Preprints

J. Chen, M. Zhou, W. Wu, J. Zhang, Y. Li, D. Li, STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics, arXiv:2406.06393, 2024.

J. Chen, C. Xiong, Q. Sun, G. Wang, G. Gupta, A. Halder, Y. Li, D. Li, Investigating spatial dynamics in spatial omics data with StarTrail, biorvix:, 2024.

J. Bryan, H. Zhou and D. Li, A compromise criterion for weighted least squares estimates, arXiv: 2404.00753, 2024.

Y. Park and D. Li, Lower Ricci curvature for efficient community detection, arXiv: 2401.10124, 2024.

W. Mu, E. Davis, K. Reed, D. Phanstiel, MI. Love, D. Li, Gaussian process for time series with lead-lag effects with applications to biology data, arXiv: 2401.07400, 2024.

B. Zhang. S. Nyquist, A. Jones, BE. Engelhardt, D. Li, Contrastive linear regression, arXiv: 2401.03106, 2024.

S. Hawke, H. Luo and D. Li, Contrastive inverse regression for dimension reduction, arXiv: 2305.12287, 2023.

D. Li, A. Jones, S. Banerjee and B. Engelhardt, Multi-group Gaussian Processes, arXiv:2110.08411, 2021. 

T. Wang, Y.Huang, and D. Li, From the Greene--Wu Convolution to Gradient Estimation over Riemannian Manifolds, arXiv:2108.07406, 2021. 

D. Li and S. Mukherjee, Random Lie brackets that induce torsion: a model for noisy vector fields, arXiv:2007.07309, 2020.

D. Li and DB. Dunson, Geodesic distance estimation with spherelets, arXiv:1907.00296, 2019. 

Selected publications

J. Bryan and D. Li, Comments on Contemporary Uses of Machine Learning for Electronic Health Records, North Carolina Medical Journal, 2024.

H. Luo, Jeremy Purvis and D. Li,  Spherical Rotation Dimension Reduction with Geometric Loss Functions, Journal of Machine Learning Research, 2024.

A. Jones*, D. Cai*, D. Li and BE. Engelhardt, Optimizing the design of spatial genomic studies, Nature Communication, 2024+.

D. Li*, A. Jones*, and B. Engelhardt, Probabilistic Contrastive Principal Component Analysis, Annals of Applied Statistics, 2024+. 

D. Li*, P. Nguyen*, Z. Zhang and DB. Dunson, Tree Representations of Brain Structural Connectivity via Persistent Homology, Frontiers in Neuroscience, 2023.

J. Chen, W. Mu, Y. Li and D. Li, On the Identifiability and Interpretability of Gaussian Process Models, NeurIPS, 2023.  

D. Li, W. Tang and S. Banerjee, Inference for Gaussian processes with Matérn covariogram on compact Riemannian manifolds , Journal of Machine Learning Research, 2023.

A. Jones, FW. Townes, D. Li and BE. Engelhardt, Alignment of spatial genomics and histology data using deep Gaussian processes, Nature Methods, 2023.

E. Chevalier, D. Li, Y. Lu, and DB. Dunson, Exponential-Wrapped Distributions on Symmetric Spaces, SIAM Journal on Mathematics of Data Science, 2022.

Pre-UNC

A. Badea*, D. Li*, A. Niculescu, R. Anderson, J. Stout, C. Williams, C. Colton, N. Maeda and DB. Dunson, Absolute Winding Number Differentiates Spatial Navigation Strategies with Genetic Risk for Alzheimer's Disease, Frontier in Neuroscience, 2022.

D. Li, M. Mukhopadhyay and DB. Dunson, Efficient manifold approximation with spherelets, Journal of Royal Statistical Society: Series B, 2022.

A. Jones, FW. Townes, D. Li and BE. Engelhardt, Contrastive latent variable modeling with application to case-control sequencing experiments , The Annals of Applied Statistics, 2022.

S. Cui, EC. Yoo, D. Li and BE. Engelhardt, Hierarchical Gaussian Processes and Mixtures of Experts in Predicting COVID Patient Trajectories, Pacific Symposium on Biocomputing (PSB), 2022. 

Y. Cao, D. Li, H. Sun, AH. Assadi and S. Zhang, Efficient Weingarten map and curvature estimation on manifolds, Machine Learning, 2021. 

D. Li and DB. Dunson, Classification via local manifold approximation, Biometrika, 2020. 

M. Mukhopadhyay*, D. Li* and DB. Dunson, Estimating Densities with Non-Linear Support by Using Fisher–Gaussian Kernels, Journal of Royal Statistical Society: Series B, 2020.

Advisee of DL

* Equally contributing authors.