education in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. Her research bridges biostatistics and machine learning to advance health data science through integrative analysis of distributed and high-dimensional datasets, with significant applications in precision medicine, opioid use disorder, sepsis treatment, and public health policy. Dr. Tang's educational journey includes foundational training at Sun Yat-sen University followed by advanced degrees in quantitative fields: PhD in Biostatistics, University of Michigan (2018) MS in Statistics, University of Virginia (2013) BA in Mathematics, University of Virginia (2012) Her research program develops statistical methodologies for data integration, causal inference, and decision-making in healthcare settings. She pioneers techniques for federated learning, robust individualized treatment rules, and high-dimensional data analysis, directly addressing challenges in health disparities, electronic health records, and clinical trial heterogeneity. Her work consistently translates methodological innovations into practical tools for biomedical researchers. Analysis of Dr. Tang's publication trajectory reveals a strategic evolution toward solving real-world healthcare data challenges through distributed learning frameworks. Her recent work emphasizes robust decision rules for heterogeneous populations (2022-2025), transfer learning for federated systems (2024), and methodological innovations for longitudinal and high-dimensional data (2019-2023), demonstrating consistent contributions across biostatistics, machine learning, and public health domains. Dr. Tang has been recognized with: IMS New Researchers Travel Award (2023) She actively mentors 6 current graduate students across PhD and MS programs while supervising 8 alumni now in industry and academia. Her research program is sustained by major funding including NIH R21DA055672 (PI, 2023-2025) on federated Medicaid data analysis, NSF DMS 2310217 (PI, 2023-2026) for longitudinal missing data methods, and multiple NIH R01 grants as Co-I addressing opioid treatment equity and precision medicine in sepsis. Dr. Tang leads the development of critical open-source software including RISE (Python) for robust individualized decisions, ifedtree (R) for tree-based federated learning, and metafuse (R/CRAN) for data integration, establishing infrastructure widely adopted in health data science research communities.

• Biostatistics
• Machine Learning
• Health Data Science
• +۷ مورد دیگر

Dominik Liebl is a Professor of Statistics at the University of Bonn's Department of Economics and a member of the Hausdorff Center for Mathematics (HCM), a Cluster of Excellence funded by the German Science Foundation (DFG). He also holds a visiting associate position at Colorado State University's Department of Statistics. His research spans Functional Data Analysis , Nonparametric Statistics , and Longitudinal Data Analysis , with applications in energy economics, finance, e-commerce, emotion psychology, and biomechanics. Recent methodological work focuses on simultaneous inference and statistical fairness . The articles in his profile demonstrate a strong emphasis on functional data methodologies applied to diverse domains like COVID-19 seroprevalence , electricity markets , and human movement science . Key subfields include confidence band design , biomechanical hypothesis testing , and high-dimensional econometric modeling . He actively contributes to open science through R-packages at CRAN/GitHub and serves as Associate Editor for the Journal of the Royal Statistical Society: Series C.

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

Benoît Le Maux is a Senior Lecturer at the University of Rennes 1, where he serves as a researcher at CREM (UMR CNRS 6211) and co-facilitator of the PPVD (Public Policy, Voting and Democracy) division. He co-directs the Master's degree in 'Engineering, Management, and Evaluation of Public Policies' and was previously co-director of the Master's in 'Public Economics and Public Finance' (2008-2016). He actively participates in academic governance as an elected member of the Scientific Council of the Faculty of Economics and the executive board of the European Public Choice Society (2012-2015). PhD in Economics, University of Rennes 1 (2006) HDR (Authorization to Direct Research), University of Rennes 1 (2013) His research focuses on public policy evaluation, microeconomic analysis of collective choices, behavioral economics, and the interplay between decentralization and democratic governance. He has published extensively on topics including legislative size optimization, ethical behavior in experimental settings, cost-benefit evaluation methods, and the political economy of local public finance. His peer-reviewed articles address questions such as the impact of monetary incentives on dishonesty, the efficacy of honesty nudges, and the determinants of public spending under fragmented political systems. Recent publications include Monetary incentives and the contagion of unethical behavior (2024) and On the optimal size of legislatures (2023), which analyze behavioral economics and institutional design. His work Local Public Economy (2020, with Sonia Paty) won the AFSE 2021 Prize in the 'Manual' category. AFSE 2021 Prize for 'Local Public Economy' Le Maux supervises PhD students including Pauline Mille (decentralization and citizen representation) and Kristýna Dostálová (quasi-experimental analysis of local public goods). He contributes to the scientific community through editorial roles in the Journal of Economic Policy and co-organizing conferences like the ANR Citizens project meetings in 2024. His research is supported by collaborations with institutions such as the Condorcet Center for Political Economy and the European Public Choice Society.

• Public Policy Evaluation
• Public Economics
• Political Economy
• +۷ مورد دیگر

Professor Matt Wand is a Distinguished Professor of Statistics at the University of Technology Sydney (UTS), affiliated with the School of Mathematical and Physical Sciences and the Statistics and Data Science Group. His academic career includes appointments at Harvard University, Rice University, Texas A&M University, the University of New South Wales, and the University of Wollongong. He is a Fellow of the Australian Academy of Science, American Statistical Association, Institute of Mathematical Statistics, and Australian Mathematical Society. Research Interests: Statistical Methodology for Multivariate Data Fast Approximate Inference, Variational Bayes, and Expectation Propagation Generalized Linear Mixed Models, Semiparametric Regression, and Streaming Data Analysis Statistical Computing, R Software Development, and CRAN Contributions Awards and Honors: Moran Medal (1997) and Hannan Medal (2013) from the Australian Academy of Science UTS Chancellor’s Medal for Exceptional Research (2013) Pitman Medal (2013) from the Statistical Society of Australia Funded Research: Principal investigator on multiple Australian Research Council grants, including projects on variational approximations, semiparametric regression, and statistical computing. Key contributions include the ACEMS Centre of Excellence and R packages like glmmEP and densEstBayes . Professional Service: Leadership roles in statistical societies, editorial work, and mentorship. Active in promoting open-source statistical tools and reproducible research.

• Statistical Methodology
• Semiparametric Regression
• Generalized Linear Mixed Models
• +۵ مورد دیگر

Jordi Cortes Martinez is a Professor in the Department of Statistics and Operations Research at Universitat Politècnica de Catalunya (UPC), affiliated with the Institute of Health Research and Innovation (INQUIS) and the GRBIO research group (Bio-statistics and Bioinformatics). His expertise focuses on Clinical Trials , Sports Science , R Programming , and Synthetic Data Analysis . Research Themes : Methodological development of composite endpoints in clinical trials, application of R Shiny for sports analytics, synthetic data quality metrics, and statistical modeling for healthcare outcomes. Recent Publications (2025): Design of trials with composite endpoints , R Shiny in sports , and External load analysis in tennis , reflecting ongoing work in clinical trial design, sports data science, and synthetic data validation. Collaborations : Active participation in BIOSTATNET networks and multidisciplinary projects with neurologists, pharmacologists, and sports scientists. His work emphasizes open science , with contributions to R packages and reproducible research frameworks. No current student advising or explicit retirement status is mentioned. Email: jordi.cortes-martinez@upc.edu

• Biostatistics
• Clinical Trials
• Sports Science
• +۲ مورد دیگر

Anestis Touloumis is a Principal Lecturer in the School of Architecture, Technology and Engineering at the University of Brighton, affiliated with the Computing and Mathematical Sciences Research Excellence Group. He holds a PhD in Statistics from the University of Florida and has held postdoctoral positions at the European Bioinformatics Institute and the University of Cambridge before joining Brighton in 2015. Education: PhD in Statistics, University of Florida (2006–2011) MSc in Applied Statistics, University of Piraeus (2003–2006) BSc in Mathematics, Aristotle University of Thessaloniki (1998–2003) His research centers on biostatistics, high-dimensional statistics, and statistical genetics, with a focus on developing methods for correlated responses, covariance matrix estimation, and hypothesis testing in high-dimensional settings. His work is applied in omics, health studies, and social sciences, and he actively disseminates methods through R packages on CRAN and Bioconductor. He teaches a range of statistical modules using R and SAS, emphasizing conceptual understanding and critical thinking. The recent research articles reflect strong trends in biostatistical methodology, particularly in high-dimensional inference, longitudinal and correlated data analysis, and statistical computing. There is a consistent emphasis on developing robust, open-source tools for real-world applications in healthcare and data science. Scientific Awards and Professional Service: Treasurer, British and Irish Region of the International Biometric Society (2020–present) Committee Member, British and Irish Region of the International Biometric Society (2019–present) External Examiner, MSc in Applied Statistics, Birkbeck University of London (2020–present) Member, International Biometric Society He has been active in research supervision and mentoring, with a stated interest in supervising postgraduate students in machine learning, categorical data, and high-dimensional statistics, often involving software development. He has contributed to research grants, notably as a Co-Investigator on the ERDF-funded Digital Research & Innovation Value Accelerator (DRIVA) project (2018–2021). He is involved in multiple research collaborations and has delivered invited talks and peer reviews for leading journals. His work bridges theoretical statistics and practical applications across disciplines.

• Biostatistics
• Categorical Data Analysis
• High-Dimensional Statistics
• +۱۳ مورد دیگر

Keefe Murphy is a Lecturer in Statistics within the Department of Mathematics and Statistics at Maynooth University , Faculty of Science & Engineering, and is affiliated with the Hamilton Institute . He is an active researcher in statistical machine learning, Bayesian nonparametrics, and clustering/classification of complex, high-dimensional data. Education: PhD in Statistics, University College Dublin MSc in Statistics, University College Dublin BSc in Economics & Mathematics, University of Limerick Research Interests: His work centres on developing and extending statistical methodologies for supervised and unsupervised learning , with emphasis on mixture models, latent variable models, Bayesian nonparametrics, and computational statistics . He actively contributes novel algorithms and software implementations, including the R packages IMIFA , MoEClust , and MEDseq available on CRAN. Current projects include extensions to Bayesian Additive Regression Trees (BART) , handling missing data , modelling multivariate count data , and variable selection in model-based clustering. Publication Profile: His recent publications (2021–2025) demonstrate a clear trajectory in advancing Bayesian machine learning methods, with contributions to Gaussian process BART models , sparse factor analysis , and educational data mining . Collaborative work spans learning analytics and multi-omic prostate cancer biomarker discovery , illustrating broad interdisciplinary impact. Awards & Recognition: Distinguished Dissertation Award (The Classification Society, 2020) Service & Advising: He serves as Associate Editor for Statistical Analysis and Data Mining and on departmental committees (Course Committee, PR Committee). He has successfully supervised PhD student Mateus Maia (graduated 2024) and currently teaches modules such as Advanced R Programming , Introduction to Data Science , and Nonparametric Statistics . Labs & Collaborations: He is affiliated with the Hamilton Institute , which fosters interdisciplinary research in applied mathematics and statistics, providing a collaborative environment for advancing computational and methodological statistics.

• Statistical Machine Learning
• Bayesian Nonparametrics
• Mixture Models
• +۱۳ مورد دیگر

Dirk Eddelbuettel is a Clinical Professor in the Department of Statistics at the University of Illinois Urbana-Champaign (UIUC), where he teaches courses such as STAT 447: Data Science Programming Methods. He holds a PhD from EHESS (France, 1997), an MA from EHESS (France, 1992), and an MSc from Karlsruhe Institute of Technology (Germany, 1992). His research focuses on statistical computing, R and C++ integration, and open-source software development. He is renowned for creating the Rcpp package, which bridges R and C++, and co-creating the Rocker Project (Docker containers for R). He also leads the r2u initiative, providing CRAN packages as Ubuntu binaries. His work emphasizes reproducible research, high-performance computing, and statistical software engineering. Notable contributions include the RcppArmadillo, RProtoBuf, and RInside packages. He actively contributes to the R community through publications, workshops, and open-source projects. His teaching and research highlight interdisciplinary approaches, combining statistical theory with computational methods. He maintains a personal website (dirk.eddelbuettel.com) and is active on social platforms discussing R, C++, and open-source technologies.

• Statistical Computing
• R Programming
• Parallel Computing
• +۴ مورد دیگر

Kazmir Lyubomyr Pavlovych is a Researcher at the Department of Problems of the Real Sector of the Regional Economy, State Institution 'Institute of Regional Studies named after M. I. Dolishnyi, NAS of Ukraine'. He holds a Candidate of Economic Sciences degree, a qualification equivalent to a Ph.D., awarded in 2015 for his dissertation on the organizational and economic principles of ecologically balanced agricultural land use. His career has been dedicated to regional economic research, with a primary focus on the socio-economic development of rural areas, land management, and metropolitanization processes. His research interests are extensive and interdisciplinary, centering on the socio-economic aspects of spatial development in rural areas, the analysis of state spatial policy during Ukraine's market transition, the theoretical and methodological foundations of land management, and the organizational and economic support for environmentally balanced agricultural land use. A significant part of his recent work investigates metropolitanization processes and the formation of metropolitan subregions in Ukraine. The analysis of his 15 most recent articles reveals a strong, consistent focus on regional and spatial economics. His research trends include the modernization of rural development management systems, the strategic planning of agricultural land use under institutional change, the conceptualization of innovative development for rural areas, and the study of metropolization. His work frequently employs socio-economic approaches, institutional analysis, and methodological modeling, with a clear emphasis on policy implications for sustainable regional development in Ukraine. No scientific awards or prizes are mentioned in the provided text. Kazmir has authored or co-authored over 150 scientific and methodological works. His advising activities and grants are not detailed in the provided information. He has collaborated with other prominent researchers, such as his scientific supervisor, Professor Mykhailo Grigorovich Stupen, and other colleagues at the Institute of Regional Studies and Lviv State Agrarian University. His work is published in key Ukrainian academic journals like 'Regional Economy' and 'Socio-economic problems of the modern period of Ukraine'. The Institute of Regional Studies, where he is based, functions as a central hub for research on Ukraine's regional development, land policy, and spatial planning.

• Regional Economics
• Spatial Development
• Land Management and Cadastre
• +۷ مورد دیگر

Jeffrey S. Racine is a Professor in the Department of Economics and a Professor in the Graduate Program in Statistics in the Department of Mathematics and Statistics at McMaster University. He occupies the Senator William McMaster Chair in Econometrics and is a Fellow of the Journal of Econometrics. He serves as an Associate Editor for Econometric Reviews and as the Deputy Editor-in-Chief for Econometrics. His previous academic appointments include Syracuse University, the University of South Florida, the University of California San Diego (two-year visiting appointment), and York University. Dr. Racine earned his Ph.D. from the University of Western Ontario in 1989 under the supervision of Aman Ullah. His educational background also includes a Master's degree from McMaster University and a Bachelor of Arts (Summa Cum Laude) from McMaster University. Professor Racine's research focuses on nonparametric estimation and inference, shape constrained estimation, cross-validatory model selection, frequentist model averaging, nonparametric instrumental methods, and entropy-based measures of dependence. His work bridges theoretical econometrics with practical computational implementations, with a strong emphasis on reproducible research. He has pioneered approaches for nonparametric estimation with mixed data types (both categorical and continuous predictors) and has made significant contributions to parallel distributed computing paradigms applied to computationally intensive nonparametric estimators. His recent publications demonstrate continued innovation in model averaging techniques, kernel density estimation, and quantile regression methods. Dr. Racine has received numerous professional recognitions including the Senator William McMaster Chair in Econometrics, being named a Fellow of the Journal of Econometrics, and receiving the Econometrics Best Paper Award in 2018. His scholarly output includes multiple books, monographs, and over 100 peer-reviewed publications in leading economics and statistics journals. As an educator and researcher, Professor Racine has made substantial contributions through his co-authored graduate textbook Nonparametric Econometrics: Theory and Practice (with Qi Li, Princeton University Press, 2007) and his monograph Nonparametric Econometrics: A Primer (Foundations and Trends in Econometrics, 2008). He has also authored influential books including An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics (Cambridge University Press, 2019) and Reproducible Econometrics Using R (Oxford University Press, 2019). His work on software implementation is equally impactful, having co-authored the widely used R packages np and crs available on CRAN, which have become standard tools for nonparametric econometric analysis. Professor Racine maintains an active research program with collaborators worldwide and continues to advance the field of nonparametric econometrics through both theoretical developments and practical implementations. His work has applications across economics, statistics, and various social sciences where flexible modeling approaches are required.

• Econometrics
• Statistics
• Nonparametric Estimation
• +۷ مورد دیگر

Kyle A. Caudle is a Professor of Mathematics at the South Dakota School of Mines and Technology, holding a Ph.D. from George Mason University, an M.S. from Salve Regina University, and a B.A. from Western State College. His research spans forecasting, tensor analysis, and anomaly detection, with applications in engineering and data science. Caudle develops computational tools for time series forecasting, tensor decomposition, and graph representation learning. He created software packages like Flow Field, rTensor2, and LTAR, published on CRAN. His interdisciplinary projects include collaborations with NIST and Naval Surface Warfare Centers on surface ship maintenance and anomaly detection. Publications emphasize multilinear algebra, temporal forecasting, and machine learning. Recent work advances tensor factorization for high-dimensional data, hierarchical graph networks, and deep generative models. Awards include the 2015 Peter Holmes Prize for innovative statistics teaching and accreditation as a Professional Statistician (ASA, 2013). He mentors graduate students and co-developed the Ph.D. in Data Science.

• Forecasting
• Time Series Analysis
• Multilinear Algebra
• +۳ مورد دیگر