This thesis naturally divides itself into two sections. The first two chapters concernthe development of Bayesian semi-parametric models for arrival times. Chapter 2considers Bayesian inference for a Gaussian process modulated ...

Although there is a rich literature on scalable methods for dimensionality reduction, the focus has been on widely applicable approaches which, in certain applications, are far from optimal or not even applicable. Dimensionality ...

This thesis contributes to a statistical modeling framework for protein sequence and structure evolution. An existing Bayesian model for protein structure evolution is extended in two unique ways. Each of these model extensions ...

Clinical prediction models offer the ability to help physicians make better data-driven decisions that can improve patient outcomes. Given the wealth of data available with the widespread adoption of electronic health records, ...

Sequential Monte Carlo (SMC) samplers have received attention as an alternative to Markov chain Monte Carlo for Bayesian inference problems due to their strong empirical performance on difficult multimodal problems, natural ...

Inferring brain states from EEG signals allows for the management of sleep disorders and brain diseases by providing an insight into the electrophysiological state of the brain. We explore the use of topic modeling – which ...

Deep learning and Bayesian Learning are two popular research topics in machine learning. They provide the flexible representations in the complementary manner. Therefore, it is desirable to take the best from both fields. ...

In this dissertation, we investigate several problems in shape analysis. We start by discussing the shape matching problem. Given that homeomorphisms of shapes are computed in practice by interpolating sparse correspondence, ...

Electron microscopy is one of the most powerful tools available in observational science. Magnifications of 10,000,000x have been achieved with picometer precision. At this high level of magnification, individual atoms are ...

I develop and apply a Bayesian method for quantitatively estimating properties of the quark-gluon plasma (QGP), an extremely hot and dense state of fluid-like matter created in relativistic heavy-ion collisions.The QGP cannot ...