Biography
Dr. Qiuyi Wu is an Assistant Professor in the Department of Epidemiology and Biostatistics at Barnett College of Public Health. She is a biostatistician and statistical data scientist whose research focuses on developing rigorous, interpretable, and computationally efficient statistical and machine learning methods for complex biomedical and public health data. Her research interests span statistical learning and inference, functional and spatial data analysis, clustering, high-dimensional modeling, multi-omics integration, image processing, and biomedical data science.
A central theme of Dr. Wu’s research is developing statistical methodology for uncovering complex and latent structures in high-dimensional data while providing principled uncertainty quantification. Her recent work develops a likelihood-based framework for probabilistic soft clustering that connects fuzzy clustering with statistical inference. This framework enables uncertainty-aware and interpretable characterization of heterogeneous populations and continuous latent structures, with applications to biomedical data such as neuroimaging, electronic health records, and single-cell data. Dr. Wu’s methodological research has also encompassed functional data analysis and biomedical image processing. During her doctoral training, she developed adaptive kernel smoothing and partial differential equation–based approaches for representing and analyzing complex functional and imaging data. Her work combines mathematical modeling, statistical theory, and computational methods to improve the efficiency, stability, and interpretability of high-dimensional data analysis.
Complementing her theoretical and methodological work, Dr. Wu has extensive experience in interdisciplinary biomedical research. At Duke University, she developed spatial statistical approaches for integrating metabolomic and proteomic data from the aging primate brain, with the goal of uncovering spatial molecular organization and biological patterns associated with aging and neurodegeneration. She has also collaborated on research investigating the effects of anesthesia and surgery on postoperative brain waste clearance, sleep, and cognitive recovery. Dr. Wu’s broader research experience spans environmental health, statistical climatology, text mining, natural language processing, and recommender systems. At Argonne National Laboratory, she worked with large-scale climate model outputs to characterize wind patterns and quantify uncertainty under future climate scenarios. Her earlier work in text mining investigated statistical approaches to document representation, topic discovery, and pattern recognition, while her work at the Statistical and Applied Mathematical Sciences Institute explored recommender systems and data-driven decision making. These experiences contribute to a research program that bridges foundational statistical methodology, modern machine learning, and interdisciplinary applications. Dr. Wu also has substantial experience in collaborative and consulting biostatistics. At the University of Rochester, she contributed to the NIH Environmental Influences on Child Health Outcomes (ECHO) program, supporting study design, statistical analysis, and manuscript development for research on prenatal and early-life environmental exposures, stress biology, fetal growth, and child health. Across her collaborative work, she has applied and developed statistical methods for complex, high-dimensional, longitudinal, spatial, and multi-modal biomedical data.
Dr. Wu received her PhD in Statistics from the University of Rochester and her MS in Applied Statistics from Rochester Institute of Technology. Prior to joining Temple University, she was a postdoctoral researcher in the Department of Biostatistics and Bioinformatics at Duke University.
Beyond research and teaching, Dr. Wu is actively engaged in professional service, mentoring, and community outreach. She serves as an officer and webmaster for the American Statistical Association’s Section on Text Analysis and has contributed to professional organizations through conference organization, journal peer review, student mentoring, and statistical outreach. She also serves as a Senior Advisor to AI4Purpose and AI4PurposeAdvisory Inc., supporting interdisciplinary initiatives in artificial intelligence and data science. Through her research, collaboration, mentoring, and service, Dr. Wu aims to advance statistical methodology while translating methodological innovation into meaningful biomedical and public health impact.
Personal website: drqiuyiwu.github.io