A variational Bayes approach to a semiparametric regression using Gaussian process priors
Victor M. H. Ong, David K. Mensah, David J. Nott, Seongil Jo, Beomjo Park, Taeryon Choi.
Electric Journal of Statistics. 11(2) pp. 4258-4296, 2017
Posts by Collection
publications
bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors
Seongil Jo, Taeryon Choi, Beomjo Park, Peter Lenk. Journal of Statistical Software. 90(10), pp. 1-41, 2019
Sparse signal shrinkage and outlier detection in high-dimensional quantile regression with variational Bayes
Daeyoung Lim, Beomjo Park, David Nott, Xueou Wang, Taeryon Choi.
Statistics and Its Interface. 13(2), pp. 237 – 249, 2020
Bayesian semiparametric mixed effects models for meta‐analysis of the literature data : An application to cadmium toxicity studies
Seongil Jo, Beomjo Park, Yeonseung Chung, Jeongseon Kim, Eunji Lee, Jangwon Lee, Taeryon Choi.
Statistics in Medicine., 2021
Spatio-temporal Local Interpolation of Global Ocean Heat Transport using Argo Floats: A Debiased Latent Gaussian Process Approach
Beomjo Park, Mikael Kuusela, Donata Giglio, Alison Gray. Annals of Applied Statistics., 2023
Robust Universal Inference
Beomjo Park, Sivaraman Balakrishnan, Larry Wasserman.
⁕ Winner of ASA Statistical Learning and Data Science Student Paper Award
talks
Bayesian Multivariate Hierarchical Semiparametric Mixed Model with Gaussian Process Priors
Bayesian Hierarchical Varying-coefficient Mixed Effect Model
Spatio-Temporal Local Interpolation for Quantifying Global Ocean Heat Transport from Autonomous Observations Permalink
teaching
Teaching Assistant
Undergraduate & graduate course, Korea University, 2016–2017
- Research Methods [Fall 17]
- Mathematical Statistics [Fall 17]
- Statistical Computing Methods [Spring 17]
- Elementary Computing Statistics [Fall 16]
Teaching Assistant
Undergraduate & graduate courses and research workshop, Carnegie Mellon University, 2018–2023
- 2021-2022 PhD TA of the Year Award
- Summer Undergrad Research Experience (SURE)
- Optum [Summer 23]
- Carnegie Mellon Sports Analytics Camp (CMSAC) [Summer 20, 21]
- Introduction to Statistical Inference [Spring 22]
- Advanced Methods for Data Analysis [Spring 20, 21]
- Probability and Mathematical Statistics [Fall 19, 20, 22; Spring 19]
- Statistical Graphics and Visualization [Summer 19]
- Statistical Computing [Fall 18]