Dr. Didong Li is an assistant professor in the Department of Biostatistics at UNC-Chapel Hill.
His research focus is statistical methods development for robust inference of complex and high-dimensional data, specifically covering manifold learning, nonparametric Bayesian inference, information geometry, and spatial statistics. He has applied these methods to electronic healthcare record data, large-scale genetic and survey data, and single-cell RNA-sequencing data.
Inaugural IMS Lawrence D. Brown PhD Student Award
2019, Institute of Mathematical Statistics
Geometric data analysis
JSM Invited Session, “Geometry & Bayes: Better Together” (2021)
JSM Invited Session, “Gaussian Process Models over non-Euclidean Domains” (2022)
Editorial Board: Journal of Machine Learning Research
Annals of Applied Statistics
Annals of Statistics
Conference on Neural Information Processing Systems (NeurIPS)
Electronic Journal of Statistics
IEEE Transactions on Neural Networks and Learning Systems
International Conference on Artificial Intelligence and Statistics (AISTATS)
Journal of the American Statistical Association
Journal of Computational and Graphical Statistics
Efficient manifold approximation with spherelets. D. Li, M. Mukhopadhyay and DB. Dunson (2022). Journal of Royal Statistical Society: Series B.
Classification via local manifold approximation. D. Li and DB. Dunson (2020). Biometrika.
Estimating densities with nonlinear support using Fisher-Gaussian kernels. M. Mukhopadhyay, D. Li and DB. Dunson (2020). Journal of Royal Statistical Society: Series B.