The PSTAT Department is delighted to welcome three outstanding new faculty members—Dr. Annie Qu, Dr. Kathryn Grace, and Dr. Tianyu Zhang—who will be joining us in the 2025–26 academic year.

Meet our new colleagues:

Dr. Annie Qu

Dr. Annie Qu is a highly distinguished statistician and data scientist whose research spans unstructured large-scale data analysis and the development of cutting-edge statistical methods in machine learning. Her work has been widely published across top-tier journals in statistics, machine learning, and medical sciences. Dr. Qu currently serves as a Co-Editor of the Journal of the American Statistical Association (Theory and Methods section) and has held associate editor roles for several leading journals. She has co-chaired the scientific program of the IMS International Conference on Statistics and Data Science from 2022 through 2025. Dr. Qu received her Ph.D. in Statistics from Penn State University and most recently served as a Chancellor's Professor at the University of California, Irvine. Google Scholar

Dr. Kathryn Grace

Dr. Kathryn Grace is a leading scholar in the interdisciplinary field of Population, Environment, and Global Environmental Change. Her expertise includes geographical and spatial data analysis, with a strong focus on environmental justice for women. Dr. Grace earned her Ph.D. in Geography and an M.A. in Statistics from UCSB. Her appointment is jointly held with the Department of Geography at UCSB through the Duncan and Suzanne Mellichamp Academic Initiative in Environmental Racial Justice. She has held visiting professorships at research institutes across France, Germany, Sweden, Austria, and Burkina Faso. Before joining UCSB, she was a Professor in the Department of Geography, Environment, and Society at the University of Minnesota, Twin Cities, and has been affiliated with UCSB's Broom Center for Demography. Google Scholar

Dr. Tianyu Zhang

Dr. Tianyu Zhang brings an interdisciplinary background in mathematics, life sciences, and physical sciences, with a research focus on statistical methodology. His work spans scalable statistical learning, genetic and transcriptomic data analysis, nonparametric modeling, model selection, polygenic risk scoring, health equity, and shape-constrained estimation—often applied to biomedical and astronomical research. He was awarded with the best student oral presentation in WMAR 2022. Dr. Zhang received his Ph.D. in Biostatistics from the University of Washington in 2022 and is currently a postdoctoral researcher in the Department of Statistics and Data Science at Carnegie Mellon University. Google Scholar

Please join us in warmly welcoming Annie, Kathryn, and Tianyu to UCSB!