Dat Do

Ph.D. Candidate Department of Statistics The University of Michigan at Ann Arbor Email: dodat@umich.edu Office: 437 West Hall, 1085 University Ave, Ann Arbor, MI 48109

Brief Biography

I am currently a Ph.D. candidate in Statistics at the University of Michigan, where I am fortunate to be advised by Professor Jonathan Terhorst and Professor Long Nguyen. Before coming to the University of Michigan, I graduated as the valedictorian of Hanoi University of Science, class of 2019.

Here is a recent version of my resume: CV

Education

• September 2019 - Present: University of Michigan

• September 2015 - June 2019: Hanoi University of Science, Vietnam National University

Research interests

A central theme of my research focuses on:

• Hierarchical Models: Identifiability, Statistical Efficiency, and Model Selection Methods

• Population genetics and Phylogenetics

• Statistical Optimal Transport

Preprints and Publications

• On the moment tensor of Dirichlet distributions and the posterior contraction of the Latent Dirichlet Allocation. In preparation.
  Dat Do*, Sunrit Chakraborty*, Jonathan Terhorst, XuanLong Nguyen

• Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models. Under review.
  Dat Do*, Linh Do*, Scott McKinley, Jonathan Terhorst, XuanLong Nguyen

• Functional optimal transport: map estimation and domain adaptation for functional data . Journal of Machine Learning Research (JMLR) 2024.
  Jiacheng Zhu*, Aritra Guha*, Dat Do*, Mengdi Xu, XuanLong Nguyen, Ding Zhao. 

• Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models. NeurIPS 2023.
  Dat Do*, Huy Nguyen*, Khai Nguyen, Nhat Ho

• Strong identifiability and parameter learning in regression with heterogeneous response. Under review.
  Dat Do*, Linh Do*, XuanLong Nguyen 

• Beyond Black Box Densities: Parameter Learning for the Deviated Components. NeurIPS 2022 .
  Dat Do*, Nhat Ho*, XuanLong Nguyen 

• Generalized Marcinkiewicz Laws for Weighted Dependent Random Vectors in Hilbert Spaces. Theory of probability and its applications, 2021.
  Ta Cong Son, Le Van Dung, Dat Do, Ta Thi Trang.

• On Label Shift in Domain Adaptation via Wasserstein Distance . Under review .
  Trung Le, Dat Do, Tuan Nguyen, Huy Nguyen, Hung Bui, Nhat Ho, Dinh Phung. 

• Entropic Gromov-Wasserstein between Gaussian Distributions . In International Conference on Machine Learning, pages 12164–12203. PMLR, 2022 .
  Khang Le, Dung Le, Huy Nguyen, Dat Do, Tung Pham, Nhat Ho.