Miriam Shiffman: We develop a full generative model and inference algorithm for reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. A central innovation is the development of a new class of Bayesian tree models for data that arise from continuous evolution along a latent nonparametric tree.
Miriam Udler: Complex diseases like type 2 diabetes (T2D) are thought to be caused by multiple contributing genetic and environmental processes. We have recently identified five key genetic pathways impacting T2D risk, and I am interested in whether we can use these pathways along with other clinical data to improve the classification (and ultimately management) of patients with T2D.
Brian Trippe: Generalized linear models and Bayesian inference provide a powerful toolkit for building interpretable models with coherent quantification of uncertainty, but are often computationally expensive to use on high-dimensional datasets. We present an approximation method which enables more efficient, accurate inference with theoretical guarantees on quality.
Eli Weinstein: The massive increase in genetic sequence data from diverse, uncultured microorganisms offers opportunities for the discovery of novel and useful molecular systems. I'll describe computational methods for finding genetic loci that are modular or programmable; our approach does not depend on identifying homology to previously characterized systems, relying instead on inference of sequence models and statistical tests for diversity.