In This Story
Through innovative course design, curricular leadership, and deep student mentorship, David Kepplinger has helped to reshape how students engage with statistics and data science at George Mason University and beyond. For this work, Kepplinger, a tenure-track assistant professor of statistics, received the College of Engineering and Computing (CEC) Dean’s Teaching Excellence Award.
“David’s teaching portfolio combines curricular leadership, technical innovation, and student-centered research training that has positioned him as a key contributor to the department’s modernization efforts in statistics and data science,” said Department Chair Jiayang Sun.
As data science, artificial intelligence, and computational methods rapidly transform the discipline, Kepplinger has played a central role in ensuring that the statistics curriculum keeps pace with contemporary practice. He was the few faculty members in 2025 with the skills, experiences, and motivation to immediately teach Introduction to Statistical Data Science, a newly developed course designed to establish a shared foundation in statistical principles, modern programming, and applied data science. The course prepares students for advanced coursework and applied projects. Kepplinger led the effortsrequired to launch the course, collaborating closely with colleagues in the Department of Computer Science.
At the doctoral level, Kepplinger also led a significant redesign of Computational Statistics, reimagining it as an advanced course aligned with modern research workflows. The revised curriculum emphasizes high-performance computing, AI-assisted research tools, and collaborative platforms. The course helps doctoral students connect theory with the kinds of tools and environments they will use in research and industry.
“In graduate education, David redefined what it means to prepare doctoral students for modern research,” said Lily Wang, associate dean for graduate studies for the Long Nguyen and Kimmy Duong School of Computing. “His course design enables students to integrate statistical thinking with cutting-edge computational tools in meaningful ways.”
Beyond individual courses, Kepplinger has focused on building scalable instructional infrastructure that enhances student learning while improving instructional efficiency. To support this goal, he developed the R package examinr, a web-based system that delivers and automatically grading labs and assignments. The tool provides students with immediate feedback and has been successfully integrated into Introduction to Exploratory Data Analysis.
Hands-on, real-world projects are also a key part of Kepplinger’s teaching. He co-organizes with Jonathan Auerbach, the annual International Cherry Blossom Prediction Competition. The competition brings together participants from around the world to predict peak bloom dates using environmental and biological data. By incorporating the competition into his courses, he gives students experience applying statistical models to real scientific questions, which has led to strong engagement and positive feedback.
Kepplinger’s impact is also reflected in the success of his students, particularly at the doctoral level. One of his PhD advisees recently received the Washington Statistical Society Outstanding Graduate Student Award and earned Student Paper Runner-Up recognition at the 2025 Statistical Methods in Imaging Conference. Both of Kepplinger’s current PhD candidates have published dissertation-based research in leading journals, including Technometrics and Statistica Sinica, prior to graduation—an uncommon level of early scholarly output in statistics. In addition to doctoral supervision, he advises a master’s project, mentors an undergraduate research student, and contributes to interdisciplinary advising across the college.
“David’s impact extends well beyond the number of students enrolled in his courses,” Sun said. “Through curricular leadership and collaboration, innovative instructional design, and highly effective mentorship, he has been in the front to advance the university’s teaching mission while preparing students for meaningful contributions in data science and statistical research.”