Didong Li

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David DunsonArts & Sciences Professor of Statistical Science & MathematicsAdresse e-mail validée de duke.edu
Barbara EngelhardtSenior Investigator, Gladstone Institutes and Professor, Stanford UniversityAdresse e-mail validée de stanford.edu
Minerva MukhopadhyayIndian Institute of Technology KanpurAdresse e-mail validée de iitk.ac.in
F. William TownesAssistant Professor of Statistics and Data Science, Carnegie Mellon UniversityAdresse e-mail validée de andrew.cmu.edu
SUDIPTO BANERJEEUniversity of California Los AngelesAdresse e-mail validée de ucla.edu
Amir H AssadiUniversity of Wisconsin MadisonAdresse e-mail validée de wisc.edu
Yueqi CaoImperial College LondonAdresse e-mail validée de imperial.ac.uk
Emmanuel ChevallierAix-Marseille UniversityAdresse e-mail validée de univ-amu.fr
Alexandra BadeaDuke UniversityAdresse e-mail validée de duke.edu
Christina L. WilliamsDuke UniversityAdresse e-mail validée de psych.duke.edu
Diana CaiCenter for Computational Mathematics, Flatiron InstituteAdresse e-mail validée de flatironinstitute.org
Wenpin TangAssistant Professor, Columbia UniversityAdresse e-mail validée de columbia.edu
Aishwarya MandyamPhD Student, Stanford UniversityAdresse e-mail validée de stanford.edu
Zhengwu ZhangUniversity of North Carolina at Chapel HillAdresse e-mail validée de email.unc.edu
Sayan MukherjeeDuke Universiy, University of Leipzig, Max Planck Institute for Mathematics in the SciencesAdresse e-mail validée de mis.mpg.de
Yulong LuAssistant Professor at University of Minnesota Twin Cities

Suivre

Didong Li

Assistant Professor, Department of Biostatistics, Gillings School of Global Public Health, UNC

Adresse e-mail validée de unc.edu - Page d'accueil

Manifold learning geometric data analysis nonparametric Bayes Gaussian processes spatial statistics


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
2D Dubins path in environments with obstacle D Yang, D Li, H Sun Mathematical Problems in Engineering 2013, 2013	31	2013
Efficient manifold approximation with spherelets D Li, M Mukhopadhyay, DB Dunson Journal of the Royal Statistical Society Series B 84 (4), 1129-1149, 2022	26	2022
Geodesic distance estimation with spherelets D Li, DB Dunson arXiv preprint arXiv:1907.00296, 2019	24	2019
Estimating densities with nonlinear support using Fisher-Gaussian kernels M Mukhopadhyay, D Li, DB Dunson Journal of Royal Statistical Society: Series B 82 (5), 1249-1271, 2020	15	2020
Probabilistic contrastive principal component analysis D Li, A Jones, B Engelhardt arXiv preprint arXiv:2012.07977, 2020	14	2020
Contrastive latent variable modeling with application to case-control sequencing experiments A Jones, FW Townes, D Li, BE Engelhardt The Annals of Applied Statistics 16 (3), 1268-1291, 2022	10	2022
Classification via local manifold approximation D Li, DB Dunson Biometrika 107 (4), 1013-1020, 2020	10	2020
Alignment of spatial genomics data using deep Gaussian processes A Jones, FW Townes, D Li, BE Engelhardt Nature Methods, 1-9, 2023	9*	2023
Efficient Weingarten map and curvature estimation on manifolds Y Cao, D Li, H Sun, AH Assadi, S Zhang Machine Learning 110 (6), 1319-1344, 2021	7*	2021
Exponential-wrapped distributions on symmetric spaces E Chevallier, D Li, Y Lu, D Dunson SIAM Journal on Mathematics of Data Science 4 (4), 1347-1368, 2022	5*	2022
Multi-group Gaussian processes D Li, A Jones, S Banerjee, BE Engelhardt arXiv preprint arXiv:2110.08411, 2021	5	2021
A statistical cohomogeneity one metric on the upper plane with constant negative curvature L Cao, D Li, E Zhang, Z Zhang, H Sun Advances in Mathematical Physics 2014, 2014	5	2014
Inference for Gaussian processes with Matérn covariogram on compact Riemannian manifolds D Li, W Tang, S Banerjee Journal of Machine Learning Research 24 (101), 1-26, 2023	4	2023
Hierarchical Gaussian processes and mixtures of experts to model Covid-19 patient trajectories S Cui, EC Yoo, D Li, K Laudanski, BE Engelhardt PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022, 266-277, 2021	4	2021
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering D Paul, S Chakraborty, D Li, D Dunson arXiv preprint arXiv:2008.07110, 2020	4	2020
From the Greene--Wu Convolution to Gradient Estimation over Riemannian Manifolds T Wang, Y Huang, D Li arXiv preprint arXiv:2108.07406, 2021	3	2021
A geodesic-based Riemannian gradient approach to averaging on the Lorentz group J Wang, H Sun, D Li Entropy 19 (12), 698, 2017	3	2017
Spherical rotation dimension reduction with geometric loss functions H Luo, JE Purvis, D Li arXiv preprint arXiv:2204.10975, 2022	2	2022
Absolute Winding Number Differentiates Mouse Spatial Navigation Strategies With Genetic Risk for Alzheimer’s Disease A Badea, D Li, AR Niculescu, RJ Anderson, JA Stout, CL Williams, ... Frontiers in neuroscience 16, 848654, 2022	1*	2022
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation A Mandyam, D Li, D Cai, A Jones, B Engelhardt Fourth Symposium on Advances in Approximate Bayesian Inference, 2022	1	2022

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