Qing Zhou

Qing Zhou is Professor and Chair of Statistics & Data Science at the University of California, Los Angeles, and a faculty member in the Interdepartmental Program in Bioinformatics. His methodological work integrates causal inference, high-dimensional statistics, Monte-Carlo computation and statistical bioinformatics, with applications ranging from gene-regulatory networks to financial risk models.

Education & Affiliations:

• Professor & Chair, Department of Statistics and Data Science, UCLA
• Faculty, UCLA Interdepartmental Program in Bioinformatics
• Ph.D. training in Statistics (implied by faculty rank and publication record)

Research Interests:

Zhou develops theory and algorithms for learning directed acyclic graphs (DAGs) to uncover causal relations from large-scale data. His group designs penalized likelihood, divide-and-conquer and federated strategies for structure learning of Bayesian networks, accompanied by R packages and scalable software. In high-dimensional inference he introduced “estimator augmentation” to deliver honest confidence sets for lasso-type estimators, and recently proposed projection-and-shrinkage techniques that merge sparse regularization with Stein estimation. Monte-Carlo innovation is another pillar: he invented the equi-energy sampler and random-walk schemes to explore energy landscapes and reconstruct density-of-states in statistical physics and Bayesian computation. In bioinformatics he builds model-based and sparse-regression tools to integrate RNA-seq, ChIP-seq, chromatin-interaction and sequence data, leading to biological discoveries on RNA editing, alternative splicing and embryonic-stem-cell regulatory circuits.

Publication Trends:

Recent articles focus on (i) causal discovery in nonlinear and high-dimensional DAGs, (ii) federated and privacy-preserving learning of causal networks, (iii) confidence-set construction post model-selection, and (iv) stochastic blockmodels and graphons for network data. A 2025 research monograph synthesizes advances in latent structure and causal graphical models.

Scientific Awards & Recognition:

• Authored research monograph “Latent Structure and Causality: Inference from Data”, World Scientific 2025
• Consistent federal and UCLA funding (implied by continuous publication output and chair position)

Advising & Grants:

While specific student names and grant numbers are not listed in the source text, the sustained publication stream in top-tier journals and the role as department chair indicate active supervision of doctoral students and postdocs and ongoing NIH/NSF support.

Labs & Teams:

Zhou leads the computational statistics and causal inference group within the UCLA Department of Statistics & Data Science, maintaining cross-departmental collaborations with the Bioinformatics program and experimental biology labs on campus.