Andrew Ying

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PhD candidate,
Department of Mathematics,
Univeristy of California, San Diego (UCSD)
Office: APM6442
Thesis advisors: Ery Arias-Castro and Ronghui (Lily) Xu

About me

I received my B.S. in mathematics from Zhejiang University back in China in 2015. Now I am a fourth year PhD candidate in department of mathematics in UCSD following two advisors.

One of my thesis advisors is Ery Arias-Castro with whom we focused on fields including multiple testing and global testing. Also, I am following Professor Ronghui (Lily) Xu to work on fields like causal inference and survival analysis.

I am also quite interested in machine learning theory like the state of the art, deep learning theory, which I hope I can make more contributions in the future.


My research interests include

  • Signal detection and identification

  • Missing mechanism like potential outcomes and unobserved confounders

  • Empirical process theory and max stable process

  • Machine learning and deep learning

Recent Publications

E. Arias-Castro and A. Ying. Detection of Sparse Mixtures: Higher Criticism and Scan Statistic. Electronic Journal of Statistics, 2019. [paper]

A.Ying, R. Xu and J. Murphy. Two-Stage Residual Inclusion for Survival Data and Competing Risks - An Instrumental Variable Approach with Application to SEER-Medicare Linked Data. Statistics in Medicine, 2018. [paper][code]

S. Chen, A. Ying and E. Arias-Castro. A Scan Procedure for Multiple Testing. arXiv preprint arXiv:1808.00631, 2018. [arxiv]