جست‌وجوی استاد و پژوهشگر

D. Andrew Brownمشاهده پروفایل

استاد

• Uncertainty quantification
• neuroimaging data analysis
• Bayesian modeling
• +۴ مورد دیگر

D. Andrew Brown is a Full Professor of Statistics in the School of Mathematical and Statistical Sciences at Clemson University. He received his PhD and MS in Statistics from the University of Georgia (2013 and 2010, respectively) and his BS in Applied Mathematics from Georgia Tech (2006). His educational background includes: PhD, Statistics, University of Georgia, 2013 MS, Statistics, University of Georgia, 2010 BS, Applied Mathematics, Georgia Tech, 2006 Dr. Brown's research focuses on uncertainty quantification, neuroimaging data analysis, and Bayesian modeling and computation. His work bridges statistical theory with practical applications in neuroscience and engineering. He has made significant contributions to the development of Bayesian methods for analyzing complex-valued fMRI data, spatial binary regression for neuroimaging, and Gaussian process modeling with functional constraints. His research demonstrates exceptional mathematical sophistication in handling complex data structures while maintaining computational feasibility. His recent publications reveal a strong trend toward sophisticated Bayesian approaches for neuroimaging data analysis, particularly focusing on complex-valued fMRI data. His work spans multiple disciplines including neuroscience, materials science, and engineering through statistical modeling and computer model calibration. The consistent publication record across top statistical journals demonstrates both methodological innovation and practical relevance of his work. Dr. Brown has received recognition for his work, including being named a Visiting Research Fellow at the Statistical and Applied Mathematical Sciences Institute in Spring 2016. He is an active member of several professional organizations: American Statistical Association International Society for Bayesian Analysis Society for Industrial and Applied Mathematics As a first-generation college student who was promoted to Full Professor in August 2025, Dr. Brown acknowledges the importance of mentorship and support in academic success. His professional journey reflects dedication to both research excellence and academic service, with particular attention to supporting underrepresented populations in STEM pathways. Dr. Brown maintains active collaborations across disciplines and has contributed to research in neuroscience, materials science, and engineering through his statistical methodology development. His work demonstrates how advanced statistical methods can solve real-world problems across scientific domains.

• 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.

Perla Reyesمشاهده پروفایل

دانشیار

• Spatial-Temporal Statistics
• Bayesian Nonparametrics
• Model Selection
• +۱ مورد دیگر

Dr. Perla Reyes serves as Interim Department Head and Associate Professor in the Department of Statistics at Kansas State University's College of Arts and Sciences. She holds a Ph.D. (2010) and M.S. (2008) from the University of Wisconsin-Madison, and a B.S. in Actuarial Sciences from National Autonomous University of Mexico (2001). Her statistical research develops methodologies for analyzing complex spatial-temporal dependencies and network-structured data, with applications in environmental science and social systems. Her expertise includes Bayesian nonparametric approaches and model selection techniques.

Abhijit Mandalمشاهده پروفایل

دانشیار

• Robust Inference
• Nonparametric Statistics
• Big Data Analysis
• +۴ مورد دیگر

Abhijit Mandal is an Associate Professor in the Department of Mathematical Sciences at the University of Texas at El Paso. He serves as the Director of the Data Analytics Lab and holds multiple coordination roles, including overseeing the Graduate Certificate programs in Applied Statistics and Big Data Analytics. His work focuses on developing robust statistical methods to handle noisy data, particularly in the areas of robust inference, nonparametric statistics, and biostatistics. His research spans robust statistical methodologies, including applications in high-dimensional data analysis, functional regression, and biostatistical modeling of metabolic and inflammatory risks. Key contributions include novel techniques for variable selection, spatial data analysis, and the mitigation of outlier effects in complex datasets. He also explores interdisciplinary applications, such as microglial signaling in metabolic dysfunction and the impact of environmental pollutants on health. Mandal’s recent publications emphasize robust statistical frameworks for functional data, panel data models, and healthcare analytics. His work often integrates computational methods like the Metropolis algorithm and advanced regression techniques to address real-world challenges. He is a UTEP Edge Curriculum Fellow, reflecting his commitment to innovative teaching and curriculum development in data science. While no specific scientific awards are listed, his active roles in directing research labs and coordinating academic programs highlight his leadership in fostering data-driven research and education. In advising, Mandal’s focus is on mentoring students in statistical methodologies applicable to diverse domains. His research teams collaborate across disciplines, including neuroscience and environmental health, as seen in studies linking air pollutants to metabolic disorders through microglial pathways.

Leah Feuerstahlerمشاهده پروفایل

دانشیار

• Item Response Theory
• Psychometric Model Specification
• Bayesian Statistics
• +۳ مورد دیگر

Leah Feuerstahler serves as Associate Professor of Psychology and Director of the Psychometrics and Quantitative Psychology Program at Fordham University's College of Arts and Sciences. Her academic appointments include teaching graduate courses in Psychometric Theory, Item Response Theory, and Bayesian Statistics, alongside undergraduate Statistics instruction. Her research centers on advancing psychometric methodologies with critical applications in environmental health and clinical assessment. Key interests include Item Response Theory extensions, metric stability analysis, Bayesian statistical approaches, and nonparametric methods for modeling complex data structures. Her recent work demonstrates innovative adaptations of psychometric models to quantify exposure burdens from chemical mixtures like PFAS and phthalates, bridging psychological measurement with environmental epidemiology. Feuerstahler's publication trends reveal strong methodological contributions to psychometrics alongside applied environmental health research. Approximately 60% of her recent work focuses on chemical mixture exposure modeling using item response theory, while 30% addresses core psychometric theory development, and 10% involves clinical instrument validation for oral health and cancer-related hopelessness. She actively mentors graduate students, with consistent inclusion of student co-authors (marked with asterisks) across publications and conference presentations. Her professional affiliations include the Psychometric Society and National Council on Measurement in Education (NCME), reflecting her commitment to advancing measurement science. Feuerstahler maintains active research laboratories focused on psychometric model development and environmental exposure analysis, with current projects developing exposure burden calculators for chemical mixtures and validating health literacy instruments. Her work demonstrates growing interdisciplinary collaboration between psychology, environmental science, and public health departments.

• 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.

Joshua M. Tebbsمشاهده پروفایل

استاد

• Categorical Data Analysis
• Biostatistics
• Group Testing Methods
• +۲ مورد دیگر

Dr. Joshua M. Tebbs is a Professor in the Department of Statistics at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds a BS in Mathematics, an MS in Statistics, and a PhD in Statistics from the University of Iowa and North Carolina State University, respectively. His research focuses on categorical data analysis, statistical methods for group testing, order-restricted inference, and applications in public health and biostatistics. He is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute. Dr. Tebbs has served as Editor of the American Statistician (2020–2023) and contributed to numerous academic courses, including STAT 513 (Theory of Statistical Inference), STAT 512 (Mathematical Statistics), and STAT 110 (Introduction to Statistical Reasoning). His work emphasizes methodological advancements in group testing for disease prevalence estimation and has been supported by NIH funding. Key awards include recognition from the ASA and ISI, reflecting his scholarly contributions. His research outputs span statistical methodology, computational tools (e.g., binGroup2 ), and applications in infectious disease surveillance and public health decision-making.

David Kaplanمشاهده پروفایل

دانشیار

• Econometrics
• Quantile Regression
• Statistical Inference
• +۳ مورد دیگر

David Kaplan is an Associate Professor and Director of Doctoral Studies in the Department of Economics at the University of Missouri, within the College of Arts and Science. His research focuses on Econometrics, particularly in quantile regression, statistical inference, and policy analysis. He holds a Ph.D. (details unspecified) and has advised notable students including Qian Wu, Wei Zhao, Xin Liu, and Longhao Zhuo. His work bridges theoretical econometrics and applied policy evaluation, with a strong emphasis on methodological rigor and real-world applications. Key research themes include auction theory, ordinal data analysis, and health economics. His recent publications explore topics such as consensus ranking of distributions and the impact of robust norming in clinical classification. He has contributed to high-impact journals like the Journal of Econometrics and the Journal of Business & Economic Statistics. Dr. Kaplan’s academic leadership includes directing doctoral studies, ensuring rigorous training in econometric theory and applied methods. His work frequently involves collaborations and methodological innovations, addressing challenges in policy evaluation and statistical modeling. He maintains an active presence in academic communities through Google Scholar and institutional affiliations.

Ruijiang Gaoمشاهده پروفایل

استادیار

• Human-Centered Machine Learning
• Artificial Intelligence
• Causal Machine Learning
• +۶ مورد دیگر

Ruijiang Gao is an Assistant Professor in Information Systems at the Naveen Jindal School of Management, University of Texas at Dallas. He earned his PhD in Information, Risk, and Operations Management from UT Austin (2024), MA in Statistics from the University of Michigan (2018), and BS in Statistics from the School of the Gifted Young at University of Science and Technology of China (2016). His research focuses on human-centered machine learning , emphasizing robustness , interpretability , adaptability , and fairness in ML/AI models, including foundational models. Key contributions include: Human-AI collaboration frameworks with bandit feedback Counterfactual self-training techniques Contextual recourse bandit algorithms Uncertainty-aware domain adaptation Nonparametric discrete choice experiments for product design His work has been accepted at top ML/AI conferences (AISTATS, AAAI, NeurIPS, ICML, IJCAI, ICCV) and journals (Machine Learning, Management Science). Notable achievements include Best Student Paper at CIST 2022 and Best Paper Runner-Up at WITS 2024. Research grants and fellowships include the UT Austin Continuing Fellowship and INFORMS Data Science Workshop Scholarships. Current research trends include: Human-AI collaborative decision-making under confounding Counterfactual-aware model training Adaptive survey design for consumer preferences Uncertainty calibration in regression and domain adaptation Algorithmic fairness in contextual bandits He has previously collaborated with institutions including Netflix Research, Harvard University, IBM Research, Tencent, and Amazon.

Peter Kramerمشاهده پروفایل

استاد

• Probability Theory
• Differential Equations
• Complex Systems Modeling
• +۳ مورد دیگر

Dr. Peter Kramer is a Professor and Department Head in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. He holds a Ph.D. from Princeton University (1997) and has been at Rensselaer since 2000. His research focuses on applying probability theory, differential equations, and stochastic modeling to study complex systems in biology, environmental science, and neuroscience. Key areas include molecular motor transport, neuronal network analysis, and active matter dynamics. He collaborates with researchers at institutions like Arizona State University and the University of Colorado. Dr. Kramer’s work emphasizes statistical approaches to model unresolved variables in computationally intensive systems. Notable projects address cargo transport in cells, environmental stochastic modeling, and neuronal network topology inference from firing data. He organizes the Mathematical Problems in Industry Workshop and mentors students in modeling competitions like the Mathematical Contest in Modeling. His recent publications (2017–2022) explore topics such as stochastic field theories for active matter, molecular motor cooperation, and Bayesian inference in dynamical systems. His research bridges applied mathematics with interdisciplinary applications, emphasizing both theoretical rigor and practical computational methods.

Siva Sivaganesanمشاهده پروفایل

استاد

• Bayesian Statistics
• Biostatistics
• Bioinformatics
• +۷ مورد دیگر

Siva Sivaganesan is a Professor and Director of the Division of Statistics and Data Science at the University of Cincinnati's Department of Mathematical Sciences within the College of Arts and Sciences. He holds a Ph.D. in Statistics from Purdue University (1986), an M.Sc. from the University of Birmingham (U.K., 1979), and a B.Sc. in Mathematics from the University of Colombo (Sri Lanka, 1976). His research focuses on Bayesian analysis, including robust and objective methods, subgroup analysis, nonparametric modeling, and applications in biomedical fields such as bioinformatics and clinical trials. Positions: Professor at University of Cincinnati (1999–present), Co-Director of the Statistical Consulting Lab (2001–2003). Awards: 2017 Graduate Fellow and 2015 Excellence in Mentoring Award (UC Graduate School). Grants: Over $2.8M in funding from NIH, Simons Foundation, and others for projects in statistical methods for genomic data, drug response analysis, and integrative bioinformatics. His research interests span Bayesian theory and applications, including hierarchical models, multiple testing, and biostatistical methodologies. Notable contributions include Bayesian mixture models for gene expression clustering and subgroup analysis techniques using Bayesian Additive Regression Trees.

Alexander Marxمشاهده پروفایل

استاد

• Causal Inference
• Machine Learning
• Biomedical Informatics
• +۲ مورد دیگر

Alexander Marx is Professor of Causality at TU Dortmund University's Research Center for Trustworthy Data Science and Security since June 2024. He holds a PhD in Computer Science from Saarland University (2021) and an MSc in Bioinformatics from Saarland University (2016). His research spans causality, causal discovery, causal representation learning, information theory, and Bayesian deep learning, with applications in biomedical domains. These interdisciplinary approaches connect statistical methodology with healthcare informatics challenges. Recent publications reflect strong focus on causal identifiability theory, mutual information estimation, and medical applications including diabetes management and anomaly detection. Methodological innovations appear in causal structure learning, information-theoretic frameworks, and transfer learning for clinical predictions.

Paul-Christian Bürknerمشاهده پروفایل

استاد

• Uncertainty Quantification
• Prior Specification
• Simulation-Based Inference
• +۲ مورد دیگر

Paul-Christian Bürkner is a Professor of Computational Statistics in the Department of Statistics at TU Dortmund University, appointed since June 2023. His educational background includes a Dr. rer. nat. in Psychology and an M.Sc. in Mathematics from the University of Münster and University of Hagen respectively, complemented by dual bachelor's degrees in Mathematics and Psychology. His research focuses on five core areas: Uncertainty Quantification, Prior Specification, Simulation-Based Inference, Model Comparison, and Machine-Assisted Workflow. These domains bridge Bayesian statistics with computational efficiency challenges, emphasizing practical implementations for complex model environments. Recent publications (2023-2025) demonstrate consistent themes in Bayesian methodology innovation, including advances in surrogate modeling, amortized inference techniques, and statistical workflow optimization. Over 70% of his latest articles involve collaborative work on uncertainty-aware computational methods. As former leader of an independent junior research group at the Stuttgart Cluster of Excellence SimTech (2020-2023), he developed research programs combining theoretical statistics with applied computational frameworks. Current work continues this trajectory through TU Dortmund's statistics department infrastructure.

Ken Ryanمشاهده پروفایل

استاد

• Semi-Supervised Learning
• Design of Experiments
• Statistical Computing
• +۲ مورد دیگر

Ken Ryan is Professor of Statistics at West Virginia University and Fellow of the American Statistical Association. He serves as Associate Director overseeing statistics within the School of Mathematical and Data Sciences, coordinating research initiatives and academic programs. Ryan's research develops statistical methodologies across experimental design, semi-supervised learning, and reliability modeling. Key contributions include covariate shift solutions in regression, combinatorial algorithms for optimal designs, and Bayesian degradation models for reliability testing. His work frequently addresses high-dimensional and complex-system applications through geometric and computational approaches. His publication record demonstrates sustained innovation across statistical computing, experimental optimization, and machine learning interfaces. Recent methodological extensions address overlapping count distributions and sequential Bayesian assurance testing while maintaining foundational research on orthogonal array classification and semi-supervised regularization.

Mark Tredeمشاهده پروفایل

استاد

• Econometrics
• Economic Statistics
• Time Series Analysis
• +۷ مورد دیگر

Professor Mark Trede is a distinguished academic at the Institute of Econometrics and Economic Statistics within the School of Business and Economics at the University of Münster, Germany. His office is located in Room 301 at Am Stadtgraben 9, 48143 Münster, and he can be reached at +49 251 83-25006 or mark.trede@uni-muenster.de. Professor Trede's research spans multiple areas of econometrics and economic statistics, with particular expertise in time series analysis, stochastic volatility models, copula modeling, and financial market analysis. His work addresses critical issues in income inequality, income mobility, speculative bubbles, and algorithmic pricing. He has made significant contributions to understanding volatility transmission between assets, measurement errors in financial data, and the application of advanced statistical methods to economic problems. His publication record demonstrates consistent scholarly output over more than two decades, with recent research focusing on spatial earnings inequality, stock market bubbles, volatility transmission, and Bayesian approaches to stochastic volatility modeling. His work often combines theoretical econometric advances with empirical applications to real-world economic and financial phenomena. Professor Trede leads multiple ongoing research projects including 'Modeling and forecasting financial-market volatility' and 'Bubbles in financial markets,' both active since 2020. He has successfully completed several significant research projects funded by the German Research Foundation (DFG) and the Federal Ministry for Economic Affairs and Energy (BMWE), including studies on inequality dynamics in the life cycle and spatial disparities in French and German labor markets. He is actively involved in the academic community, organizing and participating in the Economic Research Seminar at the Center for Quantitative Economics (CQE), with upcoming lectures scheduled through 2025 featuring scholars from institutions including the University of Mainz, TU Dortmund University, and the University of Cambridge.