Linxi Liu is an Assistant Professor of Statistics. She was a Term-Assistant Professor in the Department of Statistics at Columbia University from 2016 to 2020. Before that, She obtained her Ph.D. in Statistics from Stanford University in 2016.
I have taught the following courses at Pitt: STAT 1631/2630 Intermediate Probability, STAT 1632/2640 Intermediate Statistics, STAT 2131 Applied Statistical Methods, STAT 3691 Topics in Advanced Statistics I, STAT 2301 Statistical Computing and Intro to Data Science, STAT 1961 Data Science Capstone.
- Ph.D. in Statistics in 2016
Education & Training
Liu, L. and Ma, L. Spatial properties of Bayesian unsupervised trees. Proceedings of Thirty-Seventh Conference on Learning Theory (COLT), PMLR 247: 3556-3581, 2024.
Liu, L., Li, D. and Wong, W. H. Convergence rates of a class of multivariate density estimation methods based on adaptive partitioning. Journal of Machine Learning Research, 24(50): 1-64, 2023.
He, Z., Liu, L., Belloy, M. E., Le Guen, Y., Sossin, A., Liu, X., Qi, X., Ma, S., Wyss-Coray, T., Tang, H., Sabatti, C., Candes, E., Greicius, M. and Ionita-Laza, I. GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies. Nature Communications, 13(1): 7209, 2022.
Liu, L., Meng, Y., Wu, X., Ying, Z. and Zheng, T. Log-rank-type tests for equality of distributions in high-dimensional spaces. Journal of Computational and Graphical Statistics, 31(4): 1384-1396, 2022.
He, Z., Liu, L., Wang, C., Le Guen, Y., Lee, J., Gogarten, S., Lu, F., Montgomery, S., Tang, H., Silverman, E., Cho, M., Greicius, M. and Ionita-Laza, I. Identification of putative causal loci in whole-genome sequencing data via knockoff statistics. Nature Communications, 12(1): 3152, 2021.
- Nonparametric Statistics
- Multiple Hypothesis Testing
- Bayesian Statistics
- Statistical Machine Learning
- Statistical Genetics
