Grants per year

## 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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## Grants 2017 2020

## Collaborative Research: TRIPODS Institute for Optimization and Learning

1/1/18 → 12/31/20

Project: Research project

## CAREER: An Integrated Inferential Framework for Big Data Research and Education

9/1/17 → 6/30/20

Project: Research project

## RI: Medium: Collaborative Research: Next-Generation Statistical Optimization Methods for Big Data Computing

9/1/17 → 12/31/19

Project: Research project

## BIGDATA: Collaborative Research: F: Stochastic Approximation for Subspace and Multiview Representation Learning

9/1/17 → 8/31/20

Project: Research project

## Research Output 2004 2019

## 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 1 2019, In : Bernoulli. 25, 2, p. 1472-1503 32 p.Research output: Contribution to journal › Article

## Combinatorial inference for graphical models

Neykov, M., Lu, J. & Liu, H., Apr 1 2019, In : Annals of Statistics. 47, 2, p. 795-827 33 p.Research output: Contribution to journal › Article

## 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 journal › Article

## Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization

Li, X., Lu, J., Arora, R., Haupt, J., Liu, H., Wang, Z. & Zhao, T., Jun 1 2019, In : IEEE Transactions on Information Theory. 65, 6, p. 3489-3514 26 p., 8675509.Research output: Contribution to journal › Article

## A convex formulation for high-dimensional sparse sliced inverse regression

Tan, K. M., Wang, Z., Zhang, T., Liu, H. & Cook, R. D., Dec 1 2018, In : Biometrika. 105, 4, p. 769-782 14 p.Research output: Contribution to journal › Article