Models and Inference for Complex Data with Applications in Nuclear Non-Proliferation and Microbial Systems

Passcode: 529859

Abstract: With recent advances in science and technology, researchers are provided with unprecedented amounts of data to analyze. However, due to the complex structure of the data (e.g high dimensional, discrete, incomplete), extrapolating meaningful information from these data requires models that incorporate knowledge about the underlying systems and efficient computation methods. In this thesis, we have developed statistical models, inference algorithms (based on Monte Carlo methods and on optimizations), and their performance analysis for several complex problem domains: inverse problems in radiation detection, data fusion and classification in high dimensional microbiome studies, and contingency table analysis for inferring voting patterns in election polling data with missing information.

Chair: Professor Alfred Hero