Scholar
Lingzhou Xue
Google Scholar ID: vfiEIqUAAAAJ
Professor of Statistics, The Pennsylvania State University
Citations & Impact
Citations
2,280
H-index
24
i10-index
47
Publications
20
Co-authors
61
list available
Contact
Publications
Resume (English only)
Academic Achievements
- 2025, Penn State Schreyer Honors College (SHC) Excellence in Advising Award
- 2024, Fellow of the Institute of Mathematical Statistics (IMS)
- 2024, Penn State Huck Institutes Leadership Fellow
- 2023, Fellow of the American Statistical Association (ASA)
- 2023, National Institute of Statistical Sciences (NISS) Distinguished Service Award
- 2021, The inaugural Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award
- 2020, Adobe’s Data Science Research Award
- 2019, International Consortium of Chinese Mathematicians (ICCM) Best Paper Award
- 2019, The inaugural Bernoulli Society (BS) New Researcher Award
- 2016, International Chinese Statistical Association International Conference Young Researcher Award
- 2016, Elected Member of the International Statistical Institute (ISI)
Education
- 2023 - present, Professor of Statistics, Eberly College of Science, Pennsylvania State University
- 2019 - 2023, Associate Professor of Statistics, Eberly College of Science, Pennsylvania State University
- 2014 - 2019, Assistant Professor of Statistics, Eberly College of Science, Pennsylvania State University
- 2012 - 2013, Postdoctoral Research Associate, Princeton University (Mentor: Professor Jianqing Fan)
- 2008 - 2012, Ph.D. in Statistics, University of Minnesota (Advisor: Professor Hui Zou)
- 2008 - 2011, M.Sc. in Statistics, University of Minnesota
- 2004 - 2008, B.Sc. in Statistics, Peking University
Background
- 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.
Miscellany
- He is a dedicated mentor to Ph.D. students and postdoctoral researchers, and five of his former advisees have become tenure-track faculty members in statistics. Please see both Statistical Learning and Data Mining (SLDM) Lab and Microbiome Data Science (MDS) Lab for more details.
