Welcome to Qing Zhou's Home Page
UCLA Department of Statistics
8125 Math Sciences Bldg, Box 951554
Los Angeles, CA 90095
Professor of Statistics, UCLA
Ph.D. in Statistics, Harvard University, 2006
We develop statistical learning methods and theory in the context of big data. In particular, we are interested in structure learning of Bayesian networks from large-scale and high-dimensional data. Bayesian networks are a class of popular graphical models widely used for modeling conditional independence structures in a joint distribution and causal relations among a set of variables. The structure of a Bayesian network is represented by a directed acyclic graph (DAG). We have developed penalized likelihood methods and software packages for structure learning of DAGs from both experimental and observational data. We have also introduced data-driven concave regularization into unsupervised learning.
We are interested in uncertainty quantification for regularized sparse estimators. We have developed the technique of estimator augmentation to characterize the sampling distribution of a lasso-type estimator, which allows us to integrate Markov chain Monte Carlo and importance sampling into statistical inference for high-dimensional models.
Monte Carlo methods:
We develop Monte Carlo methods to estimate statistical and topological structures
of a probability distribution, with applications in Bayesian inference and statistical physics.
We are interested in exploring and reconstructing energy landscapes
by estimating the density of states, the tree of sublevel sets, and the domain of attraction.
We develop statistical methodologies
for efficient analysis of large-scale high-throughput genomic data.
We employ model-based and sparse regularization methods to
make statistical inference on these data. Our goal is to understand
gene regulation and decode regulatory
circuits by integrating gene expression data, protein binding data,
chromatin interaction data, and DNA sequence data.
We have constructed gene regulatory networks and identified combinatorial binding patterns in mouse embryonic stem cells. In addition, we also have
biological applications in alternative splicing and complex diseases via collaborations with experimental groups.
Zhou, Q. and Min, S. (2017). Uncertainty quantification under group sparsity.
Biometrika, 104: 613-632. [Reprint]
Aragam, B. and Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks.
Journal of Machine Learning Research, 16: 2273-2328. [Reprint]
Zhou, Q. (2014). Monte Carlo simulation for lasso-type problems by estimator augmentation.
Journal of the American Statistical Association, 109: 1495-1516. [Reprint]
Fu, F. and Zhou, Q. (2013).
Learning sparse causal Gaussian networks with experimental intervention:
Regularization and coordinate descent.
Journal of the American Statistical Association, 108: 288-300. [Reprint]
Zhou, Q. (2011).
Random walk over basins of attraction to construct Ising energy landscapes.
Physical Review Letters, 106: 180602. [Reprint]
Sridharan, R., Tchieu, J., Mason, M.J., Yachechko, R., Kuoy, E., Horvath, S.,
Zhou, Q., and Plath, K. (2009). Role of the murine
reprogramming factors in the induction of pluripotency. Cell, 136:
S.C.*, Zhou, Q.*, and Wong, W.H. (2006). Equi-energy sampler with applications in statistical
inference and statistical mechanics (with discussion). Annals of Statistics,
34: 1581-1652. (*Equally contributed authors.) [Reprint]
Zhou, Q. and Wong, W.H. (2004).
CisModule: De novo discovery of cis-regulatory
modules by hierarchical mixture modeling. Proceedings of the National Academy of Sciences of USA,
101: 12114-12119. [Reprint]