About me

I am a Data Scientist at Google. Previously, I was a postdoctoral researcher in the Department of Statistics at Carnegie Mellon University, working with Aaditya Ramdas and Alessandro Rinaldo. I recently graduated from the same department with a Ph.D. in Statistics.

Research

My research interest lies in understanding the sequential and adaptive nature of data analysis. I study how commonly used statistical inference procedures behave under the presence of an analyst’s data-dependent choices. My current projects focus on designing and analyzing nonasymptotic sequential testings and online change-point detection procedures.

Keywords: Always-valid inference, Sequential test, Multi-armed bandit, Change-point detection

Recent Papers

E-detectors: a nonparametric framework for online changepoint detection
J. Shin, A. Ramdas, A. Rinaldo
New England Journal of Statistics in Data Science, 2024 arXiv, code

Nonparametric iterated-logarithm extensions of the sequential generalized likelihood ratio test
J. Shin, A. Ramdas, A. Rinaldo
IEEE Journal on Selected Areas in Information Theory, 2021 arXiv, code

On conditional versus marginal bias in multi-armed bandits
J. Shin, A. Ramdas, A. Rinaldo
Thirty-seventh International Conference on Machine Learning (ICML 2020) arXiv

Are sample means in multi-armed bandits positively or negatively biased?
J. Shin, A. Ramdas, A. Rinaldo
Neural Information Processing Systems (NeurIPS 2019, Spotlight) arXiv

On the bias, risk and consistency of sample means in multi-armed bandits
J. Shin, A. Ramdas, A. Rinaldo
SIAM Journal on Mathematics of Data Science, 2021 arXiv proc