Doctor of Philosophy in Computer Science and Engineering - Warehouse Scale and Parallel Systems

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. Research in warehouse-scale and parallel systems advances the architecture and software that power cloud computing, scientific discovery, and data-intensive industries. Faculty and students in CSE design highly parallel processors, scalable distributed systems, energy-efficient data centers, virtualization, and high-speed interconnects. Research in this area tackles performance, efficiency, reliability, and manageability at unprecedented scale, enabling new applications in business, science, and engineering. Collaborations with industry ensure real-world impact and technology transfer. Example projects: Scalable software, hardware, and datacenter systems for high-efficiency cloud deployment; Energy-aware hardware and software for parallel supercomputing and scientific simulation; Customizable accelerators for machine learning and big data.