Research
High Dimensional Inference/Big Data Analytics
Methods: inverse covariance matrix estimation (CLIME, SCIO) with applications in recovering networks and prediction, covariance matrix estimation (LOREC) with applications in identifying hidden factors and model structures.
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Network Models for Neuroscience, Genomics and Beyond
Network models help elucidate the biological mechanisms behind the data. We are interested in inferring large networks from data, and linking these networks to quantitative traits.
Causal Inference
Methods: Understanding what causes brain to think? How to infer causality from complex time series? One method is reported here.
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Imaging Analytics and Spatial Temporal Pattern Analysis
Imaging data arise in neuroscience, genomics, proteomics, and many other disciplines. We are interested in understanding how these patterns predict scientific (e.g. medical and biological) outcomes.