Ph.D. Presentations

Modern scientific studies increasingly rely on complex datasets exhibiting spatial and temporal dependence, particularly in social, environmental, and climate applications. This dissertation develops statistical models and computational methods for analyzing such data, with an emphasis on capturing dependence structures, nonlinear dynamics, and uncertainty quantification. A spatial deep learning framework is developed to extend classical geostatistical […]

Most optimization problems face the challenge of computing an optimum solution requiring superpolynomial time. In particular, they are classified as NP-hard problems that have no polynomial-time algorithm to date. Instead, […]

Research on fault-tolerance protocols for approximate Byzantine agreement (ABA) has largely focused on ensuring that distributed processes remain consistent despite fewer than 1/3 faulty processes. Yet in many real systems, consistency is only useful when it enables processes to make accurate decisions from replicated, noisy, and potentially adversarially corrupted data relative to an ideal fault-free […]