Mark Carpenter, PhD, College of Sciences and Mathematics Faculty and Staff

Short Bio

D. Mark Carpenter, Ph.D., is a Professor of Statistics and Data Science at Auburn University with more than 30 years of experience in statistical research, teaching, consulting, and interdisciplinary scientific collaboration. His work spans statistical modeling, medical research,biostatistics, machine learning, decision science, and the development of interpretable and reliable AI methods for complex real-world problems.

Dr. Carpenter’s applied research has crossed a broad range of disciplines. Earlier in his career, he worked extensively in medical and healthcare research, serving as a Senior Biostatistician and Associate Director of Medical Statistics. In those roles, he collaborated with physicians, scientists, and healthcare organizations on the design and analysis of clinical and medical studies and on the use of statistical methods to address healthcare and population-level problems.

His subsequent research has included substantial work in aerospace, defense, and national security. For more than 15 years, he has applied statistical and machine-learning methods to problems involving propulsion systems, trajectory and object classification, uncertainty quantification, model validation, and other data-intensive aerospace and defense applications. He has also worked on problems in manufacturing, fraud detection, disease management, and commercial analytics.

Across these areas, Dr. Carpenter has participated as principal investigator, co-principal investigator, investigator, biostatistician, or consultant on more than $3 million in externally funded research. His career reflects a consistent emphasis on using rigorous statistical reasoning to solve interdisciplinary problems rather than applying methodology within a single application domain.

His current research interests include statistical learning, multivariate and functional data analysis, classification, simulation, uncertainty quantification, explainable and responsible AI, and statistical decision-making. A central theme of his recent work is the integration of statistical principles with machine learning and artificial intelligence to produce models that are not only predictive, but also interpretable, validated, and useful for decision-making.

Dr. Carpenter teaches graduate and undergraduate courses in statistics and data science and has mentored students working across statistics, machine learning, artificial intelligence, engineering, and interdisciplinary applications. His teaching emphasizes statistical reasoning, understanding uncertainty, rigorous validation, and the use of quantitative evidence to support defensible decisions.