جست‌وجوی استاد و پژوهشگر

Tasos Christofidesمشاهده پروفایل

استاد

• Probability Inequalities
• Demimartingales
• Stochastic Orders
• +۲ مورد دیگر

Tasos Christofides is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, School of Natural and Applied Sciences. He has been serving in this position since 2004 after previously holding the rank of Associate Professor from 1991 to 2004. Prior to his appointment at the University of Cyprus, he was an Assistant Professor at the State University of New York at Binghamton from 1987 to 1991. His educational background includes a Ph.D. (1987) and MSE (1985) in Mathematical Sciences from The Johns Hopkins University, USA, and a Mathematics degree (1983) from the University of Athens, Greece. Professor Christofides specializes in Probability Inequalities, Demimartingales, Stochastic Orders, Survey Methodology, and Indirect Questioning Techniques. His research primarily focuses on theoretical statistics with applications to survey methodology, particularly in developing techniques for handling sensitive questions while preserving respondent privacy. His work bridges probability theory with practical survey design, making significant contributions to both theoretical foundations and applied methodologies. His publication record demonstrates consistent contributions to statistical theory, with a particular emphasis on U-statistics, demimartingales, and randomized response techniques. The trajectory of his research shows progression from foundational work on U-statistics and probability inequalities in the early career to more applied survey methodology techniques in later years, while maintaining strong theoretical underpinnings. Professor Christofides serves as Associate Editor for several prestigious statistical journals including Communications in Statistics-Theory and Methods, Communications in Statistics-Computation and Simulation, Journal of Statistical Theory and Practice, and Statistics and Probability Letters. His editorial service reflects his standing in the statistical community and his expertise across multiple domains of statistical theory and methodology. While specific grant information is not provided in the available text, his sustained research output and editorial positions indicate active engagement with the research community.

Shaobo Liمشاهده پروفایل

دانشیار

• High-dimensional robust statistics
• Nonparametric regression
• Ordinal data analysis
• +۴ مورد دیگر

Shaobo Li is an Associate Professor in the Department of Analytics, Information, and Operations at the University of Kansas School of Business. His research spans high-dimensional robust statistics, ordinal data analysis, and data privacy in business contexts. Ph.D. in Business Administration, University of Cincinnati M.S. in Statistics, University of Cincinnati B.A. in Mathematics, Shandong University Li’s work focuses on nonparametric regression techniques, corporate bankruptcy prediction models, and methodological innovations in ordinal variable analysis. He has contributed to data privacy frameworks for marketing and financial applications, emphasizing flexible statistical approaches. Recent publications highlight his expertise in partial association quantification, point-of-sale data anonymization, and second-party data utilization. Collaborative efforts include applications in marketing science and business analytics.

Ben Sherwoodمشاهده پروفایل

دانشیار

• Quantile regression
• Semiparametric regression
• High-dimensional data analysis
• +۴ مورد دیگر

Ben Sherwood is an Associate Professor and Jack and Shirley Howard Mid-Career Professor in the Analytics, Information, Operations academic area at the University of Kansas School of Business. His research focuses on developing advanced statistical methodologies with applications across various domains including healthcare, finance, and business analytics. Ph.D. in Statistics, University of Minnesota, 2014 B.A. in Mathematics and Computer Science, Macalester College, 2003 Post Doctoral Fellow at Johns Hopkins Biostatistics Department, 2014-2016 Sherwood's research primarily centers on quantile regression methodologies, with special emphasis on penalized approaches for high-dimensional data. His work extends to semiparametric regression, multivariate regression models, and addressing challenges with missing data. He develops statistical methods with practical applications in business problems, healthcare analytics, and genomic studies. His approach often involves creating new statistical techniques, implementing them in software, and providing theoretical foundations through mathematical proofs. His publication record shows a consistent trajectory of impactful research, with recent work focusing on quantile regression for equity premium prediction, Bayesian network applications for PTSD screening, and advanced techniques for model selection in high-dimensional settings. His research bridges theoretical statistics with practical applications, particularly in business analytics contexts. Sherwood actively mentors PhD students, encouraging them to develop novel statistical methods while maintaining flexibility in their research direction. He frequently co-advises students with faculty from related disciplines including Professors Prakash Shenoy, Karthik Srinivasan, and Shaobo Li. His students have worked on diverse projects including beta regression for model selection uncertainty, Bayesian networks for PTSD prediction in veterans, bankruptcy prediction for firms, and analyzing crowdfunding platform dynamics. Complementing his theoretical work, Sherwood has developed multiple software packages to implement his methodologies, including rqPen for penalized quantile regression, hrqglas for group variable selection, and mcen for multivariate cluster elastic net models. These tools make advanced statistical methods accessible to practitioners across various fields.

Yingfu Liمشاهده پروفایل

دانشیار

• Experimental Designs
• Biostatistics
• Statistical Computing
• +۳ مورد دیگر

Dr. Yingfu (Frank) Li is an Associate Professor of Statistics in the College of Science and Engineering at the University of Houston-Clear Lake (UHCL), where he has taught undergraduate and graduate statistics for over 20 years. His research focuses on experimental designs, biostatistics, and statistical computing, with notable contributions to survival analysis, censored data methodologies, and imputation techniques. He has authored 19 refereed publications, with 13 published post-joining UHCL. His research interests include advanced statistical methodologies such as nonparametric estimation, robust parameter designs, and applications in biopharmaceutical research. Dr. Li's work bridges theoretical statistics with practical applications, particularly in healthcare and engineering contexts. His articles demonstrate expertise in areas like Kaplan-Meier estimator refinements, Hadamard matrix-based experimental designs, and variance estimation under missing data scenarios. Though no specific awards are listed, his prolific publication record reflects sustained academic contribution. Courses taught include Introduction to Statistics, Applied Statistical Methods, and Statistical Computing.

Kalimuthu Krishnamoorthyمشاهده پروفایل

استاد

• Statistical Methodology
• Occupational Exposure Analysis
• Missing Data
• +۶ مورد دیگر

Professor Kalimuthu Krishnamoorthy holds the Philip and Jean Piccione Endowed Chair in Statistics at the University of Louisiana at Lafayette. His research focuses on statistical methodologies for occupational exposure analysis, missing data, and tolerance regions, with contributions to censored data analysis and calibration techniques. He has advised over 30 Ph.D. students and led NIOSH-funded projects on exposure assessment. His work includes developing statistical software tools like StatCalc and over 200 peer-reviewed articles. Education: Ph.D. (Statistics, 1985) Indian Institute of Technology-Kanpur; M.Sc. & B.Sc. (Statistics) Madras University. Grants: Multiple NIOSH grants (R01-OH series) totaling $2.6M, focusing on exposure data analysis methodologies. Labs/Teams: Leads statistical research groups at the University of Louisiana, collaborating on occupational health and environmental statistics.

• Bayesian Methods
• Biostatistics
• Data Science
• +۴ مورد دیگر

Saman Muthukumarana is a Professor and Head of the Department of Statistics at the University of Manitoba. He joined the department in 2010 as an Assistant Professor, was promoted to Associate Professor in 2016, and became a full Professor in 2022. He holds a BSc (Honours Special) in Statistics from the University of Sri Jayewardenepura, an MSc from Simon Fraser University, and a PhD from Simon Fraser University under Dr. Tim Swartz, focusing on Bayesian methods and applications. His research emphasizes Bayesian methodologies for complex models, with applications in social networks, health studies, sports analytics, environmental science, and machine learning. He has secured over $8.4M in research funding from NSERC, Mitacs, CIHR, and other organizations. His work has been published in journals such as the Canadian Journal of Statistics, Machine Learning with Applications, and IEEE Open Journal of Instrumentation & Measurement. Dr. Muthukumarana’s research spans Bayesian computation, biostatistics, data science, and environmental statistics. He has contributed to anomaly detection in buildings, predictive modeling for public health (e.g., Long COVID), and ecological studies like salmon stock recruitment. His collaborative projects include developing statistical tools for microbiome analysis and improving machine learning approaches for imbalanced datasets. He also leads the Data Science Nexus, fostering interdisciplinary research. His grants and collaborations highlight his role in advancing statistical methodologies for real-world challenges, including health, energy efficiency, and ecological conservation. While no specific awards are listed, his extensive funding and publication record reflect his scholarly impact. He currently supervises graduate students and actively participates in academic leadership roles.

• Econometrics
• Applied Econometrics
• Statistical Modeling
• +۳ مورد دیگر

Shakeeb Khan is a Professor of Economics at Boston College. His primary affiliation is with the Department of Economics. He holds a Ph.D. from Princeton University. His research focuses on econometrics, particularly in areas such as panel data models, endogeneity, censored regression, and binary response models. He has contributed extensively to methodological advancements in econometric theory and applications. Education: Ph.D. in Economics from Princeton University. Research interests include: Nonparametric and semiparametric estimation techniques Endogeneity and identification in econometric models Applications of econometric methods to labor economics, policy evaluation, and development economics His work often addresses challenges in censored data, dynamic models, and factor structures. Recent research trends emphasize high-dimensional models and robust inference in nonlinear frameworks. Courses taught include advanced econometrics and econometric theory.

Adam Sykulskiمشاهده پروفایل

مدرس ارشد

• Statistics
• Oceanography
• Applied Statistics
• +۵ مورد دیگر

Adam Sykulski is a Senior Lecturer in Statistics at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. His research focuses on time series and spatiotemporal statistics, particularly spectral analysis and Fourier methods, applied to environmental and oceanographic sciences. He holds roles including Trustee of the Royal Statistical Society Council (elected 2024), External Examiner at King's College London, and leads the EPSRC CDT in Mathematics for Future Climate. He has supervised 7 PhD students to completion, with 4 current advisees, and contributed to 32 peer-reviewed publications in top-tier journals like Biometrika and IEEE Transactions on Signal Processing. Education: PhD and MSci in Mathematics from Imperial College London (2008–2011 and 2003–2007). Professional development includes a Postgraduate Certificate in Academic Practice (2020). Research interests span statistical methodologies for environmental data, oceanographic modeling, and interdisciplinary applications. Key roles include Partner Investigator in the Australian Research Council’s TIDE project and leadership in equality, diversity, and inclusion initiatives at Imperial College. His work integrates statistical theory with practical challenges in climate science and operational research.

Constance van Eedenمشاهده پروفایل

استاد

• Estimation in restricted parameter spaces
• Decision theory
• Nonparametrics
• +۳ مورد دیگر

Constance van Eeden (1927–2021) was an Honorary Professor at the University of British Columbia (UBC) and held academic positions at institutions such as the University of Minnesota, Université de Montréal, and Université du Québec à Montréal. She contributed significantly to statistical theory, particularly in estimation in restricted parameter spaces, decision theory, and nonparametric methods. Her career spanned over five decades, during which she supervised numerous PhD and MSc students. Van Eeden was recognized with prestigious awards, including the 1990 Gold Medal from the Statistical Society of Canada and Fellowships from the Institute of Mathematical Statistics and American Statistical Association. Her research interests included foundational statistical problems such as parameter estimation under constraints, selection procedures, and robust estimation. She played a pivotal role in developing statistical programs in Canada and contributed to the establishment of the Constance van Eeden Endowment Fund at UBC, supporting statistical education and research initiatives. Van Eeden's work also involved editorial roles in journals like the Annals of Statistics and Canadian Journal of Statistics . Her legacy includes over 70 publications, including influential books and articles on estimation theory. The van Eeden Fund continues to support distinguished lectures, summer schools, and student awards, reflecting her commitment to advancing statistical science.

Dimitris N. Politisمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.

• Financial Economics
• Portfolio Optimization
• Stochastic Dominance
• +۵ مورد دیگر

Professor Wing-Keung Wong is a distinguished academic at the Department of Finance, Asia University . With over 187 scholarly papers and 638 citations, his work spans critical areas in financial economics and quantitative finance. Current affiliation: Asia University, Department of Finance Past affiliations: National University of Singapore, Chinese University of Hong Kong, Erasmus University Rotterdam Research Themes include: Portfolio optimization and stochastic dominance theory Market efficiency analysis across diverse financial instruments Behavioral finance and investor decision-making models Risk measurement with VAR and CVaR frameworks International financial market integration studies Quantitative trading system development Key Article Trends reveal consistent focus on empirical finance, mathematical modeling, and decision science applications in portfolio management and market anomalies.

Eleni Vasilakiمشاهده پروفایل

استاد

• Bioinspired Machine Learning
• Neuromorphic Computing
• Computational Neuroscience
• +۳ مورد دیگر

Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.

Marie Davidianمشاهده پروفایل

استاد

• Biostatistics
• Longitudinal Analysis
• Clinical Trials
• +۴ مورد دیگر

Marie Davidian is the J. Stuart Hunter Distinguished Professor of Statistics at North Carolina State University (NC State) and Director of Faculty Grants. She holds an adjunct position at Duke University's Department of Biostatistics and Bioinformatics. Her primary research focuses on statistical methods for dynamic treatment regimes, clinical trial design, longitudinal data analysis, and handling missing/mismeasured data. Davidian earned her Ph.D. in Statistics from the University of North Carolina at Chapel Hill in 1987 under Raymond J. Carroll. She has led significant training initiatives, including co-directing the Integrated Biostatistical Training Program for CVD Research (2006–2021) and the Summer Institute in Biostatistics (SIBS, 2004–2019). These programs emphasized cardiovascular disease research collaboration and biostatistical methodology. Her educational contributions include courses on longitudinal data analysis, missing data methods, and precision medicine. Her research interests span statistical methodologies for personalized medicine, causal inference, and pharmacokinetic modeling. She has been recognized with the Dr. D. D. Mason Award (2011–2012) and contributed to high-impact studies in oncology, HIV treatment, and chronic disease management. Davidian’s work bridges methodological innovation with practical applications in healthcare, emphasizing translational research and interdisciplinary collaboration.

Jonathan Dugginsمشاهده پروفایل

عضو هیئت علمی

• Statistics Education
• Nonparametrics
• Simulation-based Inference
• +۳ مورد دیگر

Jonathan Duggins is an Associate Teaching Professor in the Department of Statistics at North Carolina State University. He holds a Ph.D. in Statistics from Virginia Polytechnic Institute and State University (2010). His research focuses on statistics education methodologies, nonparametric analysis, and simulation-based inference. He is a co-author of Fundamentals of Programming in SAS: A Case Studies Approach , a textbook emphasizing practical SAS programming through real-world applications. Dr. Duggins has received the 2020-2021 Dr. Cavell Brownie Mentoring Award for his contributions to student development. His work spans educational program design, statistical software optimization, and agricultural student pathway analysis. His technical expertise includes advanced SAS programming techniques such as PROC REPORT customization, attribute map creation, and web scraping. Recent research explores agricultural student enrollment patterns and publishing methodologies for technical conferences like SESUG. His applied projects include developing simulated clinical research data frameworks and optimizing data manipulation efficiency across hardware/software systems. Ph.D. in Statistics (2010) — Virginia Tech Co-developed SAS programming curriculum for higher education Active contributor to SESUG conference proceedings His work bridges statistical theory with practical applications, emphasizing pedagogical innovations in technical education. Current projects include refining simulation-based teaching methods and advancing data visualization techniques in SAS environments.

Changbo Zhuمشاهده پروفایل

استادیار

• Optimal Transport
• Functional and Object Data Analysis
• High-Dimensional Statistics
• +۶ مورد دیگر

Changbo Zhu is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, part of the College of Science. He is also a Fellow of the Lucy Family Institute for Data & Society. His office is located at 101H Crowley Hall, and he can be reached at czhu4@nd.edu. Dr. Zhu holds a Ph.D. from the University of Illinois at Urbana-Champaign (2020), an M.S. and B.S. from the National University of Singapore (2016 and 2014, respectively). He completed a postdoctoral fellowship at the University of California, Davis from 2020 to 2022. His research focuses on the intersection of statistics, geometry, and optimization, with key interests in optimal transport, functional and object data analysis, time series analysis, high-dimensional statistical inference, and statistical machine learning. His work addresses challenges such as longitudinal data analysis, change point detection, and distance covariance methodologies. Zhu’s recent publications emphasize advancements in optimal transport theory, including barycenter optimization and autoregressive models, as well as applications in neuroimaging and high-dimensional data analysis. He collaborates with leading researchers such as Hans-Georg Müller and Jane-Ling Wang on topics ranging from spherical autoregressive models to brain volume trajectory studies. He advises Kaheon Kim, a Ph.D. candidate focusing on statistical optimal transport. His teaching portfolio includes courses on optimization algorithms for machine learning, computational statistics with R, and statistical computing methods. Zhu is affiliated with the Lucy Family Institute for Data & Society, contributing to interdisciplinary data science initiatives.