Nhat Ho

Nhat Ho

Hi!!! I am currently a postdoctoral fellow in the Electrical Engineering and Computer Science (EECS) Department where I am very fortunate to be mentored by Professor Michael Jordan and Professor Martin Wainwright. Before going to Berkeley, I finished my Phd degree in the Department of Statistics, University of Michigan, Ann Arbor where I am very fortunate to be advised by Professor Long Nguyen and Professor Ya'acov Ritov.

At this moment, I am interested in several directions from the interplay between computation and theory in machine learning and statistics. Some of these directions can be summarized as follows:

  • Unified frameworks for understanding singularity structures with statistical inference of popular estimators (e.g., MLE) in mixture and hierarhical models under the perspective of algebraic geometry, partial differential equation, and optimal transport theory.
  • Computational trade-offs between statistical efficiency and computational complexity/optimization in mixture and hierarchical models under singular and misspecified settings.
  • Efficient deep generative models for understanding and improving convolutional neural networks (CNN) and beyond.
  • Scalable models to study complex multi-level structures data based on the idea from optimal transport theory.
  • Statistical challenges of utilizing mixture and hierarchical models in principal stratification.
  • Unified framework for achieving the uniform confidence interval bounds in partial identification.
  • For a recent version of my CV, please email me via minhnhat@berkeley.edu.


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    Current collaborators: Long Nguyen, Ya'acov Ritov, Michael Jordan, Martin Wainwright, Bin Yu, Richard Baraniuk, Anima Anandkumar , Natesh Pillai , Avi Feller , Peng Ding , Ankit Patel, Will Fithian, Hung Bui, Dinh Phung , Mikhail Yurochkin , Viet Huynh , Aritra Guha , Chiao-Yu Yang , Tan Nguyen, Lihua Lei , Raaz Dwivedi , Koulik Khamaru, Joseph Borja, Wenlong Mou, Tianyi Lin, Banghua Zhu, Yuting Wei.

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