The program is open only to students who are already enrolled in a Ph.D. program at UC Berkeley.
The Designated Emphasis in Computational and Data Science and Engineering Program at the University of California, Berkeley trains students to use and manage scientific data, whether it is in analyzing complex physical systems or in using statistics and machine learning, along with data visualization, to extract useful information from the massive amount of data that can be collected from sensors today. The CDSE program is committed to the development of new curricula and expanded programs aimed at development and propagation of the use of numerical and computational tools to further research across multiple disciplines. To that end, the CDSE program will actively support the training and multidisciplinary education of scientists, engineers and technical specialists who are experts in relevant areas.
The CDSE program that crosses numerous disciplines, and participating departments include Computer Science, Mathematics, Chemistry, Mechanical Engineering, Astronomy, Neuroscience and Political Science, among many others. Upon graduation, the student receives a “PhD in X with a Designated Emphasis in Computational and Data Science and Engineering” on their transcript and diploma. This designation certifies that he or she has participated in, and successfully completed, a Designated Emphasis in addition to the departmental requirements for the PhD, and completion of the DE-CDSE will also be posted to the student’s transcript.
- Identifying existing and encouraging the development of new courses that best serve to educate computational science and engineering students
- Encouraging involvement in CSE research activities by undergraduate and graduate students and postdoctoral researchers, and, potentially, by experienced professionals seeking to develop new skills that can benefit their careers
- Supporting formal short “boot-camp” education programs involving both live and web-based course offerings, with certificates if completed
- Supporting summer schools, seminar series, and tutorials
- Integration of these activities with LBNL
A great many fields of science, engineering, finance and social science are embracing modeling, simulation, and data analysis as necessary tools to advance their fields. Sometimes this is driven by need to perform simulations of systems that cannot easily be directly measured, and sometimes it is driven by the increasing generation of large data sets that require extensive computation to understand. Both need to take advantage of the computational power which comes from continuing advances in computer components and architectures, including parallel computing.