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, computer scientists turn to find an approximate solution and create numerous elegant algorithms. However, in the modern era, computational environments have changed drastically, and we […]

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 […]

Pattern counting is a fundamental problem in computer science with applications in many domains. For a fixed small pattern H, we are given a large graph G and we are […]

Sequencing technologies have revolutionized our understanding of biology, yet many existing methods require fragmentation of DNA or RNA, fundamentally limiting our ability to study these molecules in their native, intact […]

Centromeric DNA is critical for accurate chromosome segregation and genome stability, but due to its repetitive nature, it was only recently fully included in a human reference. Rapid evolution and […]

Every year, tens of thousands of tons of plaster-based materials are used in restoration and conservation applications, many of which are derived from non-renewable sources and discarded at the end […]

This work addresses improvements in branch prediction mechanism to support high perfor- mance processors. The state of the art aims to balance the prediction latency and prediction accuracy using multi […]

Autonomous Cyber Operations (ACO) agents promise effective security automation with minimal human intervention, yet their deployment raises three interconnected challenges: agents must be realistic (reproducing diverse attacker sophistication), secure (preventing […]