Department of Biostatistics and Health Data Science
A713 Public Health · 130 DeSoto Street · Pittsburgh, PA 15261
The Ding Lab is the research group of Dr. Ying Ding, Professor in the Department of Biostatistics and Health Data Science and Associate Dean for Graduate Academic Affairs at the University of Pittsburgh.
We develop rigorous statistical methodology for complex biomedical data — semiparametric methods for time-to-event data, subgroup and individualized-effect inference in precision medicine, and statistical and machine-learning approaches for high-dimensional genetic and multi-omics data. The methods we build are shaped by real scientific questions, developed hand in hand with our collaborators.
Copula and transformation models for interval-censored, left-truncated, and bivariate survival outcomes, and — more recently — neural and deep Cox models for dynamic prediction, together with the estimation theory and software behind them.
Recent work (Zeng) 2026 JASA · (Zeng) 2025 JRSS-C · (Liu) 2025 Statistics in Medicine · (Sun) 2023 Biometrics · (Sun) 2021 Biostatistics
Identifying subgroups with differential treatment efficacy and drawing logically coherent inference about them, and estimating heterogeneous and individualized treatment effects — increasingly from observational and electronic health record data.
Recent work (Bo) 2026 Lifetime Data Analysis · (Sui) 2025 Biostatistics · (Bo) 2025 Statistics in Medicine · (Wei) 2021 Statistics in Medicine · (Ding) 2018 Annals of Applied Statistics
Statistical and deep-learning methods for high-dimensional biological data: prediction from genome-wide association and longitudinal imaging data, model-based clustering of single-cell data, and the integration of multiple omics modalities.
Recent work (Zhang) 2025 AI Sensors · (Zhou) 2024 Briefings in Bioinformatics · (Yan) 2020 Nature Machine Intelligence · (Sun) 2020 Statistics in Medicine · (Wang) 2020 Nucleic Acids Research
Long-standing partnerships that supply the questions behind the methodology — age-related macular degeneration, Alzheimer's disease and related psychosis, pancreatitis, colon cancer, and pediatric asthma.
Recent work (Jiang) 2026 CNS Drugs · (Swaminathan) 2025 Pancreatology · (Chen) 2024 Journal of Clinical Oncology · (Liu) 2024 Journal of Statistical Research
Dr. Ying Ding is awarded a new NIH/NIGMS R35 grant, Advancing Deep Survival Methods for Prediction, Subgroup Identification, and Causal Inference (R35GM164304, 2026–2031).
We're hiring. The lab is recruiting a postdoctoral associate in statistical and machine-learning methodology. See the posting →
Dr. Ying Ding is named Statistician of the Year by the American Statistical Association, Pittsburgh Chapter.
Congratulations to Lang Zeng on completing his Ph.D., Rank-based Deep Learning for Survival Analysis.
Dr. Ying Ding is elected a Fellow of the American Statistical Association.
Congratulations to Zhiyu Sui on completing his Ph.D., Transfer Learning Approaches for Estimation and Evaluation of Individualized Treatment Decisions.
Congratulations to Na Bo on completing his Ph.D., New Methods for Analyzing Heterogeneous Treatment Effects in Survival Data.