Statistical science is a broad discipline that encompasses activities from data acquisition and
processing, exploratory data analysis, complex modeling of stochastic processes, inference,
hypothesis testing, parameter estimation, uncertainty quantification, Bayesian and casual inference, and decision making under uncertainty. The Department’s mission is to function as a leading international center for statistical research, education, and service.
The Department boasts a diverse array of research in statistical methodology and applications, with faculty members recognized nationally and internationally for their contributions to the field. Among the accolades received are the COPPS Award (Carroll), the Noether Senior Scholar Award and Lecture (Carroll, Spiegelman), and the Wilcoxon Prize (Carroll). These awards underscore the Department's commitment to advancing statistical science and its impact on the broader academic community.
Current strengths of Department include spatial statistics, Bayesian methodology, functional data analysis, modeling of measurement error, non-parametric methods, analysis of high and ultrahigh dimensional data, causal inference, and computational bioinformatics. Further discussion of the faculty’s research productivity is provided throughout our webpages.
As part of a land-grant university, the Department takes its responsibility for statistical education very seriously. The Department teaches approximately 5,700 undergraduate students each year. A vast majority of these students enroll in service courses designed to provide a rudimentary understanding of statistics that will allow them to interpret scientific findings in their fields of study, as well as to interpret statistical analysis reported in popular media, government studies and business reports. The more advanced of these courses also provide students with background in experimental design, the statistical analysis of simple experiments and observational studies, and regression analysis.
Our excellent academic environment fosters world-class, modern, and impactful research.
The Statistics Department will advance five research goals that were initially identified during the SOAR meeting and refined by working group suggestions and Statistics faculty feedback. These research goals significantly enhance the existing strengths and unique capabilities of the Statistics Department and look to the future of statistical research by identifying innovative strategic initiatives that require new statistical foundations, methodology, computational thinking, and infrastructure. Our five identified research goals are facilitated by three overreaching departmental research strengths: our excellence in Bayesian Statistics, in Statistical Computing, and Theoretical Foundations.
Some objectives common for all goals are sponsoring RA positions with interdepartmental faculty who conduct research on the interface of two or more identified goals, providing resources for focused workshops, and in future junior hires target the excellent candidates with research interest in one or more stated goals.
The first goal is to strengthen our leadership in Statistical Data Science by facilitating research opportunities in data science and enhancing our reputation in data-science research. The next goal is to develop a strong research group in Causality by becoming renowned for causal research, education, and training in 5-10 years.
Thirdly, we plan to establish a Research Center for Temporal and Spatial Statistics by maintaining and strengthening our reputation in the research areas of time series and spatial statistics; and increasing the impact and visibility of temporal and spatial statistics.
Another goal is to establish a Center for Health Data Science and Biomedical Statistics by building on existing strengths to become leaders in cross-domain Health Data Science. We will focus some faculty hiring in specific areas such as biomedical data integration, electronic health record data and causal research, and spatio-temporal applications. Additionally, we can provide resources to support biomedical research activities and Increase impact and visibility of Health Data Science research.
Finally, we will undertake new strategic research initiatives by building a program for researching improved methods of Statistics Education; building up research capacity in Quantum Information Science (QIS); increasing research efforts in Astrostatistics; and to strengthen our instruction and research capacity in Bioinformatics jointly with the Department of Biology.
Looking ahead, we will embark on several strategic research initiatives to advance our Department's impact. These include:
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Developing a Program for Enhanced Statistics Education: We aim to innovate and improve methodologies in statistics education utilizing advancements in AI.
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Expanding Research in Quantum Information Science (QIS): Building research capacity in this cutting-edge field is a priority.
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Increasing Efforts in Astrostatistics: We will enhance our research endeavors in this interdisciplinary area.
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Strengthening Bioinformatics Research and Instruction: In collaboration with the Department of Biology, we will bolster our capabilities in bioinformatics to support both teaching and research.
These initiatives are designed to enhance our research impact and educational offerings, positioning the Department at the forefront of these dynamic fields.
Research Areas of Concentration
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Bayesian Methods:
The Department of Statistics at Texas A&M University is nationally and internationally renowned for its outstanding research in numerous areas of Bayesian statistics, encompassing foundational theory, innovative methodological and algorithmic advancements, and impactful real-world applications. Key strength areas include Bayesian approaches to scientific machine learning, uncertainty quantification, hypothesis testing and model selection, semiparametric/nonparametric modeling and inference, measurement error modeling, causal learning, analysis of structured high-dimensional data, spatial statistics, time series analysis, networks and other dependent data, design and convergence analysis of Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC), and variational Bayesian (VB) algorithms and interplay between their statistical and algorithmic properties, decision theory, and quantum algorithms. Faculty collaborate broadly on applying Bayesian methods to cutting-edge applications with researchers across the College of Arts and Sciences, College of Engineering, Mays Business School, and College of Agriculture and Life Sciences.
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Measurement Errors
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Bioinformatics:
The department is dedicated to developing novel statistical, computational, and AI methodologies for large-scale and high-throughput data in the highly interdisciplinary field of bioinformatics. It houses the Center for Statistical Bioinformatics and administers the Bachelor of Science degree in Bioinformatics. The department maintains close partnerships and seamless collaborations with the Departments of Biology and Biomedical Engineering, as well as the Colleges of Agriculture & Life Sciences and Medicine. Faculty expertise spans single-cell (multi)omics, spatial transcriptomics, Mendelian randomization for causal inference, neuroimaging, microbiome, digital health, immunotherapy, and cancer genomics.
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Machine Learning, Artificial Intelligence:
The Department of Statistics at Texas A&M University advances the foundations and practice of modern machine learning and artificial intelligence through cutting-edge statistical research that emphasizes both methodological rigor and real-world impact. The department plays a leading role in the Texas A&M TRIPODS Research Institute for Foundations of Interdisciplinary Data Science (FIDS), an NSF-funded institute dedicated to advancing interdisciplinary research in data science. Our faculty develop principled approaches for causal learning, high-dimensional and structured data analysis, network learning, probabilistic and scalable Bayesian modeling, reinforcement learning, and uncertainty quantification. We further contribute advanced methodologies and theories in nonconvex and stochastic optimization, tensor and functional data analysis, variational inference, and other core areas of ML/AI, enhancing statistical validity and computational efficiency in complex learning problems. In parallel, faculty lead high-impact interdisciplinary collaborations that leverage and advance ML/AI techniques to tackle real-world scientific challenges. Applications span digital and precision health, ecological behavior modeling, environmental and spatiotemporal systems, genomics and single-cell omics, neuroimaging and brain connectomics, and social networks.
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Neuro Imaging:
The department advances interpretable, uncertainty-aware statistical neuroimaging across multiple imaging modalities, including structural MRI, functional MRI, diffusion MRI, and EEG/MEG. Development of statistical methods for image-on-image prediction, multilayer and time-varying brain-network models that link connectivity to cognition and disease, and spatial/space-time frameworks, is paired with scalable algorithms for consortia-scale data. By fusing images, networks, and behavior, the department delivers insights with calibrated uncertainty and reliable biomarkers for cognitive aging and neurological disorders, in collaboration with experts across neuroscience, psychology, and public health.
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Spatial Statistics:
The Department of Statistics at Texas A&M University has established a strong national and international reputation in spatial and spatio-temporal statistics. Faculty develop innovative theories, methodologies, and computational tools for addressing challenges in analyzing large and complex spatial and spatio-temporal data. Research strengths include scalable Gaussian process for massive datasets, hierarchical Bayesian modeling, spatial temporal models, spatial data fusion, multiresolution and nonstationary modeling, spatial extremes and rare event analysis, spatial clustering, spatial machine learning models, and spatial causal inference. Applications of these methods address pressing challenges in environmental and climate science, ecology and conservation, energy and natural resource management, epidemiology and public health, urban planning, and agricultural sciences. Emerging application research directions include statistical modeling for spatial transcriptomics and high-dimensional spatial omics data, bridging spatial methodology with modern biological and biomedical applications.
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Time Series, Network, and Dependent Data:
The department develops statistical methods for analyzing data with complex dependencies, including temporal patterns, network structures, and correlated observations. Faculty expertise in time series addresses questions about how systems evolve over time, detecting changes in dynamic processes, analyzing cyclical patterns and trends, and modeling data from wearable devices and monitoring systems. In network analysis, research focuses on understanding community structures in social and biological networks, learning relationships among interconnected variables, analyzing brain connectivity patterns, and modeling information flow in technological systems. The department also advances methods for high-dimensional data with dependencies, large-scale streaming data analysis, and understanding uncertainty in complex systems. Applications span neuroscience and brain imaging, digital health and continuous monitoring technologies, environmental systems, genomic regulatory networks, and social and technological networks.