Congratulations to Ziyue Wu for passing his dissertation proposal!

Ziyue’s proposal involves using ensemble machine learning (a.k.a. super learning) for modeling healthcare expenditures. His first project proposes a two-stage super learner for dealing with healthcare expenditures with zero inflation and heavy right tails. Using simulations and data from the Medical Expenditures Panel Survey and the Back Pain Outcomes using Longitudinal Data, Ziyue showed improvements over existing methods for modeling healthcare data.

In his second project, Ziyue studied a super learner based on a Huber risk function. Ziyue proved theoretical results pertaining to the estimator’s behavior and again demonstrated impressive benefits in simulations and real data analysis.

Congrats, Ziyue!

About David Benkeser

I’m an Assistant Professor of Biostatistics and Bioinformatics at Emory University’s Rollins School of Public Health primarily interested in research involving vaccines, machine learning, causal inference, and all things data science. I teach courses on statistical theory, causal inference, and tools for data science. Beyond that, I offer individualized consulting services for research in clinical medicine, public health, and beyond.