With the growing commonality of multi‐omics datasets, there is now increasing evidence that
integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers …

BackgroundAdolescence is characterized by profound change, including increases in
negative emotions. Approximately 84% of American adolescents own a smartphone, which …

We propose a general class of algorithms for estimating heterogeneous treatment effects on
multiple studies. Our approach, called the multi-study R-learner, generalizes the R-learner to …

It is increasingly common to encounter prediction tasks in the biomedical sciences for which
multiple datasets are available for model training. Common approaches such as pooling …

We propose the “study strap ensemble”, which combines advantages of two common
approaches to fitting prediction models when multiple training datasets (“studies”) are …

We introduce a statistical procedure that integrates datasets from multiple biomedical
studies to predict patients' survival, based on individual clinical and genomic profiles. The …

Cross-study replicability is a powerful model evaluation criterion that emphasizes
generalizability of predictions. When training cross-study replicable prediction models, it is …

The smartphone has become an important personal companion in our daily lives. Each time
we use the device, we generate data that provides information about ourselves. This data, in …

In many areas of biomedical research, exponential advances in technology and facilitation
of systematic data-sharing increased access to multiple studies. This dissertation proposes …

Real-time neurochemical sensing during awake behavior in humans allows for direct
investigation of the neural signals that drive human decision-making. Current techniques to …