Learning structure models from complex data: principles and algorithms

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Almost all data is now digitally stored, with unprecedented computing power available to process it. These developments have created new opportunities to advance computer interpretation (e.g. natural language processing, computer perception) and intelligent data-analysis (e.g. bio-informatics) by analyzing the massive amounts of complex data stored in text, multimedia, and scientific repositories. My research addresses the fundamental challenge of synthesizing predictive models from data, focusing on the problem of learning structured predictors in the presence of latent variables and incomplete observations. These challenges are motivated by the fact that predictions in complex domains are not just simple class labels or scalar values, but are complex structured outputs---such as parse trees, scene labellings or graph labellings---that involve multiple outputs to be predicted in a coordinated fashion, often with intervening latent variables. The key problem is training complex predictors when some of the output or intervening latent variables are unobserved.