Computational and Data Science and Engineering

University of California, Berkeley

About the Program

The Designated Emphasis (DE) in Computational and Data Science and Engineering Program (CDSE) 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 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 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.

Some of the CDSE activities include:

  • Identifying existing and encouraging the development of new courses that best serve to educate computational science and engineering students.
  • Encouraging involvement in CDSE 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.

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Applicants must already be students within a PhD program at UC Berkeley.

Students must be accepted into the program and petition to add the DE-CDSE before taking their qualifying exam.

Applicants for the DE-CDSE program are required to submit an online application; for access to the application, please see the program's website. With this form, the student must specify their proposed area of CDSE study and list which three courses they will take to fulfill the requirements. They also will upload a completed and signed Graduate Petition for change of Major or Degree Goal; a recommendation letter from their adviser; transcripts; a CV; and a one-page statement about why they are applying to the program.

For further information regarding admission to graduate programs at UC Berkeley, please see the Graduate Division's Admissions website.

Designated Emphasis Requirements


The student must take one course from each of the categories below; all courses taken to fulfill the DE requirements must be taken for a letter grade.

Category 1: Mathematical Tools
Select one of the following:
Advanced Matrix Computations
Numerical Solution of Differential Equations
Numerical Solution of Differential Equations
Probability for Applications
Theoretical Statistics
Theoretical Statistics
Statistical Models: Theory and Application
Statistical Models: Theory and Application
Experimental Design
The Statistics of Causal Inference in the Social Science
Nonparametric and Robust Methods
Statistical Learning Theory
Advanced Topics in Learning and Decision Making
Introduction to Statistical Computing
Statistical Computing
Analysis of Time Series
A course not listed, by petition to the program director
Category 2: High Performance Computing
Select one of the following:
Applications of Parallel Computers
Special Topics (Software Engineering for Scientific Computing)
A course not listed, by petition to the program director
Category 3: Application area
Application area courses that utilize the above tools in a significant manner 1

The student proposes this course, including a detailed syllabus documenting the use of mathematics and computation in an application area.

Qualifying Exam

Students must have a DE-CDSE component in their qualifying exam, with a DE-CDSE faculty member on the exam committee.


At least one member of the DE-CDSE faculty be on the dissertation committee.

Faculty and Instructors


Pieter Abbeel, Associate Professor. Artificial Intelligence (AI); Control, Intelligent Systems, and Robotics (CIR); Machine Learning.
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Dmitris Achlioptas, Professor.

Alice M. Agogino, Professor. New product development, computer-aided design & databases, theory & methods, intelligent learning systems, information retrieval & data mining, digital libraries, multiobjective & strategic product, nonlinear optimization, probabilistic modeling, supervisory.
Research Profile

Ramakrishna Akella, Professor.

Venkatesh Akella, Professor.

George A. Akerlof, Professor. Economics, macroeconomics, poverty, family problems, crime, discrimination, monetary policy, German unification.
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Richard Allen, Professor. Seismology earthquakes earthquake hazard mitigation earth structure tomography natural hazards.
Research Profile

Nina Amenta, Professor.

Rajeevan Amirtharajah, Professor.

Venkat Anantharam, Professor. Communications & Networking (COMNET); Artificial Intelligence (AI); Control, Intelligent Systems, and Robotics (CIR); Security (SEC); Signal Processing (SP).
Research Profile

Contact Information

Graduate Group in Computational and Data Science and Engineering

6117 Etcheverry Hall

Phone: 510-642-9172

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Department Chair/Head Graduate Adviser

Tarek I. Zohdi, PhD (Department of Mechanical Engineering)

6117 Etcheverry Hall

Phone: 510-642-9172

Program Coordinator

Yvette Subramanian

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