Wen Wei LohAssistant Professor | QTM
Biography
Dr. Wen Wei Loh joined the Emory QTM team in Fall 2022. He comes to us via a post-doctoral fellowship at Ghent University in Belgium. Prior to joining Ghent University, he worked as a postdoctoral research fellow in Biostatistics at the University of North Carolina Chapel Hill. Dr. Loh received his PhD in Statistics from University of Washington and MA in Statistics from Harvard University.
Education
- Ph.D., Statistics, University of Washington, 2016
- M.A., Statistics, Harvard University, 2006
- B.Sc., Mathematics and Statistical Science, University College London, 2005
Research
Selected Publications
R code on GitHub
Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2020). Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown. Biometrics, Accepted.
R code on GitHub
Loh, W. W., Moerkerke B., Loeys T., Poppe L., Crombez G., and Vansteelandt S. (2020). Estimation of controlled direct effects in longitudinal mediation analyses with latent variables in randomised studies.
Multivariate Behavioral Research, 55(5), 763-785.
Loh, W. W., and Vansteelandt S. (2020). Confounder selection strategies targeting stable treatment effect estimators. Statistics In Medicine, Accepted.
R code on GitHub
Loh, W. W., Hudgens M.G., Clemens J.D., Ali M., and Emch, M.E. (2020). Randomization inference with general interference and censoring. Biometrics, 235-245.
R code on GitHub
Loh, W. W., Richardson, T. S., and Robins, J. M. (2017). An apparent paradox explained. Statistical Science, 32(3), 356-361.
Rigdon, J., Loh, W. W., and Hudgens, M. G. (2017). Response to comment on 'Randomization inference for treatment effects on a binary outcome'. Statistics in Medicine, 36(5), 876-880.
R package
Loh, W. W., and Richardson, T. S. (2015). A finite population likelihood ratio test of the sharp null hypothesis for compliers. In Thirty-First Conference on Uncertainty in Artificial Intelligence.
R package
Teaching
- QTM 210: Probability and Statistics
- QTM 530: Computing I