Congratulations to University of Washington Biostatistics faculty member Ting Ye on her promotion to associate professor with tenure, effective July 1, 2026.
Ye joined the department as an assistant professor in 2021, after a PhD in statistics at the University of Wisconsin–Madison and a postdoctoral fellowship in the Department of Statistics at the Wharton School.
Ye develops statistical methods for clinical trials and causal inference, and most recently multimodal AI for biomedical science. At her TREND Lab, she pairs AI with causal inference to find what changes outcomes, not what merely predicts them, and tailors AI advances to clinical development, accelerating timelines and improving success rates without relaxing rigor. As evidence generation multiplies in the AI era, she is also building the statistical infrastructure that next-generation trials will require, including platform trials and other complex innovative designs.
Ye established the general theory for using baseline covariates to improve efficiency in randomized trials across the full range of endpoint types. Two of her first-author papers are cited in the FDA's final guidance on covariate adjustment, and her methods have been used in the primary analyses of pivotal trials supporting recent approvals. Committed to bridging theory and practice, she led development of the RobinCar R packages, downloaded more than 20,000 times, and co-founded and co-chairs the ASA Biopharmaceutical Section Scientific Working Group on Covariate Adjustment.
"Trials are the gold standard, but we can't run one for every question," Ye said. "Mendelian randomization lets us borrow randomization from nature; my work is about making it hold up when the genetic variants are weak or invalid, which is what we actually meet in practice." Her methods are now widely used in genetic epidemiology studies.
Ye’s research program is supported by an NIH R35 MIRA grant, a PCORI award, a Cystic Fibrosis Foundation award, and a research award from Eli Lilly and Company. She also leads the UW sub-award for the HPTN Statistical and Data Management Center.
