Linxi Liu is an Associate 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.
She has 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
