This thesis broadens the space of rich yet practical models for structured prediction. We introduce a general framework for modeling with four ingredients: (1) latent variables, (2) structural constraints, (3) learned ...

Many scientific disciplines are being revolutionized by the explosion of public data on the web and social media, particularly in health and social sciences. For instance, by analyzing social media messages, we can instantly ...

The healthcare industry has been adopting technology at an astonishing rate. This technology has served to increase the efficiency and decrease the cost of healthcare around the country. While technological adoption has ...

Computational modeling of the human brain has long been an important goal of scientific research. The visual system is of particular interest because it is one of the primary modalities by which we understand the world. ...

We can track the physical evolution of massive galaxies over time by characterizing the morphological signatures inherent to different mechanisms of galactic assembly. Structural studies rely on a small set of measurements ...

This dissertation explores computational methods to address the problem of physics-based modeling and ultimately doing inference from data in multiple modalities where there exists large amounts of low dimensional data ...

In the past few years, Deep Learning has become the method of choice for producing state-of-the-art results on machine learning problems involving images, text, and speech. The explosion of interest in these techniques ...

Human language artifacts represent a plentiful source of rich, unstructured information created by reporters, scientists, and analysts. In this thesis we provide approaches for adding structure: extracting and linking ...