I am a Ph.D. Candidate in Statistics at University of California, Los Angeles. My advisor is Dr. Oscar Hernan Madrid Padilla.
I graduated with Highest Distinction honors from University of California, Berkeley, where I pursued a dual degree in Applied Mathematics and Statistics. Throughout my undergraduate years, I had the privilege of collaborating with Dr. Carl Boettiger as a research intern at rOpenSci and with Dr. Philip B. Stark as a research assistant.
I have completed Data Science internships with the Data Science & Statistical Research Team at Nokia Bell Labs (June–December 2022), the Payments Data Science and Engineering Team at Netflix (June–September 2023), and the AIM-DS Team at Google (June–September 2024).
My current research focus is on non-parametric statistics and causal inference. I am also interested in their application in computational biology, social science, and economics.
S. Ye and O.H.M. Padilla. Non-parametric Quantile Regression via the K-NN Fused Lasso. Journal of Machine Learning Research, Vol. 22, No. 111, 1-38, 2021. PDF. Code.
S. Ye, Y. Chen, and O.H.M. Padilla. 2D Score Based Estimation of Heterogeneous Treatment Effects. PDF. Code. Journal of Causal Inference, Vol. 11, No. 1, 1-26, 2023.
A.K. Glazer, H. Luo, S. Devgon, C. Wang, X. Yao, S. Ye, F. McQuarrie, Z. Li, A. Palma, Q. Wan, W. Gu, A. Sen, Z. Wang, G.D. O’Connell, P.B. Stark. Look Who’s Talking: Gender Differences in Academic Job Talks. PDF. ScienceOpen Research, 2023.
S. Ye, Y. Chen, and O.H.M. Padilla. Causal Effect Estimation via Graph Based Fused Lasso. Under Review, 2024+.
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