• 2780 Citations
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Personal profile

Research Interests

As a computer scientist and statistician, I use computation and data as a lens to explore science and intelligence. To make progress, I examines this with the point of view provided by the twin windows of modern nonparametric method and probabilistic graphical model. My specific research focuses on nonparametric structure learning and representation learning. Success on this research has the potential to revolutionarize the foundation of the second generation of artificial intelligence (i.e., statistical machine learning) and push the frontier of the third generation of artificial intelligence (i.e., deep learning). My applied research interest is to develop a unified set of computational, statistical, and software tools to extract and interpret significant information from the data collected from a variety of scientific areas.

Education/Academic qualification

Machine Learning and Statistics, PhD, Carnegie Mellon University

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  • 1 Similar Profiles
High-dimensional Mathematics
Graphical Models Mathematics
Parameter estimation Engineering & Materials Science
Principal component analysis Engineering & Materials Science
Estimator Mathematics
Lasso Mathematics
Discriminant analysis Engineering & Materials Science
Linear regression Engineering & Materials Science

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Grants 2017 2021

Learning systems
Deep neural networks
Graph in graph theory
Turing Machine
Maximum Degree
Big data
Parameter estimation
Learning systems
Big data
Distributed computer systems
Public health

Research Output 2004 2019

1 Citation (Scopus)

An extreme-value approach for testing the equality of large U-statistic based correlation matrices

Zhou, C., Han, F., Zhang, X. S. & Liu, H., May 2019, In : Bernoulli. 25, 2, p. 1472-1503 32 p.

Research output: Contribution to journalArticle

Correlation Matrix
Extreme Values

Blessing of massive scale: spatial graphical model estimation with a total cardinality constraint approach

Fang, E. X., Liu, H. & Wang, M., Jul 1 2019, In : Mathematical Programming. 176, 1-2, p. 175-205 31 p.

Research output: Contribution to journalArticle

Cardinality Constraints
Spatial Model
Graphical Models
Convex Geometry
1 Citation (Scopus)

Combinatorial inference for graphical models

Neykov, M., Lu, J. & Liu, H., Apr 2019, In : Annals of Statistics. 47, 2, p. 795-827 33 p.

Research output: Contribution to journalArticle

Graphical Models
Graph in graph theory
Lower bound
Graph Connectivity

Efficient, certifiably optimal clustering with applications to latent variable graphical models

Eisenach, C. & Liu, H., Jul 1 2019, In : Mathematical Programming. 176, 1-2, p. 137-173 37 p.

Research output: Contribution to journalArticle

Latent Variable Models
Semidefinite Programming
Graphical Models
Computational complexity
Semidefinite Programming Relaxation

Layer-wise learning strategy for nonparametric tensor product smoothing spline regression and graphical models

Tan, K. M., Lu, J., Zhang, T. & Liu, H., Aug 1 2019, In : Journal of Machine Learning Research. 20

Research output: Contribution to journalArticle

Tensor Product Splines
Smoothing Splines
Multivariate Functions
Learning Strategies
Graphical Models