Navigating latent structure in multi-omics
Modern sequencing, imaging, and spectroscopy technologies give a window into complex biological environments. However, exploring these data without getting lost remains a challenge — statistical methods can help chart out the territory. We are interested in building models and interfaces that help researchers summarize general patterns, spot interesting relationships, and articulate uncertainty. We have worked with probabilistic and latent variable models to better incorporate contextual knowledge and integrate complementary data sources.
Interactivity and visualization in earth system monitoring
Climate change has affected many earth systems, and data about these changes can guide societal adaptation. Remote sensing instruments can provide detailed views of ecosystems at a global scale, but manual annotation and summarization is an overwhelming task. Features of interest can be few and far between, and designing models that work well across different environments remains a challenge. We adapt ideas from machine learning and data visualization to make remote sensing data useful for practical downstream tasks.