Doctor of Philosophy in Computer Science and Engineering - Databases and Data Mining

The doctoral degree, i.e. the PhD, is primarily intended for students desiring a career in research andor collegiate teaching. The focus is on advanced CSE topics, on learning to perform research and to write research papers, and on making fundamental new contributions to a CSE topic. Students take advanced course work and write a doctoral dissertation, also called a thesis. Program overview: Students newly admitted to the doctoral program are classified as precandidates. Upon entering a doctoral program, there is a PhD qualifying process, normally completed during the first two years. After all requirements except the dissertation are completed, students become candidates. Students entering a CSE doctoral program with a bachelor’s degree typically become candidates in the third year and are strongly encouraged to complete the degree within five years. Such students ordinarily complete the requirements for a master’s degree along the way and receive this degree in addition to the PhD A masters thesis is optional. Students who enter a CSE doctoral program with a master’s in the field of their program typically become candidates in their second year and are strongly encouraged to complete the degree within four years. Such students are not ordinarily eligible to receive a CSE master’s degree. Database and data mining researchers in CSE build systems to manage, analyze, and extract value from massive, complex datasets. Research in this area covers scalable architectures for big data analytics, real-time stream processing, information integration, privacy-preserving data mining, and user-friendly query interfaces. Faculty and students address societal issues such as scientific data sharing, healthcare analytics, and secure information exchange. Our interdisciplinary approach bridges statistics, algorithms, networking, human-computer interaction, and application domains to create robust data-driven solutions. Example projects: Large-scale data management and integration for science and biomedicine; High-performance, adaptive cloud databases for fast, reliable data analytics; Incorporating learning-based methods into database management for automated optimization; Scalable techniques for mining and analyzing massive graph networks.