About me

I am a Data Scientist, Research at Google, developing quantitative methods to optimize creative generation and drive systematic efficiencies in YouTube Ads. I earned Ph.D. in Statistics from Carnegie Mellon University, where my research focused on robust statistical inference, including assumption-light methods for accommodating model misspecification. I was very fortunate to be advised by Sivaraman Balakrishnan and Larry Wasserman. In collaboration with oceanographers and Mikael Kuusela, I also developed a spatiotemporal framework for large-scale oceanographic data from Argo floats to provide data-driven insights into global ocean heat transport and related dynamical phenomena, such as El Niño & La Niña.

Prior to joining the Carnegie Mellon University, I worked on wide-ranging applications and extensions in the Bayesian semiparametric regression and Variational inference at Korea University advised by Taeryon Choi. Hierarchical modeling and fast Variational Bayes approximation to the flexible Bayesian regression framework were the central themes.