Abstract: Haohan Wang's (Carnegie Mellon University, Expected 2021) research focuses on the systematic development of trustworthy machine learning (ML) systems that can be deployed to answer biomedical questions in the real-world scenarios, consistently responding over significant variations of the data. In particular, Haohan's work focuses on improving robustness of ML models to dataset shift, specifically towards the application of early prediction of Alzheimer's disease from genetic and imaging data. Haohan's methods focus on using a nuanced understanding of the data generative process in order to better account for expected distributional shifts, yielding more robust and interpretable models of Alzheimer's diagnosis. In other work, Haohan has also investigated the use of ML methods on genomic and transcriptomic data for biomedical applications.