1. Research Area

 Our research interest broadly lies in statistics and data science with an emphasis on developing statistical methods with theoretical guarantees. The core of research interest centers around Bayesian inferences with large parameter spaces. We are also interested in frequentist’s inferential methods and other scientific areas that are motivated by collaborators in various field in academia and industry. Some topics of recent interest include but not limited to
— frequentist’s properties of Bayes procedures
— efficient and scalable MCMC algorithms
— finite and infinite mixture models
— learning theory
— online learning methods

2. Publication

  •  Chae. M and Walker. S (2020). Wasserstein upper bounds of the total variation for smooth densities. To appear in Statistics & Probability Letters.
  •  Chae. M and Walker. S (2020). An EM-based iterative method for solving large sparse linear systems. Linear and Multilinear Algebra. 68(1):45–62.
  •  Chae. M, Lin. L.and Dunson. D (2019). Bayesian sparse linear regression with unknown symmetric error. Information and Inference. 8(3):621–653.
  •  Chae. M, Kim. Y and Kleijn. B (2019). The semi-parametric Bernstein-von Mises theorem for regression models with symmetric errors. Statistica Sinica. 29(3):1465–1487.
  •  Chae. M, Martin. R and Walker. S (2019). On an algorithm for solving Fredholm integrals of the first kind. Statistics and Computing. 29(4):645–654.
  •  Frank. G, Chae. M and Kim. Y (2019). Additive time-dependent hazard model with doubly truncated data. Journal of the Korean Statistical Society. 48(2):179–193.
  •  Chae. M and Walker. S (2019). Bayesian consistency for a non parametric stationary Markov model. Bernoulli. 25(2):877–901.
  •  Chae. M, Martin. R and Walker. S (2018). Convergence of an iterative algorithm to the non parametric MLE of a mixing distribution. Statistics & Probability Letters. 140:142–146.
  •  Chae. M and Walker. S (2017). A novel approach to Bayesian consistency. Electronic Journal of Statistics. 11(2):4723–4745.
  •  Kim. Y, Chae. M, Jeong. K, Kang. B and Chung. H (2016). An online Gibbs sampler algorithm for hierarchical Dirichlet processes prior. In Proceedings of the ECML-PKDD Discovery Challenge Workshop. pages 509–523.
  •  Woo. S, Chae. M, Jebb. A and Kim. Y (2016). A closer look at the personality turnover relationship: criterion expansion. dark traits. and time. Journal of Management. 42(2):357–385.
  •  Kim. Y and Chae. M (2014). Beta processes and survival analysis. The Korean Journal of Applied Statistics. 27(6):891–907.
  •  Jeong. K, Chae. M, Lee. S, Cho. K and Kim. Y (2013). Development of high-value traits of dairy cattle using survival analysis. Journal of the Korean Data Analysis Society. 15(5):2407–2416.
  • Chae. M, Kang. M and Kim. Y (2013). Documents recommendation using large citation data. Journal of the Korean Data and Information Science Society. 24(5):999–1011.
  •  Chae. M, Weissbach. R, Cho. K and Kim. Y (2013). A mixture of beta-Dirichlet processes prior for Bayesian analysis of event history data. Journal of the Korean Statistical Society. 42(3):313–321.

3. Research Project 

  •  Development of AI-based recommendation system for curated retailing services in Samsung C&T (with Samsung C&T). Nov 2019 – Apr 2020.