Resume
Academic Achievements
- Online-forgetting process for debiased-Lasso using summary statistics, Statistica Sinica, 2025+
- Limit results for estimation of connectivity matrix in multi-layer stochastic block models, Journal of Statistical Planning and Inference, 2026
- Stochastic block model-aware topological neural networks for graph link prediction, Transactions on Machine Learning Research, 2025
- Privacy-preserving communication-efficient spectral clustering for distributed multiple networks, Computational Statistics and Data Analysis, 2025
- Federated Bayesian network learning from multi-site data, Journal of Biomedical Informatics, 2025
- Spectral co-clustering in multi-layer directed networks, Computational Statistics and Data Analysis, 2024
- Fedpower: privacy-preserving distributed eigenspace estimation, Machine Learning, 2024
- On the efficacy of higher-order spectral clustering under weighted stochastic block models, Computational Statistics and Data Analysis, 2024
- Randomized spectral co-clustering for large-scale directed networks, Journal of Machine Learning Research, 2023
- Randomized spectral clustering in large-scale stochastic block models, Journal of Computational and Graphical Statistics, 2022
- Sparse directed acyclic graphs incorporating the covariates, Statistical Papers, 2020
- Structure learning of sparse directed acyclic graphs incorporating the scale-free property, Computational Statistics, 2019
Research Experience
- Conducted multiple academic visits during doctoral studies and published numerous papers in various fields.
Education
- Received Ph.D. in Statistics in December 2019 from the School of Mathematics at Northwest University. During Ph.D. and Master’s studies, spent a year as a visiting student in the Department of Statistics at Columbia University and three months as a visiting student in the Department of Statistics at University of Wisconsin-Madison.
Background
- Currently a Lecturer at the School of Mathematics, Northwest University, China. Research interests include network analysis, privacy-preserving data analysis, federated learning, and graphical models.
Miscellany
- Teaches high dimensional statistics and data visualization; developed RandClust and Rclust R packages for randomized spectral clustering of large-scale networks.