罗翔宇 职称：助理教授、博士生导师（统计与大数据研究院）
研究方向：贝叶斯统计，非参数贝叶斯，统计基因组学，生物信息学，统计计算
联系方式：xiangyuluo@ruc.edu.cn


个人简历罗翔宇2018年9月起任中国人民大学统计与大数据研究院助理教授。他2018年博士毕业于香港中文大学统计系，导师为魏颖颖教授。2014年本科毕业于中国科学技术大学。
Xiangyu Luo is an assistant professor at the Institute of Statistics and Big Data, Renmin University of China, starting from Sep. 2018. He obtained his Ph.D. degree from Department of Statistics, The Chinese University of Hong Kong in 2018 and was supervised by Prof. Yingying Wei. Prior to that, Xiangyu Luo graduated from Department of Statistics and Finance, University of Science and Technology of China with a Bachelor of Science degree in 2014.
Xiangyu Luo has broad research interests in Bayesian statistics, nonparametric Bayes, statistical genomics, bioinformatics, statistical computing and statistical learning. He likes involving in developing novel statistical models to solve practical biological problems. His specific research fields include reconstructing biological regulatory/coactivation networks via statistical graphical models, correcting batch effects in high-throughput genomic data, deconvoluting methylation signals in epigenome-wide association studies, and discovering subject heterogeneity at the single-cell resolution.


Selected Publications:


1. Xiangyu Luo, and Yingying Wei* (2018) Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks. The Annals of Applied Statistics, Vol. 12, No. 3, 1749-1772. (Paper, parallel C code)
2. Xiangyu Luo, and Yingying Wei* (2019) Batch effects correction with unknown subtypes. Journal of the American Statistical Association, 114:526, 581-594. (Paper, Software: Bioconductor R package BUScorrect)

3. Xiangyu Luo, Can Yang*, and Yingying Wei* (2019) Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies. Nature Communications, Volume 10, Article number: 3113. (Paper, Software: Bioconductor R package HIREewas)


Preprint:


4. Qiuyu Wu#, Xiangyu Luo*. A nonparametric Bayesian approach to simultaneous subject and cell heterogeneity discovery for single cell RNA-seq data. (Paper on arXiv, Software: R package SCSCwin for Windows users; R package SCSClinux for Linux/Mac OS users.)
(* indicates corresponding author; # represents supervised student)