Lingzhou Xue

About

Lingzhou Xue is a Professor of Statistics at Penn State. He received his B.Sc. in Statistics from Peking University in 2008 and his Ph.D. in Statistics from the University of Minnesota in 2012. He was a postdoctoral research associate at Princeton University from 2012-2013. His research interests include high-dimensional statistics, nonparametric statistics, statistical and machine learning, large-scale optimization, and statistical modeling in biomedical, environmental, and social sciences. His recent research focuses on causal inference, federated learning, graphical models, high-dimensional inference, optimal transport, random objects, and reinforcement learning. He is a dedicated mentor to Ph.D. students and postdoctoral researchers, with five of his former advisees becoming tenure-track faculty members in statistics.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Statistics
• Biology
• Algorithm
• Geography
• Waste management
• Cartography
• Computational biology

Selected publications

• Compositional knockoff filter for high‐dimensional regression analysis of microbiome data

  Biometrics · 2020 · 33 citations

  A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine-mapping of the microbiome, we propose a two-step compositional knockoff filter to provide the effective finite-sample false discovery rate (FDR) control in high-dimensional linear log-contrast regression analysis of microbiome compositional data. In the first…

  DOI

• An Alternating Manifold Proximal Gradient Method for Sparse Principal Component Analysis and Sparse Canonical Correlation Analysis

  INFORMS Journal on Optimization · 2020 · 28 citations

  Sparse principal component analysis and sparse canonical correlation analysis are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data. Both problems can be formulated as an optimization problem with nonsmooth objective and nonconvex constraints. Because nonsmoothness and nonconvexity bring numerical difficulties, most algorithms suggested in the literature either solve some relaxations of them or are heuristic and lack convergence guarant…

  DOI

• Assessing Contamination of Stream Networks near Shale Gas Development Using a New Geospatial Tool

  Environmental Science & Technology · 2020 · 17 citations

  Chemical spills in streams can impact ecosystem or human health. Typically, the public learns of spills from reports from industry, media, or government rather than monitoring data. For example, ∼1300 spills (76 ≥ 400 gallons or ∼1500 L) were reported from 2007 to 2014 by the regulator for natural gas wellpads in the Marcellus shale region of Pennsylvania (U.S.), a region of extensive drilling and hydraulic fracturing. Only one such incident of stream contamination in Pennsylvania has been docum…

  DOI

• spVelo: RNA velocity inference for multi-batch spatial transcriptomics data

  Genome biology · 2025-08-11 · 4 citations

  articleOpen access

  RNA velocity has emerged as a powerful tool to interpret transcriptional dynamics and infer trajectory from snapshot datasets. However, current methods fail to utilize the spatial information inherent in spatial transcriptomics and lack scalability in multi-batch datasets. Here, we introduce spVelo, a scalable framework for RNA velocity inference of multi-batch spatial transcriptomics data. spVelo supports several downstream applications, including uncertainty quantification, complex trajectory…

  PublisherOA PDFDOI

• Composition-on-composition regression analysis for multi-omics integration of metagenomic data

  Bioinformatics · 2025-07-01 · 1 citations

  articleOpen access

  MOTIVATION: Compositional data are frequently encountered in many disciplines, such as in next-generation sequencing experiments widely used in biomedical studies. Regression analysis with compositional data as either responses or predictors has been well studied. However, when both responses and predictors are compositional, the inventory of analysis tools is surprisingly limited, especially in the high-dimensional setting. Among the few existing methods, most of them rely on a log-ratio transf…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: New Methods, Theory and Applications for Nonsmooth Manifold-Based Learning

  NSF · $200k · 2020–2024

• Collaborative Research: New Statistical Methods and Theory for High-Dimensional Data

  NSF · $126k · 2015–2018

• Innovated Statistical Inference for Complex and High-Dimensional Data

  NSF · $195k · 2018–2023

Frequent coauthors

• Hui Zou

  Yangzhou University

  38 shared

• Jianqing Fan

  22 shared

• Shiqian Ma

  Rice University

  15 shared

• Amal Agarwal

  13 shared

• Xiufan Yu

  13 shared

• Danning Li

  12 shared

• Kevin H. Lee

  Western Michigan University

  11 shared

• Bingyuan Liu

  Pennsylvania State University

  11 shared

Labs

• Department of StatisticsPI

Education

• Ph.D., Statistics

  University of Minnesota

  2012

Awards & honors

• Penn State Schreyer Honors College (SHC) Excellence in Advis…
• Institute of Mathematical Statistics (IMS) Fellow, 2024
• Penn State Huck Institutes Leadership Fellow, 2024
• American Statistical Association (ASA) Fellow, 2023
• National Institute of Statistical Sciences (NISS) Distinguis…