جست‌وجوی استاد و پژوهشگر

Ting Fung Maمشاهده پروفایل

استادیار

• Spatial Statistics
• Environmental Statistics
• Public Health Applications
• +۳ مورد دیگر

Ting Fung Ma is an Assistant Professor in the Department of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He joined the university in 2022 after completing his Ph.D. in Statistics at the University of Wisconsin-Madison, following a B.S. and M.Phil. in Statistics from the Chinese University of Hong Kong. Prior to academia, he worked as a government statistician and human resource analyst. His research focuses on developing novel methodologies for analyzing dependent and large-scale datasets, emphasizing theoretical rigor and computational efficiency. Key areas include spatial, spatiotemporal, and environmental statistics, with interdisciplinary applications in public health, machine learning, and data science. Recent work explores causal inference in ecological systems, pandemic-related epidemiological trends, and optimization of therapeutic regimens in chronic diseases. His publications demonstrate expertise in spatiotemporal modeling, change-point detection, and machine learning integration with ecological/disease dynamics. Notable themes include pandemic impact assessments, wildlife disease spread, and healthcare optimization strategies. His methodological contributions address challenges in big data and non-stationary processes. Ma’s academic journey reflects a blend of rigorous statistical training and practical experience, positioning him at the forefront of interdisciplinary research in computational statistics and environmental/public health analytics.

Dexin Shiمشاهده پروفایل

دانشیار

• Psychometrics
• Statistical Methodology
• Causal Inference
• +۳ مورد دیگر

Dr. Dexin Shi is an Associate Professor in the Department of Psychology at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds expertise in quantitative psychology, focusing on statistical methodology development and application. Dr. Shi earned his Ph.D. in Quantitative Psychology from the University of Oklahoma (2016) and completed a postdoctoral fellowship at the University of South Carolina. His research emphasizes psychometrics, causal inference, missing data analysis, and model selection, with applications in educational, clinical, and health psychology. He has published over 50 peer-reviewed articles in top journals like Psychological Methods and Structural Equation Modeling . Notable awards include the 2021 Rising Star Award from the Association for Psychological Science and the McCausland Faculty Fellowship (2021–2024). Dr. Shi teaches courses such as PSYC 220 (Psychological Statistics) and advanced quantitative methods like PSYC 815 (Causal Inference). He has received grants for innovative pedagogy and open educational resources. His research trends highlight advancements in statistical techniques, including Bayesian methods, machine learning applications in equating, and causal pathway identification. He actively explores methodological challenges in psychological assessment and data analysis.

Hsin-Hsiung Huangمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Machine Learning
• Neuroimaging
• +۳ مورد دیگر

Dr. Hsin-Hsiung Huang is an Associate Professor in the Department of Statistics and Data Science at the University of Central Florida (UCF). His research focuses on Bayesian modeling, dimension reduction, neuroimaging, and threat detection. He holds a Ph.D. in Statistics from the University of Illinois at Chicago and multiple advanced degrees in Mathematics and Statistics from Georgia Tech and National Taiwan University. Dr. Huang has led projects funded by NSF's ATD program (e.g., NSF DMS-1924792 and DMS-2318925) and NIH's NINDS R01 award. His team achieved top placements in the ATD Challenge competitions from 2021 to 2023. Awards include the UCF Research Incentive Award (2021), College of Sciences Seed Grant (2023–24), and the 2025 OSCaR Award for Outstanding Scholarship. His research interests include Bayesian ultrahigh-dimensional variable selection, matrix/tensor regression, and spatiotemporal modeling. Key areas of application include neuroimaging reconstruction, threat detection algorithms, and medical data analysis. Publications span statistical methodologies, neuroimaging algorithms, and applications in healthcare and environmental risk modeling. He actively collaborates across disciplines, integrating machine learning with biomedical and ecological data challenges.

Larry Tangمشاهده پروفایل

استاد

• Statistical Methodology
• Forensic Science
• Diagnostic Medicine
• +۴ مورد دیگر

Dr. Larry Tang is a Professor in the Department of Statistics & Data Science at the University of Central Florida (UCF), affiliated with the National Center for Forensic Science and the NIH Clinical Center. He previously held positions at George Mason University (2007-2019) and completed postdoctoral training in Biostatistics at the University of Washington. His research focuses on statistical methodology applied to forensic science, diagnostic medicine, clinical trial design, and criminology, with funding from the National Institute of Justice and National Institutes of Health. Education: B.S. in Finance, University of Science and Technology of China (2000) M.S./Ph.D. in Statistics, Southern Methodist University (2003/2005) Postdoctoral Fellow, University of Washington (2005-2007) Research Interests: Dr. Tang develops statistical methods for evaluating forensic evidence, diagnostic tests, and criminological studies. Key areas include ROC curve analysis, group sequential designs, algorithmic bias detection, and biometric recognition systems. His work bridges theoretical statistics with practical applications in criminal justice and healthcare. Grants & Collaborations: Active research is supported by federal grants focusing on forensic methodology and mental health outcomes. He collaborates with the NIH Clinical Center on disability assessment studies and the National Center for Forensic Science on fire debris analysis and latent fingerprint evaluation.

Richard Goldenمشاهده پروفایل

استاد

• Computational Psychometrics
• Statistical Machine Learning
• Learning Sciences
• +۳ مورد دیگر

Richard Golden is Professor of Cognitive Science in the School of Behavioral and Brain Sciences at the University of Texas at Dallas, with a cross-appointment in the Erik Jonsson School of Engineering and Computer Science. He directs the Cognitive Informatics and Statistics (COINS) Laboratory and serves as Program Head for both the undergraduate Cognitive Science BS and graduate Applied Cognition and Neuroscience MS programs. Education: Ph.D. in Experimental Psychology (Computational Cognitive Science) - Brown University (1987) M.S. in Electrical Engineering (Statistical Pattern Recognition) - Brown University (1986) B.S. in Electrical Engineering and Psychology - University of California, San Diego (1982) Postdoctoral training at Stanford University and University of Pittsburgh Research Focus: Golden's work integrates psychometrics, learning science, and statistical machine learning to develop quantitative models of skill acquisition. His research spans: (1) Computational psychometrics for educational assessment, (2) Theoretical foundations of machine learning algorithms, and (3) Cognitive modeling of text comprehension and recall using Markov field approaches. Publication Trends: Recent work (2018-2023) focuses on cognitive diagnostic models, model misspecification detection, and statistical learning frameworks. His research demonstrates consistent innovation in psychometric methodologies with applications in education and healthcare. Awards & Honors: Invited keynote speaker for major conferences in mathematical psychology and neural networks IEEE Senior Member (1999) Secretary-Treasurer Service Award from Society for Mathematical Psychology (2017) Faculty Development Award for econometrics research (1998) Students & Funding: Actively advises graduate students in cognitive science and engineering. Recent grants include: SPARK: Information Matrix Tests for Cognitive Diagnostic Models ($50,000, UT Dallas) Neuroscience-Inspired Deep Learning ($16,000, Raytheon) Cognitive Diagnostic Knowledge Tracing research ($10,000, UT Dallas) Laboratory Leadership: Directs COINS Lab researching cognitive informatics and statistical methodologies. Maintains affiliations with Society for Mathematical Psychology, Psychometric Society, and International AI in Education Society.

Tariq Hasanمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Zero-Inflated Models
• Spatiotemporal Statistics
• +۲ مورد دیگر

Tariq Hasan is Professor and Director of Graduate Studies in Mathematics and Statistics at the University of New Brunswick. His research develops statistical methods for complex data structures including longitudinal, zero-inflated, and spatiotemporal datasets. Applications span environmental science, public health, and ecology. Methodological innovations include: joint modeling of clustered multinomial outcomes, Tweedie GLMs for skewed longitudinal data, and zero-inflated models accounting for autocorrelation. Work addresses real-world challenges like amphibian roadkill mortality analysis, air pollution health impacts, and osteoporotic fracture risk prediction. Publications demonstrate consistent focus on handling data complexities: simultaneous modeling of correlated processes, preservation of experimental designs in paired analyses, and integration of random effects for cluster heterogeneity. Recent advances address state-space frameworks for binomial series with random cluster sizes.

Ba M. Chuمشاهده پروفایل

استاد

• Asset Allocation
• Risk Management
• Dependence Modelling
• +۴ مورد دیگر

Ba M. Chu is a Full Professor in the Department of Economics at Carleton University, affiliated with the Faculty of Public and Global Affairs. He holds degrees from Hanoi and London, including a Ph.D. in Economics. His expertise spans optimal asset allocation, risk management, dependence modeling, and asymptotic theory. B.A. (Hanoi) M.Sc., M.Phil., Ph.D. (London) Research focuses on econometric methodologies such as VaR estimation via large deviations, copula modeling, goodness-of-fit testing with L-moments, and non-linear dependence measures. Key contributions include pandemic forecasting models and foundational work on copula recovery techniques. His recent publications address topics ranging from COVID-19 economic impacts to panel data analysis and financial risk modeling. Dr. Chu speaks Vietnamese and Mandarin fluently alongside English. His work integrates theoretical econometrics with practical applications in finance and public policy, emphasizing robust statistical frameworks for complex economic systems.

Jost Reineckeمشاهده پروفایل

استاد

• Rational Choice Theories
• Longitudinal Classification
• Structural Equation Modeling
• +۳ مورد دیگر

Prof. Dr. Jost Reinecke is a Professor of Quantitative Methods of Empirical Social Research at Bielefeld University, Faculty of Sociology. He holds affiliations with the Center for Statistics, Bielefeld Center for Data Science, and serves on the Executive Board of the Bielefeld Graduate School in History and Sociology. His education includes a Ph.D. in Sociology from the University of Giessen (1990) and post-doctoral qualification from the University of Münster (1996). Research interests focus on rational choice theories, longitudinal and cross-sectional classification modeling, juvenile delinquency development, health behaviors in adolescents, and advanced multiple imputation techniques for complex datasets. His work bridges theoretical social science and applied statistical methodology. Recent publications demonstrate a strong emphasis on advanced statistical modeling, criminological research, and social cohesion studies. Key themes include Bayesian methods, panel data analysis, growth curve modeling, and applications in German social policy contexts. Methodological innovation in handling incomplete data and time-series analysis is consistently evident.

Zsolt Talataمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Probability Theory
• Information Theory
• +۳ مورد دیگر

Zsolt Talata is an Associate Professor of Mathematics at the University of Kansas, affiliated with the Department of Mathematics within the College of Liberal Arts & Sciences. He holds a Ph.D. in Applied Mathematics from Budapest University of Technology and Economics (2005) and has held academic positions at the Georgia Institute of Technology (2005-2007) and the Hungarian Academy of Sciences. His research focuses on mathematical statistics, probability theory, and information theory, with funding from the NSF and U.S. Army Research Office. He has been recognized for teaching excellence and scholarly contributions, including induction into Phi Beta Delta and leadership roles in statistical honor societies. Education: M.S. in Electrical Engineering (2000) and Ph.D. in Applied Mathematics (2005) from Budapest University of Technology and Economics. Research areas include statistical inference, model selection, Gibbs fields, and applications in bioinformatics and materials science. He has authored over 20 peer-reviewed publications and secured multiple grants, including NSF awards for statistical estimation of process memory structures. Teaching spans graduate and undergraduate courses in stochastic processes, statistical theory, probability, and applied statistics. He has advised numerous research projects, including collaborations on hydrogel mechanics for cartilage regeneration. His work bridges theoretical statistics with practical applications, emphasizing interdisciplinary research and education.

Yuping Zhangمشاهده پروفایل

دانشیار

• High-Dimensional Data Analysis
• Statistical Genomics
• Network Biology
• +۲ مورد دیگر

Yuping Zhang is an Associate Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. She holds a PhD in Statistics from Peking University and completed postdoctoral training at Stanford University. Her research develops statistical methods for high-dimensional genomic data, including single-cell transcriptomics, chromatin conformation analysis, and integrative pathway modeling. Dr. Zhang creates computational tools for extracting biological insights from complex omics datasets. She has made significant contributions to trajectory inference for single-cell RNA-seq data, network-based analysis of multi-platform genomics, and statistical learning of chromosomal architecture. Her methodologies facilitate precision medicine applications in cancer genomics and developmental biology. Dr. Zhang's lab creates open-source software for analyzing chromatin interactions, reconstructing cell differentiation trajectories, and detecting structural variations from sequencing data.

Yanchun Baoمشاهده پروفایل

مدرس ارشد

• Longitudinal and survival analysis
• Causal methods
• Mendelian Randomization
• +۳ مورد دیگر

Dr. Yanchun Bao is a Senior Lecturer in Statistics and Data Science at the School of Mathematics, Statistics and Actuarial Science (SMSAS), University of Essex. She holds a PhD in Statistics from the University of Manchester (2007), an MSc from Peking University (2002), and a BSc from Yunnan University (1999). Prior roles include postdoctoral research positions at the University of Manchester, Brunel University, and University College London, followed by research roles at the Institute for Social and Economic Research (ISER) from 2014 to 2019. Her research focuses on statistical methodology in health, biology, and social sciences, encompassing longitudinal/survival analysis, causal inference (e.g., Mendelian Randomization), bioinformatics (omic data analysis: SNPs, DNAm, proteomics), and covariance modeling. She actively supervises PhD students in these areas, emphasizing interdisciplinary applications. Key contributions include studies on epigenetic markers in shift work, socioeconomic health gradients, and genomic analyses of risk tolerance. Her work leverages large datasets such as Understanding Society and UK Biobank. Collaborations span institutions like ISER, Brunel University, and University College London. Grants and funding details are listed but not elaborated here.

Susumu Shikanoمشاهده پروفایل

استاد

• Political Methodology
• Bayesian Statistics
• Electoral Systems
• +۲ مورد دیگر

Susumu Shikano is Professor of Political Methodology at the University of Konstanz’s Department of Politics and Public Administration. He directs the interdisciplinary Master’s program in Social and Economic Data Science (SEDS) and leads research projects on electoral systems and behavioral decision-making. His research integrates Bayesian statistics , experimental methods, and formal modeling to analyze political behavior, administrative delegation, and electoral dynamics. Key themes include voter perceptions of party competition, behavioral consequences of public service motivation, and methodological innovations in multilevel analysis. Recent experimental work explores visual influences in voting, risk aversion in bureaucracy, and misinformation during health crises. Publications emphasize quantitative rigor, with applications to European and German political contexts. Shikano teaches graduate courses in Bayesian models, survey design, and causal inference. He currently investigates inequities in online information-seeking via an NDTP-funded project.

Min Tsaoمشاهده پروفایل

استاد

• Empirical Likelihood
• Model/Variable Selection
• Group Effects in Linear Models
• +۳ مورد دیگر

Min Tsao is a Professor in the Department of Mathematics and Statistics at the University of Victoria (UVic). He holds a PhD from Simon Fraser University. His primary research interests focus on empirical likelihood methodologies, model/variable selection techniques, and group effects in regression models with strongly correlated predictors. He has contributed to advancements in constrained minimum criterion (CMC) for model selection and group least squares regression to address multicollinearity issues. Education: PhD in Statistics, Simon Fraser University Research Interests: Development of extended empirical likelihood frameworks for improved statistical inference Model selection criteria using log-likelihood ratios (e.g., CMC as an alternative to AIC/BIC) Handling multicollinearity through group effects and group least squares regression Applications of saddlepoint approximation in statistical methods Notable Achievements: Recipient of the Canadian Journal of Statistics Award Teaching: Fall 2024: STAT 350 (Mathematical Statistics I) and STAT 353 (Applied Regression Analysis) Spring 2025: Courses to be announced Prof. Tsao’s work bridges theoretical statistics with practical applications, emphasizing robust methodologies for complex regression scenarios.

Juan Restrepoمشاهده پروفایل

استاد

• Non-equilibrium statistical dynamics
• Data assimilation
• Artificial Intelligence
• +۶ مورد دیگر

Juan Restrepo is a Joint Faculty Professor at the University of Tennessee, Knoxville, with a primary appointment in the Department of Mathematics within the College of Arts and Sciences. He also holds adjunct positions at Duke University and Oregon State University. As Section Head of Mathematics in Computation at Oak Ridge National Laboratory (ORNL), he leads research in data-driven methodologies, scientific computing, and uncertainty quantification. His expertise spans non-equilibrium statistical dynamics, data assimilation, and probabilistic methods in climate science and oceanography. Education: Restrepo earned a PhD in Physics (1992) and MS in Engineering Acoustics (1987) from Pennsylvania State University, an Electrical Engineering degree (1985) from Columbia University, and a BS in Music (1983) from New York University. Research Interests: He focuses on integrating data and physical models using AI, machine learning, and Bayesian techniques. Applications include climate dynamics, ocean transport, epidemics, and coastal resilience. His work bridges deterministic and stochastic systems, with contributions to high-performance computing and ensemble methods. Grants & Awards: Notable grants include DOE funding for high-performance ensemble computing and interdisciplinary projects on climate resilience. Awards include APS and SIAM Fellowships, the SIAM Geosciences Career Prize, and a DOE Young Investigator Award. Advising & Service: Restrepo has mentored numerous PhD and master’s students in applied mathematics, climate science, and computational methods. He actively promotes diversity in STEM, serving on committees for professional societies and leading initiatives at Oregon State University. His leadership roles include co-directing the Dynamics and Data Science Institute (D2SI).

Loukia Meligkotsidouمشاهده پروفایل

دانشیار

• Bayesian Inference
• Computational Statistics
• Hidden Markov Models
• +۴ مورد دیگر

Loukia Meligkotsidou is an Associate Professor of Statistics at the Department of Mathematics of the National and Kapodistrian University of Athens. She holds a BSc in Statistics from Athens University of Economics and Business (2002) and a PhD in Statistics from Lancaster University (2005), focusing on filtering methods for population genetics, phylogenetics, and mixture models. Her research interests span Bayesian inference, computational statistics, hidden Markov models, biostatistics, and econometrics. She teaches undergraduate courses on Bayesian Inference and postgraduate courses on Time Series analysis. Her work emphasizes methodological advancements in statistical modeling, particularly in longitudinal data analysis, competing risks, and model uncertainty. Recent publications address issues like shared parameter models for longitudinal data, Bayesian unit root testing, and quantile regression approaches in financial forecasting. She is a Fellow of the Royal Statistical Society and active in professional societies like ISBA and IMS. Her research integrates theoretical contributions with applied problems in economics, finance, and biology. Notable contributions include frameworks for handling informative dropout mechanisms and developing efficient Bayesian computational methods for complex models. Her work bridges statistical methodology and real-world applications, particularly in econometrics and biostatistics.