Our group uses statistical and computational methods to study questions in genomics and evolutionary biology. Much of our work focuses on questions relating to genetic variation and evolution. An important part of our work is in developing appropriate statistical and computational approaches that can yield new insights into biological data.
My lab’s research uses neuroimaging to understand how neural systems give rise to complex cognitive functions and how these systems break down in neuropsychiatric disorders. We use machine learning techniques to decode behavior from neuroimaging data and to characterize the multidimensional structure of neural representations. We are also heavily involved in the development of neuroinformatics tools, including ontologies of mental function (through the Cognitive Atlas project), data sharing (through the OpenFMRI and Neurovault projects), and automated meta-analysis (through the Neurosynth project).
Research concerns health policy, clinical policy, and the development of analytic methods for evaluating policy questions. Particular interest in technology assessment and the application of decision theory to clinical/health policy problems. Special interest in questions related to disease caused by the human immunodeficiency virus (HIV) and cardiovascular disease.
My research interests include analysis of high throughput biological data, for instance finding age-related genes in multiple species and tissues with the Kim lab. I am generally interested in settings where both rows and columns of the data matrix correspond to entities of interest, that is, neither are IID. Special interests include adjusting for the effects of latent variables, finding ways to bootstrap and cross-validate non-IID data, and making extensions to three-way and higher order data arrays. I also work on Monte Carlo methods.