Research

Research Interests:

My research is at the intersection of Bayesian statistics, high-dimensional inference, and machine learning, focusing on structured data such as tensors, networks, and multivariate time series. I develop Bayesian tensor regression models with random projection–based dimension reduction to enable scalable inference in complex settings. My theoretical work establishes concentration inequalities and posterior consistency under compression, providing guarantees for reliable uncertainty quantification, with applications in macro-finance and financial econometrics.

Publications:

Journal articles:

• Markov Switching Multiple-equation Tensor Regressions. Journal of Multivariate Analysis (2025): 105427. (with Roberto Casarin and Radu Craiu) pdf, poster, Code.

Book Chapters:

• Casarin, R., Craiu, R., & Wang, Q. (2025). Markov Switching Tensor Regressions. New Trends in Functional Statistics and Related Fields, 109. Springer, Cham. pdf

Working Papers:

• Compressed Bayesian Tensor Regression (jointly with Roberto Casarin and Radu Craiu). arxiv
• Bayesian tensor regression with stochastic volatility