Our focus is on developing novel artificial intelligence (AI) and machine learning (ML) methodologies to advance neurodegenerative disease research. Rapid advances in AI and ML are in the process of transforming biomedical and health research. We work on specific AI and ML methods for prediction, stratification/endotype discovery and systems-level analyses, with an emphasis on high-dimensional and heterogeneous data, causality and latent states and processes. 

Advances in high-throughput molecular assays and deep phenotyping coupled with systems-level analyses are changing how biomedical research is done. Such approaches can inform stratification into disease subtypes/endotypes, allow prediction of disease state and help elucidate relevant biology at a systems level. 

Our efforts are directed towards developing AI, ML and high-dimensional statistical methods to fully realize this potential. This involves working on novel methods motivated by, and applied to, specific scientific questions but also working with colleagues across research areas to clarify conceptual issues that arise in moving towards truly scalable and data-intensive approaches. In the area of systems biology, we are currently working on principled yet highly scalable approaches by which to build and test global molecular networks that are specific to biological or disease context. Furthermore, we are working on methods for prediction and stratification of neurodegenerative diseases, with an emphasis on multimodal analyses using diverse high-dimensional data types.