uc berkeley computer science phd application

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U.S. News & World Report ranks UC Berkeley computer science graduate program No. 1

UC Berkeley’s computer science graduate program was ranked first in the nation for the second year in a row by U.S. News & World Report , according to 2024 rankings  released April 8.

Berkeley’s program in the Department of Electrical Engineering and Computer Sciences shared the top spot with computer science programs at the Massachusetts Institute of Technology, Stanford University and Carnegie Mellon University. 

Several other Berkeley graduate programs in business, public health, public affairs and more were listed in the top 20 for their disciplines. These rankings are based on a survey of academics at peer institutions, according to U.S. News .

Berkeley’s Department of Electrical Engineering and Computer Sciences is shared by the College of Computing, Data Science, and Society and the College of Engineering. Learn more about Berkeley’s computer science graduate program.

Computational Precision Health

About the Program

Computational Precision Health (CPH) is an exploding field across both academia and industry. This rapidly evolving field integrates the tremendous advances in data science and data availability that have occurred over the past decades with expertise in clinical medicine, public health, and health care systems to enable a paradigm shift in the ways we treat and prevent disease. Advances in data and analytics open the door to faster deployment of more effective health interventions, but this potential can only be achieved if the underlying computational and analytic tools are conceived, tested, and validated for the health and health care needs of diverse individuals and communities. The field of Computational Precision Health aims to realize this potential.

The PhD in Computational Precision Health leverages and bridges the complementary expertise and incredible resources of UC Berkeley and UCSF to create an unparalleled and truly unique learning environment. Students in the PhD in Computational Precision Health will develop skills and expertise in both the computational sciences (machine learning and AI, natural language processing, statistical inference and modeling, data standards, parallel computing and data at scale, etc.) and health sciences (clinical decision sciences and cognitive informatics, clinical delivery, clinical research, implementation science, health information policy, etc.) 

Students will develop the ability to work in interdisciplinary teams from ideation to development, testing, and validation in the real world. Coursework will be complemented by extensive and early interaction with world-class faculty—through research rotations, seminar series, and practicums—at the intersection of computation and health, and will develop proficiency in cross-disciplinary research and communication. A focus on diversity, equity and inclusion, human-centered design accommodating diverse users, and the ethical implications and societal impacts of the work will be embedded throughout the program. 

Designated Emphasis

The Designated Emphasis in CPH is administered by the joint UC Berkeley/UCSF Computational Precision Health Augmented Graduate Group. The UCB CPH DE allows PhD students from affiliated UCB programs to incorporate CPH courses and advising into their PhD. CPH DE students will receive a solid grounding in the fundamentals of computational precision health, with training in the application of computation to the practice of medicine and public health. Students will be part of an interdisciplinary, intercampus community of UC Berkeley and UCSF scholars with diverse academic backgrounds, providing unique cross-campus opportunities, including direct exposure to the clinical care and health science environment offered at UCSF.

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PhD Admissions

The PhD program in Computational Precision Health welcomes students from a broad range of computational sciences, health sciences, and interdisciplinary backgrounds.

Applications are submitted via the UC Berkeley Graduate Division application portal. More information about requirements .

Designated Emphasis (DE) Admissions

A designated emphasis (DE) is an interdisciplinary specialization, such as a new method of inquiry or an important field of application, which is relevant to two or more existing doctoral degree programs. A DE is not a standalone program, but is offered to complement a student’s current doctoral studies. UC Berkeley PhD students are welcomed to apply as well as students from UCSF affiliate programs. More information.

Students are encouraged apply at least two semesters before your Qualifying Examination by submitting the following materials to the Computational Precision Health Program, by emailing the documents below to [email protected] .

• Petition for Admission to the Designated Emphasis in Computational Precision Health .
• Letter of intent summarizing your research interests and background in Computational Precision Health. When possible, the letter should include one or more CPH Core Graduate Group faculty members from your home campus as potential DE advisors
• Letter of recommendation from a  CPH Graduate Group Core  or Affiliate faculty member, ideally from your home campus.
• An updated CV and academic transcript

Doctoral Degree Requirements

Courses may be taken at ucsf  or uc berkeley :, cph 215 : lab rotations  (at least 4 units x 2 semesters in the first year).

Students will take two 10-week research group rotations in their first year. One rotation will be on each campus, with one rotation in a predominantly computational science “lab” (with a health emphasis) and one in a health science “lab” (with a computational emphasis).

CPH 270 : Computational Precision Health Seminar  (2 semester units x 6 terms)

Students will enroll in six terms of the doctoral seminar, including the first two terms after matriculation. Seminar will consist of journal club-style discussion of recent literature in computational precision health, talks by guest faculty, and presentations by second and third year students on work in progress.  Seminar will consist of journal club-style discussion of recent literature in computational precision health, talks by guest faculty, and presentations by second and third year students on work in progress.

UCSF courses

Three-part cornerstone series, taken at ucsf  (3 units x 3 quarters).

CPH 200A, CPH 200B AND CPH 200C: COMPUTATIONAL PRECISION HEALTH CORNERSTONE COURSE SERIES (3 UNITS PER COURSE, 3 UNITS PER QUARTER FIRST YEAR) This course series, which uses Problem-Based Learning to build student’s ability to work effectively in interdisciplinary teams, from ideation to development, testing, and validation in the real world.

CPH 201A: CPH PRACTICUM (3 UNITS):  Provides the foundations for understanding and engaging with inpatient and outpatient clinical care. Students will gain deep and continuing exposure to the clinical and public health contexts in which CPH advances are to be deployed. Students will have in-depth real world exposure relevant to problem area(s) covered in the problem-based learning core, including clinical, research, and operational work in inpatient, outpatient, community health, and/or public health settings, AND

CPH 201B: CPH PRACTICUM  (3 UNITS):  A 2-semester course series taken during the second year of Computational Precision Health, augmenting the Cornerstone course to provide deep and continuing exposure to the clinical and public health contexts in which CPH advances are to be deployed. Students will have in-depth real world exposure relevant to problem area(s) covered in the problem-based learning core, including clinical, research, and operational work in inpatient, outpatient, community health, and/or public health settings.

Designated Emphasis Requirements

Curriculum/coursework.

Students admitted to the CPH DE program must complete two semesters of the CPH Doctoral Seminar, and at least 3 courses from the core course list below, in the following two domain areas:

• Health and Public Health Sciences
• Computing and Statistical Sciences

In order to ensure that the DE confers sufficient additional breadth beyond a student’s home program, students in a primarily computational PhD program (for example, Bioengineering, Electrical Engineering and Computer Science, Computer Science, Statistics, Biostatistics, Computational Biology, Industrial Engineering and Operations Research) will be required to take at least two courses in the health domain; those in Epidemiology and Health Policy will be required to take at least two courses in the Computational Sciences domain. 

Core Courses

Qualifying courses are listed below. Additional courses falling within the two domains below may also qualify, to be approved by the student’s DE advisor.

1. Health and Public Health Science

2. Computing and Statistical Sciences

In some cases, for example, STAT 154 and STAT 156 / STAT 256 , an upper division undergraduate course may be acceptable for the DE. This is due to the desire to accommodate students from non-computational PhD programs who may not have the programming, mathematics or statistics prerequisites for corresponding graduate-level coursework.

CPH Doctoral Seminar

In addition, students will participate in at least 2 semesters of the CPH Doctoral Seminar. The seminar will consist of a combination of journal club-style discussion of recent literature in Computational Precision Health, and guest faculty speakers drawn from across the CPH Graduate Group and beyond. This seminar will be held in conjunction with UCSF DE CPH students.

Elective Courses

No Elective courses are required for the Designed Emphasis, but the DE Advisor may guide students on additional courses to supplement their training in this field.

UC Berkeley Affiliated Programs

PhD in Bioengineering

PhD in Electrical Engineering and Computer Sciences

PhD in Computer Science

PhD in Biostatistics

PhD in Statistics

PhD in Computational Biology

PhD in Epidemiology

PhD in Health Policy

PhD in Industrial Engineering and Operations Research

PhD in Information Science

*Students from any PhD program at UC Berkeley are welcome to apply.

Normative Time

No additional time can be added to the normative time of your home department.

Contact Information

[email protected]

Director, UC Berkeley UCSF Joint Program in Computational Precision Health

Maya Petersen, MD, PhD

[email protected]

Ida Sim, MD, PhD

[email protected]

Senior Administrative Officer

Rhiannon Lewis

[email protected]

Graduate Student Affairs Officer

Bianca Victorica

[email protected]

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Computational Precision Health

PhD Program

The PhD in Computational Precision Health leverages and bridges the complementary expertise and incredible resources of UC Berkeley and UCSF to create an unparalleled and truly unique learning environment.

Students in the PhD in Computational Precision Health will develop foundational competency in the computational and mathematical sciences (e.g., machine learning, AI, causal and statistical inference, and algorithmic bias and fairness), drawing from motivating examples from the health domain. Students will be introduced to the  health sciences (clinical decision sciences and cognitive informatics, clinical delivery, clinical research, implementation science, health information policy, etc.) through program activities to provide the context for computational problems in the field of CPH.

Students will develop the ability to work in interdisciplinary teams from ideation to development, testing, and validation in the real world. Coursework is complemented by extensive and early interaction with world-class faculty–through research rotations, seminar series, and practicums–at the intersection of computation and health, and students will develop proficiency in cross-disciplinary research and communication. A focus on diversity, equity and inclusion, human-centered design accommodating diverse users, and the ethical implications and societal impacts of the work is embedded throughout the program.

Students interested in applying to the PhD program may contact Bianca Victorica [email protected] for information. Students are also encouraged to reach out to AGG faculty to discuss their interests. See details on Admissions page

Program Requirements

Normative time to degree: 5 years.

Students will enter the PhD program in Computational Precision Health from a wide range of backgrounds, so each program of study will be personalized to and tailored to student background, and goals.

All students will complete:

• CPH Cornerstone course series (3 semester units, 2 semesters)
• CPH Practicum series (2 semester units, 2 semesters)
• CPH Doctoral Seminar series (2 semester units, 6 semesters)
• Foundational courses: Minimum of four classes, selected in close consultation with their Academic Advisor (year 1) or Research Advisor(s) (year 2).
• Advanced Electives: Minimum of two advanced electives, based on intended dissertation work.
• Race and Racism in Science, and Ethics; The Responsible Conduct of Research (1.3 semester units; 1 semester unit)
• Rotations: Students will take two 10-week research group rotations in their first year. One rotation in a predominately computational lab (with health emphasis) and one in a predominantly health science lab (with computational focus).

Qualifying Exams (QEs)

The oral qualifying exam is an important milestone, intended to certify that a PhD candidate is on track to progress to the research phase of their graduate studies. Each student should pass the QE by the end of the fourth semester.

The exam will evaluate the depth of student knowledge in their research area, breadth of knowledge in fundamentals of computational precision ehealth, ability to formulate a research plan, and critical thinking. Specifically, the QE in CH will cover fundamentals of computational science, fundamentals of health science, and the student’s area of specialization.

QE Committee

The QE Committee will consist of four members of the Berkeley or UCSF Academic Senates: three CPH AGG core faculty members, and an outside member, who may be a CPH AGG affiliate, but must be a Senate member from the same home campus as the student. At least one faculty member from each campus must be included. The QE Committee Chair must be a core member of the CPH AGG and from the same home campus as the student.

Dissertation

All dissertation projects must be scholarly, independent and original research that implements knowledge, techniques, and methods from the computational and health sciences to contribute new knowledge to the field. Students will commence work on their dissertation by the fourth semester, after advancing to candidacy.

Dissertation Committee

The Dissertation Committee will consist of at least three members of the Berkeley or UCSF Academic Senates, with at least one member from each. The student’s Research Advisor (or co-Advisors) will serve as the Chair (or co-Chairs). The Chair and Academic Senate Representative must both be members of the Academic Senate and from the same home campus as the student.

Oral Presentations

A dissertation defense will not be required ; however, students will be required to present their research orally on a number of occasions, including during the Doctoral seminar, and during program retreats.

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<  Berkeley Engineering Site

Berkeley Engineering Launches AI Program for Execs

Berkeley, CA, Aug 27, 2024 — On November 11-12, 2024, Berkeley Engineering’s Sutardja Center for Entrepreneurship & Technology (SCET) will launch the AI for the C-Suite program, a premier executive education course designed to equip senior leaders with strategic insights into leveraging artificial intelligence (AI) within their organizations. This in-person AI strategy workshop, held on the UC Berkeley campus, will be led by an exceptional faculty of AI pioneers and industry innovators who have shaped the future of AI.

Revolutionizing Executive Leadership with AI

Berkeley Engineering, consistently ranked among the top engineering schools globally, including the #3 U.S. undergraduate and graduate engineering program, is proud to offer this advanced AI strategy training tailored for C-suite executives. The AI for the C-Suite program is a unique opportunity for senior leaders to engage directly with world-renowned AI experts and gain actionable insights to drive AI transformations in their businesses.

World-Class Faculty Leading the Program

Participants will have the chance to learn from Berkeley’s distinguished faculty, including:

• Director of Berkeley Robot Learning Lab
• Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab
• Professor, Berkeley Electrical Engineering & Computer Sciences (EECS)
• Industry Fellow, UC Berkeley College of Engineering, SCET
• Distinguished Professor, UC Berkeley Computer Science
• Smith-Zadeh Professor in Engineering
• Associate Professor, Electrical Engineering & Computer Sciences (EECS)
• Director of AI Safety and Alignment, Google DeepMind
• CEO & Co-Founder, Databricks
• Adjunct Professor, UC Berkeley Electrical Engineering & Computer Sciences (EECS)
• Director, CITRIS Policy Lab
• Associate Research Professor, Goldman School of Public Policy
• Executive Chairman, Databricks
• Director, Sky Computing Lab
• Professor, UC Berkeley Computer Science
• Partner, Cybersecurity & Data Privacy Practice, Mayer Brown
• Founder, Co-Chief Executive Officer, NxtWork

Empowering the Next Generation of AI Leaders

UC Berkeley is ranked #1 globally by Pitchbook for producing venture-backed startups, a testament to its deep commitment to innovation and entrepreneurship. At the heart of this success is Berkeley’s Sutardja Center for Entrepreneurship & Technology (SCET), which has been instrumental in nurturing groundbreaking startups and fostering an entrepreneurial mindset. The AI for the C-Suite program reflects this mission by empowering executives to lead their organizations through AI-driven transformations, equipping them with the tools and frameworks needed to develop and implement strategic AI initiatives.

What Makes This Program Unique?

This executive AI course is not just about learning AI concepts; it’s about applying them to business strategy. With hands-on workshops and direct access to industry-leading AI practitioners, participants will gain practical insights that can be immediately applied to their organizations. The program covers everything from AI strategy development to the ethical implications of AI in business.

“AI is transforming every industry, and leaders need to be equipped with the knowledge to navigate these changes. The AI for the C-Suite program provides an unparalleled opportunity to learn from the best in the field and apply those insights to real-world business challenges,” said Pieter Abbeel, AI Pioneer and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab.

“Berkeley Engineering’s commitment to advancing technology and entrepreneurship is embodied in this program. We are excited to bring together such an esteemed group of faculty to help executives lead with AI,” said David Gallacher, Business Strategy Expert and Industry Fellow at SCET.

About Berkeley SCET

The Sutardja Center for Entrepreneurship & Technology (SCET) at UC Berkeley is a global leader in technology innovation and entrepreneurship education. SCET offers a range of programs that empower professionals and executives to turn innovative ideas into successful ventures. With a focus on hands-on learning and real-world application, SCET is at the forefront of producing the next generation of tech leaders.

Kristina Susac Head of Professional Programs [email protected] https://scet.berkeley.edu/professional-programs/

← Solving swimming: Aqua AI founder Peyton McQueen makes waves

Financial Aid

The Department of Geography strives to support every student admitted to the program, within the constraints of available resources. Support typically includes some work as a teaching assistant or research assistant and some fellowship stipend to allow students time for their own work.

Students with the most successful academic records are nominated for, and generally receive,  multi-year university fellowships  of stipends plus tuition for two years. Combined with department support in the form of teaching or research assistantships and stipends for two more years, these provide four years of support, with a fifth year of stipend for advancing to candidacy within normative time. You can read more about other type’s of fellowships and support on the  Graduate Division Fellowships page .

Other students are typically offered at least one semester of teaching assistantship and one semester of full or partial stipend for the first three years, with a fourth year of stipend for advancing to candidacy within normative time.

In later years, the department and dissertation advisors support students in applying for any national grants for which they are eligible. Over the last ten years, our students have been awarded an average of around $100,000 a year in extramural funding. In addition, we have enough teaching assistantships for advanced students to bridge gaps in fellowship support.

STUDENT PARENTS

If you have children, you are eligible for various forms of aid from the Graduate Division – above and beyond any Department support.

One of the most important is a “Parent Grant” which provides up to $8,000 of additional stipend per academic year for child support. There is also subsidized Family Student Housing and Day Care. For more information and deadlines see the  Financial Support for Student Parents page.

Other Graduate Division resources, including Childbirth Accommodation Funding, are found in the  Families Matter’s Resource Guide .

(DS421) NSF RESEARCH TRAINING PROGRAM

DS421 is an two-year interdisciplinary graduate training program with the mission to prepare a new generation of researchers and policy makers to address challenges at the intersection of natural, social and data sciences by translating data into evidence-based analysis of impacts and solutions.  The DS421 program is open to incoming and first year PhD students from all departments and schools at UC Berkeley. 

The program seeks a diverse cohort of students with a desire to pursue interdisciplinary research addressing coupled human-natural systems with a strong quantitative, data science component.  Applicants should have a background in one or more of the following areas: natural or environmental science, social science, public policy, landscape architecture, environmental planning, statistics, computer science, or related fields.

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Landing Page – Master of Development Engineering (Work In Progress)

Uc berkeley’s development engineering programs.

Development Engineering at Berkeley (DevEng) is an interdisciplinary graduate program that trains the next generation of social change-driven engineers to solve challenges in underserved communities across the globe.

WHAT IS DEVELOPMENT ENGINEERING

Development Engineering as a field merges technology and social impact. It engages with communities to develop scalable and innovative solutions that solve complex challenges globally and locally. Development Engineering differs from traditional engineering by:

• combining social sciences with technical training
• focusing on social impact: designing for sustainability and practicality first
• underscoring the value of failure as a key step in the engineering process
• fostering a deep understanding of the context and culture in which engineers are working
• emphasizing human-centered design and collaboration with the end user communities

Development Engineering Programs

Currently, students may pursue: 

Professional Master of Development Engineering

Designated emphasis in development engineering (phd), other programs.

Other programs that support the students: 

Digital Transformation of Development Fellows Program (DToD)

Professional Pathways

The development sector needs engineers with both strong technical skills and the interdisciplinary background necessary to understand complex local contexts. The engineering world needs a stronger understanding of how to learn from and work in partnership with underserved communities to catalyze change. Many innovative solutions that aim to address the needs of marginalized populations never realize their potential or reach those who would benefit from them most.

Previous Development Engineering Career Pathways include: 

• Program Manager, Energy Storage Systems
• Climate Tech Program Manager
• Global Partnerships Associate
• Technology and Innovation Advisor
• Electrical Design Engineer
• Senior Mechanical Design Engineer
• Technology Architect
• Innovative Health Finance Manager
• Social Impact Project Lead
• Smart Cities Initiatives Intern

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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

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The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.

The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

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The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members.  More information about signing up for classes.

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Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.

The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC) , the Center for Technology, Society & Policy , and the BioSENSE Lab .

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I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni .

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UC Berkeley Named #1 Online Master’s in Data Science

The Master of Information and Data Science (MIDS) at UC Berkeley was ranked #1 among online data science master’s degree programs by  Fortune  magazine, in a report published Wednesday, August 28, 2024. 

This is  Fortune ’s third year providing rankings of the  best online data science master’s programs . On the importance of these rankings,  Fortune  stated that “the employment of data scientists is projected to increase by 35% by 2032, surpassing the average growth rate for all other professions in the nation.” 

Ashwini (Ashwin) Thota, a data science expert who worked with  Fortune  on their ranking, added “If you go to a school that teaches you just  about statistical algorithms and machine learning theory, you’ll get into a situation where you're not ready to code and implement what you learned…If you want to get a job in data science, you need experience solving problems…”

The MIDS program employs an experiential project-based learning curriculum to ensure students get hands-on experience during their time in the degree program. 

“At the UC Berkeley School of Information, we train leaders in the field of data science by maintaining the highest standards of rigor and teaching students to be aware of contextual factors such as ethics, organizational behavior, and responsible research,” Associate Adjunct Professor Paul Laskowski said.

MIDS, which officially launched at the School of Information at UC Berkeley in 2014, was the country’s first fully online master’s degree in data science. MIDS provides a multi-disciplinary curriculum that prepares students in any career to solve real-world problems across their organizations using complex and unstructured data.

“We’ve got a view that data science is more than just sitting and programming—that data science involves issues of identifying the core question that’s coming from business and being able to focus that into the type of question that we can bring an algorithm to solve,” said Alex Hughes, Professor and Head Graduate Advisor for MIDS. 

The MIDS curriculum features a wide range of courses that provide students with a comprehensive understanding of how data science can be used to inform decision making in their organizations. Students complete programming-focused courses in concurrence with courses that focus on the ethical impact of data science and how to effectively communicate results.

The 27-unit  MIDS program is designed for the working professional’s schedule. The program is typically completed in 20 months, but offers flexible options and can be completed on accelerated, standard, or decelerated paths. MIDS students also participate in an in-person or online immersion experience, and complete a culminating capstone project that integrates the core skills and concepts learned throughout the program.

“At the UC Berkeley School of Information, we train leaders in the field of data science by maintaining the highest standards of rigor and teaching students to be aware of contextual factors such as ethics, organizational behavior, and responsible research.”

Ranking Methodology

Fortune  analyzed each school based on  a series of criteria : school-provided data (80%) such as acceptance rate, graduation rate, and more; average annual search volume (15%) based on the average number of times people search for each school on Google during a month; and previous  Fortune online data science master’s rankings (5%).

Earlier this year the Master of Information and Cybersecurity (MICS) was  ranked the #2 online master’s in cybersecurity by  Fortune .  

The Master of Information and Cybersecurity (MICS) program at UC Berkeley ranked #1 among online cybersecurity master’s degree programs by Fortune magazine, released on June 30, 2022.

The Master of Information and Data Science (MIDS) program at the UC Berkeley School of Information ranked #2 among online data science master’s degree programs by Fortune magazine, released on January 19, 2022.

Entrepreneurship

Berkeley Engineering Launches AI Program for Execs

Berkeley, CA, Aug 27, 2024 — On November 11-12, 2024, Berkeley Engineering’s Sutardja Center for Entrepreneurship & Technology (SCET) will launch the AI for the C-Suite program, a premier executive education course designed to equip senior leaders with strategic insights into leveraging artificial intelligence (AI) within their organizations. This in-person AI strategy workshop, held on the UC Berkeley campus, will be led by an exceptional faculty of AI pioneers and industry innovators who have shaped the future of AI.

Revolutionizing Executive Leadership with AI

Berkeley Engineering, consistently ranked among the top engineering schools globally, including the #3 U.S. undergraduate and graduate engineering program, is proud to offer this advanced AI strategy training tailored for C-suite executives. The AI for the C-Suite program is a unique opportunity for senior leaders to engage directly with world-renowned AI experts and gain actionable insights to drive AI transformations in their businesses.

World-Class Faculty Leading the Program

Participants will have the chance to learn from Berkeley’s distinguished faculty, including:

Pieter Abbeel – AI Pioneer

Director of Berkeley Robot Learning Lab

Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab

Professor, Berkeley Electrical Engineering & Computer Sciences (EECS)

David Gallacher – Business Strategy Expert

Industry Fellow, UC Berkeley College of Engineering, SCET

Stuart Russell – AI Thought Leader

Distinguished Professor, UC Berkeley Computer Science

Smith-Zadeh Professor in Engineering

Anca Dragan – Robotics & AI Researcher

Associate Professor, Electrical Engineering & Computer Sciences (EECS)

Director of AI Safety and Alignment, Google DeepMind

Ali Ghodsi – AI Entrepreneur

CEO & Co-Founder, Databricks

Adjunct Professor, UC Berkeley Electrical Engineering & Computer Sciences (EECS)

Brandie Nonnecke – AI Ethics Specialist

Director, CITRIS Policy Lab

Associate Research Professor, Goldman School of Public Policy

Ion Stoica – Cloud Computing Innovator

Executive Chairman, Databricks

Director, Sky Computing Lab

Professor, UC Berkeley Computer Science

Dominique Shelton Leipzig – AI, Privacy & Cybersecurity Expert

Partner, Cybersecurity & Data Privacy Practice, Mayer Brown

Founder, Co-Chief Executive Officer, NxtWork

Empowering the Next Generation of AI Leaders

UC Berkeley is ranked #1 globally by Pitchbook for producing venture-backed startups, a testament to its deep commitment to innovation and entrepreneurship. At the heart of this success is Berkeley’s Sutardja Center for Entrepreneurship & Technology (SCET), which has been instrumental in nurturing groundbreaking startups and fostering an entrepreneurial mindset. The AI for the C-Suite program reflects this mission by empowering executives to lead their organizations through AI-driven transformations, equipping them with the tools and frameworks needed to develop and implement strategic AI initiatives.

What Makes This Program Unique?

This executive AI course is not just about learning AI concepts; it’s about applying them to business strategy. With hands-on workshops and direct access to industry-leading AI practitioners, participants will gain practical insights that can be immediately applied to their organizations. The program covers everything from AI strategy development to the ethical implications of AI in business.

“AI is transforming every industry, and leaders need to be equipped with the knowledge to navigate these changes. The AI for the C-Suite program provides an unparalleled opportunity to learn from the best in the field and apply those insights to real-world business challenges,” said Pieter Abbeel, AI Pioneer and Co-Director of the Berkeley Artificial Intelligence (BAIR) Lab.

“Berkeley Engineering’s commitment to advancing technology and entrepreneurship is embodied in this program. We are excited to bring together such an esteemed group of faculty to help executives lead with AI,” said David Gallacher, Business Strategy Expert and Industry Fellow at SCET.

About Berkeley SCET

The Sutardja Center for Entrepreneurship & Technology (SCET) at UC Berkeley is a global leader in technology innovation and entrepreneurship education. SCET offers a range of programs that empower professionals and executives to turn innovative ideas into successful ventures. With a focus on hands-on learning and real-world application, SCET is at the forefront of producing the next generation of tech leaders.

Kristina Susac Head of Professional Programs [email protected] https://scet.berkeley.edu/professional-programs/

The post Berkeley Engineering Launches AI Program for Execs appeared first on UC Berkeley Sutardja Center .

Electrical Engineering & Computer Sciences PhD

The Department of Electrical Engineering and Computer Sciences offers three graduate programs in Electrical Engineering: the Master of Engineering (MEng) in Electrical Engineering and Computer Sciences, the Master of Science (MS), and the Doctor of Philosophy (PhD).

Master of Engineering (MEng)

The Master of Engineering (MEng) in Electrical Engineering & Computer Sciences, first offered by the EECS Department in the 2011-2012 academic year, is a professional masters with a larger tuition than our other programs and is for students who plan to join the engineering profession immediately following graduation. This accelerated program is designed to train professional engineering leaders who understand the technical, economic, and social issues around technology. The interdisciplinary experience spans one academic year and includes three major components: (1) an area of technical concentration, (2) courses in leadership skills, and (3) a rigorous capstone project experience.

Master of Science (MS)

The Master of Science (MS) emphasizes research preparation and experience and, for most students, provides an opportunity to lay the groundwork for pursuing a PhD.

Doctor of Philosophy (PhD)

The Berkeley PhD in EECS combines coursework and original research with some of the finest EECS faculty in the US, allowing students to prepare for careers in academia or industry. Our alumni have gone on to hold amazing positions around the world.

Contact Info

[email protected]

215 Cory Hall

Berkeley, CA 94720

At a Glance

Admit Term(s)

Application Deadline

December 9, 2024

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

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UW-Milwaukee Academic Catalog

Computer science.

Computer Science, PhD

The Doctor of Philosophy, the highest degree offered by the University, is conferred in recognition of marked scholarship in a broad field of knowledge as well as distinguished critical or creative achievement within a special area of the general field (the special area being the subject of the doctoral dissertation). The Doctor of Philosophy (PhD) in Computer Science program in the College of Engineering and Applied Science (CEAS) is designed to meet the traditional high standards for such programs. The PhD in Computer Science is administered by the division of Computer Science in the department of Electrical Engineering and Computer Science. Some aspects of the program are delegated to the CEAS Graduate Office.

The program is flexible, allowing the student to develop a plan of studies tailored to meet individual needs. Evaluation of the study plan is based on its appropriateness as a computer science program, the availability within the University of appropriate course offerings, and the availability within the division of Computer Science of a faculty member who is qualified to serve as the student’s major professor.

The PhD degree requires a minimum of 66 credits beyond the baccalaureate, including a dissertation. The student must also satisfy a residence requirement.

Many of the courses leading toward graduate degrees in CEAS are offered in the late afternoon or evening. So, students can complete much of their coursework on a part-time basis.

Admission Requirements

Credits and courses, additional requirements, application deadlines.

Application deadlines vary by program, please review the application deadline chart for specific programs. Other important dates and deadlines can be found by using the One Stop calendars .

An applicant must meet  Graduate School requirements  plus these program requirements to be considered for admission to the program:

• Applicants holding a MS degree in computer science will generally be admitted without deficiencies. Applicants holding a BS degree in computer science may be admitted only if they are exceptionally strong, such as with a record including successful completion of courses normally taken at the graduate level in computer science.
• Applicants holding MS degrees from domains outside of computer science may be admitted with specific program-defined course deficiencies, provided that the deficiencies amount to no more than two courses. The student is expected to satisfy deficiency requirements within three enrolled semesters. The deficiencies are monitored by the Graduate School and the division of Computer Science. No course credits earned in making up deficiencies may be counted as program credits required for the degree. The mathematics preparation must generally include mathematics equivalent to MATH 231 . Otherwise, the made-up deficiencies must be sufficient to assure that the applicant is able to proceed with advanced work directed toward the doctoral degree.
• A minimum grade point average of 3.0 on the basis of 4.0, in the highest degree granted. An applicant with a master’s degree in engineering or computer science having a GPA of less than 3.0, but at least equal to 2.75, may be admitted if substantial evidence can be submitted demonstrating that the applicant has the capacity to perform satisfactory doctoral work.
• All applicants are required to submit a brief (1 or 2 page) statement describing their professional goals and at least two letters of reference.
• The Graduate Record Examination (GRE) is required for all international and domestic applicants.
• International students require proof of English language proficiency. Complete information is available at the  UWM Center for International Education .
• Applicants with a relevant master’s degree who intend to complete an additional master’s in Computer Science at UWM should announce their plans at the time of admission, and not later than the start of their second year into the PhD program.

Reapplication

A student who receives a master’s degree at UWM must formally apply for admission to the Graduate School as a doctoral student before continuing studies that will be credited toward the Doctor of Philosophy in Computer Science.

The minimum degree requirement is 66 graduate credits beyond the bachelor’s degree. The minimum credit  distribution of coursework to be undertaken must be as follows depending on the option selected.

The 6-credit requirement in mathematics and/or quantitative methods may be met by satisfactorily completing certain courses specified by the Department or by taking the minor in mathematics. When such courses also count for either the major or the minor area, the remaining credits may be taken as approved electives.

The student must achieve a 3.0 GPA separately in each of the following areas: the major area, the minor area, and the quantitative methods area.

The minor is normally in another area offered in the College or in the physical sciences or mathematics or in management sciences. Consideration of any other area as a minor requires the prior approval of the Department.

A minimum of 26 credits, excluding doctoral thesis, must be at the 700 level or higher.

Major Professor as Advisor

The Graduate School requires that the student must have a major professor to advise, supervise, and approve the program of study before registering for courses. The incoming student will be assigned to an initial Program Advisor at the time of admission. Prior to the completion of 12 credits (9 credits for part-time students), the student must select a major professor who will be the student’s thesis advisor. The student, in consultation with the major professor, develops a proposed program of studies which is submitted for approval. For subsequent changes, the student must file a revised program of study for approval.

Foreign Language

There is no foreign language requirement for the degree.

The program residence requirement is satisfied either by completing 8 or more graduate credits in two consecutive semesters, exclusive of summer sessions, or by completing 6 or more graduate credits in each of three consecutive semesters, exclusive of summer sessions.

Qualifying Examination

Each student in the program must take and pass a Qualifying Examination to demonstrate that the student is qualified for doctoral-level work. The Qualifying Examination is a written exam and is structured in two parts: Part 1 and Part 2. The examination is offered twice a year during the regular academic year. 

Students entering with only a bachelor’s degree or with a master’s degree in an area unrelated to their major may take the Qualifying Examination for the first time after earning 12 credits of graduate work at UWM and must successfully pass the exam before earning 30 credits of graduate work at UWM.

Students admitted after completing an appropriate master’s degree must take this examination no later than the semester immediately after 18 credits of graduate work have been earned at UWM.

A student may take the Qualifying Examination twice. On the first attempt, the student must attempt both Part 1 and Part 2 of the examination.

• If the student passes both parts, then the student has passed the entire examination and will be permitted to proceed toward the Doctor of Philosophy degree.
• If the student fails both parts, then the student must take the entire exam again at its next offering.
• If a student passes only one of the two parts, then the student must take the examination again at its next offering, but may choose to take only the part of the examination that was not passed on the first attempt.
• If a passing grade is not obtained on the second attempt of the Qualifying Examination, the student will not be permitted to proceed toward the Doctor of Philosophy degree.

A student who fails the qualifying exam twice is subject to dismissal from the PhD in Computer Science program. A student may appeal the failure and dismissal within 30 days of being notified of the failure. If the student does not appeal or the appeal is not granted, the College will recommend to the Graduate School that the student be dismissed. A student who is dismissed from the PhD in Computer Science program because of failing the qualifying exam may not be enrolled in the PhD in Computer Science program for a complete calendar year. This does not preclude the student from being enrolled in any other degree program offered by the University. A student who wishes to re-enroll in the program after a calendar year has passed must apply as any other student would, including payment of fees. A student readmitted after having failed the qualifying exam twice must take the qualifying exam in the first semester of matriculation and this will count as the student’s first attempt at the exam. The student may appeal this requirement prior to the first scheduled day of classes. If the student fails the qualifying exam on this first attempt, the student is permitted the customary second attempt as described above. All appeals must be in writing and directed to the CEAS Associate Dean for Academic Affairs.

Doctoral Program Committee

The Doctoral Program Committee is proposed by the major professor in consultation with the student and the department. The Committee must include at least five graduate faculty (three from major area, one from minor area, and one from any area, including the major and minor areas). The last member may be a person from outside the University (such as another university, a research laboratory, or a relevant industrial partner), provided that person meets Graduate School requirements. The Committee may have more than five members, provided that the majority of the Committee members are from the student’s major field.

Doctoral Preliminary Examination

A student is admitted to candidacy only after successful completion of the doctoral preliminary examination conducted by the Doctoral Program Committee. This examination, which normally is oral, must be taken before the completion of 48 credits of graduate work toward the Doctor of Philosophy degree in Computer Science and should be taken within the first seven years in the program. Prior to the examination, the student must present a proposal for a doctoral dissertation project. The examination may cover both graduate course material and items related to the proposed dissertation project.

Dissertation and Dissertator Status

The student must carry out a creative effort in the major area under the supervision of the major professor and report the results in an acceptable dissertation. The effort of the student and the major professor to produce the dissertation is reflected in the PhD in Computer Science program requirement that the student complete at least 18 credits of doctoral thesis. 

After the student has successfully completed all degree requirements except the dissertation, the student may enter Dissertator Status. Achieving Dissertator Status requires successful completion of the Doctoral Preliminary Examination and prior approval of the student’s advisor, the Doctoral Program Committee, and the Computer Science GPR of a dissertation proposal that outlines the scope of the project, the research method, and the goals to be achieved. Any proposal that may involve a financial commitment by the University also must be approved by the Office of the Dean. After having achieved Dissertator Status, the student must continue to register for 3 credits of doctoral thesis per semester during the academic year until the dissertation is completed.

Dissertation Defense

The final examination, which is oral, consists of a defense of the dissertation project. The doctoral defense examination may only be taken after all coursework and other requirements have been completed. The student must have Dissertator Status at the time of the defense.

All degree requirements must be completed within ten years from the date of initial enrollment in the doctoral program.

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