Jingyi (Ginny) Zheng, PhD
208A Extension Hall
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https://webhome.auburn.edu/~jzz0121/
Short Bio
Jingyi (Ginny) Zheng is an Associate Professor of Statistics and Data Science in the Department of Mathematics and Statistics at Auburn University. Her research focuses on statistical learning and machine learning, particularly for high-dimensional, structured, and complex data. She develops geometry-aware methods and computational tools with applications in biomedical imaging, healthcare, neuroscience, computer vision, and signal processing. Her federally supported research fosters interdisciplinary collaborations across medicine, engineering, agriculture, veterinary science, and other fields.
Education
PhD UC Davis 2019
Professional Experience
Associate Professor of Statistics and Data Science
Department of Mathematics and Statistics, Auburn University
2024âPresent
Assistant Professor of Statistics and Data Science
Department of Mathematics and Statistics, Auburn University
2019â2024
Innovation
Dr. Zheng develops statistical learning and machine learning methods for high-dimensional and structured data. Her research emphasizes geometry-aware machine learning, deep learning, and the analysis of biomedical images, brain connectivity, and spatiotemporal signals.
Her current work includes Bures-Wasserstein-based methods for analyzing covariance matrices, data generalization methods for functional MRI connectivity, and geometry-aware transformer models for fMRI analysis. She is expanding this research to incorporate additional geometric metrics for structured data. She also collaborates on interdisciplinary projects in cardiovascular health, neuroscience, healthcare outcomes, detection-canine research, agriculture, veterinary science, and computer vision. Her research has been supported by the National Institutes of Health, National Science Foundation, Department of Homeland Security, and Department of Veterans Affairs.
Engagement
Dr. Zheng is committed to expanding student participation in statistics and data science through research mentoring, interdisciplinary training, and experiential learning. She has mentored undergraduate and graduate students through Auburn’s Undergraduate Research Fellowship Program, National Science Foundation REU programs, and independent research projects. Student work under her mentorship has led to conference presentations, peer-reviewed publications, research awards, and continued graduate study.
She has also contributed to STEM outreach and the broader research community through activities such as the Alabama Science and Engineering Fair STEM Expo, the Auburn Research Student Symposium, and the organization of statistics and data science seminars. Through these efforts, she seeks to make modern statistical and computational methods accessible to students and to connect data science training with meaningful real-world research problems.
