Fangzheng Xie

Bayesian Methods and Mixture Models Statistical Methods and Inference Random Matrices and Applications Gaussian Processes and Bayesian Inference Statistical Methods and Bayesian Inference

Publications

Citations

188

Est. group size

—

Recurring co-author estimate

Active years

Publishing since 2017

Research summary

AI-generated

Fangzheng Xie develops statistical theory and methods for analyzing complex, high-dimensional data, with a strong focus on networks (graphs) and matrix-based models. Much of the work uses Bayesian approaches and spectral methods (techniques based on eigenvectors and eigenvalues) to estimate hidden structure and quantify uncertainty. Recent projects extend these ideas to clustering and to evaluating large language models.

Statistical inference for networks and graphsRandom matrix theory and spectral methodsBayesian modeling in high dimensionsEigenvector analysis and perturbation theoryGaussian process and mixture models

Publication activity has been fairly steady, averaging around five to six papers per year earlier in the decade and settling near three to four per year in recent years.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence

Publications per year over the last 10 years — averaging 3.2/year recently

Recent publications

Meta-Router: Bridging Gold-standard and Preference-based Evaluations in Large Language Model Routing
arXiv (Cornell University) · 2025
SANVI: A Fast Spectral-Assisted Network Variational Inference Method with an Extended Surrogate Likelihood Function
arXiv (Cornell University) · 2025
Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions
PubMed Central · 2025
Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions.
PubMed · 2025
Bias-Corrected Joint Spectral Embedding for Multilayer Networks With Invariant Subspace: Entrywise Eigenvector Perturbation and Inference
IEEE Transactions on Information Theory · 2024
Higher-Order Entrywise Eigenvectors Analysis of Low-Rank Random Matrices: Bias Correction, Edgeworth Expansion, and Bootstrap
arXiv (Cornell University) · 2024
Bias-Corrected Joint Spectral Embedding for Multilayer Networks with Invariant Subspace: Entrywise Eigenvector Perturbation and Inference
arXiv (Cornell University) · 2024
Spectral norm posterior contraction in Bayesian sparse spiked covariance matrix model
Electronic Journal of Statistics · 2024
Entrywise limit theorems for eigenvectors of signal-plus-noise matrix models with weak signals
Bernoulli · 2023
Euclidean Representation of Low-Rank Matrices and Its Geometric Properties
SIAM Journal on Matrix Analysis and Applications · 2023
An eigenvector-assisted estimation framework for signal-plus-noise matrix models
Biometrika · 2023
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration
SIAM/ASA Journal on Uncertainty Quantification · 2022
Bayesian Sparse Spiked Covariance Model with a Continuous Matrix Shrinkage Prior
Bayesian Analysis · 2022
Bayesian Sparse Gaussian Mixture Model in High Dimensions
arXiv (Cornell University) · 2022
Statistical inference of random graphs with a surrogate likelihood function
arXiv (Cornell University) · 2022

Publishes in

arXiv (Cornell University)×21
Figshare×4
Journal of the American Statistical Association×3
Bayesian Analysis×2
Biometrika×2

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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