Xiaoran Jiang
Xiaoran Jiang
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Post Doc, Department of Statistics
Post Doc, Department of Statistics
George Mason University
George Mason University
About me
I am a postdoctoral researcher at George Mason University. My research focuses on developing advanced machine learning algorithms, optimizing statistical models, and applying them to real-world datasets. My research interests include:
Inference for branching processes in random environments, with applications to biological problems.
Compressed regression using sketching matrices and random sampling techniques.
Exploring non-parametric methods for local correlation curve estimation.
I am also interested in topics related to deep learning, adversarial robustness, and large language models.
News and Updates
01/25: Our paper, Ancestral Inference and Learning for Branching Processes in Random Environments, is now available on arxiv.
08/24: I gave a contributed talk at JSM 2024 on Ancestral Inference of Branching Processes in Random Environments.
07/24: I delivered an invited talk at ICORS meets DSSV 2024 on Compressed Regression.
06/24: I presented on Branching Processes in Random Environments at WNAR 2024.
03/24: I gave a talk on Ancestral Inference of PCR using Branching Processes at George’s STAT DAY 2024.
Education
George Mason University - PhD in Statistical Science, 2025
George Washington University - Master of Science in Statistics, 2018
Wuhan University - Bachelor of Arts in Statistics, 2016
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