Tong Tong Wu Lab
Welcome to the Wu Lab
As a biostatistician, my research has two major strands: the development of novel statistical methods and collaborative research in medicine and public health. My methodological research has focused on high-dimensional data analysis, in conjunction with other areas such as empirical likelihood, machine learning, longitudinal data analysis and survival analysis. Some of this work was motivated by and applied to computational biology problems. I have developed new statistical methods and computational algorithms for high- and ultra-high-dimensional data. Besides my methodological research, I have developed applications with biomedical and public health significance.
Tongtong Wu, Ph.D.
Principal Investigator
Publications
- Leon, S.** and Wu, T.T.$ (2024) Comparison of Longitudinal Trajectories Using a High-dimensional Partial Linear Semiparametric Mixed-Effects Model. Journal of the American Statistical Association, accepted.
- Yang, L.** and Wu, T.T.$ (2023) Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization. Biometrics, Volume 79(2), 761-774.
- Yang, L.**, Young, D.R.$, and Wu, T.T.$ (2022) Clustering of Longitudinal Physical Activity Trajectories Among Young Females with Selection of Associated Factors. PLOS ONE, 17(5):e0268376.
- Leon, S.**, Ren, J.**, Choe, R., and Wu, T.T. (2022) Semiparametric Mixed-Effects Model for Analysis of Non-invasive Longitudinal Hemodynamic Responses During Bone Graft Healing. PLOS ONE, 17(4):e0265471.
- Chang, J., Chen, S., Tang, C.Y., and Wu, T.T. (2021) (alphabetic order) High-Dimensional Empirical Likelihood Inference. Biometrika, Volume 108(1), 127-147.
- Chang, J., Tang, C.Y., and Wu, T.T. (2018) A New Scope of Penalized Empirical Likelihood with High-Dimensional Estimating Equations. Annals of Statistics, Volume 46, 3185--3216.
Affiliations
Contact Us
Wu Lab
?Dept of Biostatistics and Computational Biology
Saunders Research Building 4141
265 Crittenden Boulevard, CU 420630
Rochester, New York 14642-0630
(585) 273-1031