university of california berkeley online phd computer science

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I School Online UC Berkeley School of Information I School Online UC Berkeley School of Information I School Online UC Berkeley School of Information

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Welcome to the UC Berkeley School of Information Online: vibrant, imaginative, and determined to transform the world using the power of information.

We are a community of scholars, practitioners, and students working together to advance knowledge everywhere people interact with information and digital technologies. We are committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

Always at the forefront of creativity and problem solving, the UC Berkeley School of Information has offered online programs since 2014. At the I School, online students become full members of our community and contribute to asking hard questions in order to find innovative solutions — from wherever they live.

“UC Berkeley is one of the top universities in the world, and it is absolutely a premiere institution for high-level teaching and high-level research. That’s what students really gain by taking courses with professors, the faculty, and the affiliates here. We are experts in each of our own areas, and we’re committed to not just going out and doing cutting-edge research, but being able to teach that — being able to convey that to others so they can go out and solve real problems.”

— Coye Cheshire, I School Professor

Explore Our Online Graduate Programs

Our online programs evoke curiosity, encourage imaginative thinking, and teach advanced technical skills. Students learn to design and build the information systems that will shape the way humans live and interact in the future.

Master of Information and Data Science (MIDS)

Learn to ask complex, relevant questions of data; use the latest tools and analytical methods to derive insights from complex and unstructured data; and drive solutions that have a real impact on society.

LEARN MORE ABOUT MIDS

Master of Information and Cybersecurity (MICS)

Gain hands-on practical experience using secure systems and applications; develop an understanding of the legal requirements associated with cybersecurity; and learn to lead, manage, and contribute to building cybersecurity solutions.

LEARN MORE ABOUT MICS

Are You a Forward Thinker?

Learn more about our programs and join us in transforming the world using information.

Online Graduate Programs

Learning Experience

I School Advantage

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Engage in a Unique Learning Experience

The online learning experience provides students with the opportunity to earn a UC Berkeley School of Information education from wherever they are.

The Virtual Campus  hosts everything students need to succeed in one place. Students have one-click access to their live classes, upcoming assignments, grades, and faculty office hours. All resources are also available on a mobile app.

Live Online Classes  on the Zoom HD video platform bring classmates and I School faculty face-to-face weekly. Students collaborate and participate in discussions using features such as document and video sharing, collaborative annotations, polling functionality, and small-group breakout rooms.

Immersive Course Work  can be completed on students’ own time in between their weekly classes. All course work is designed specifically for the online learning environment by UC Berkeley I School faculty and world-class course developers. This content includes dynamic videos, interactive case studies, self-paced recorded lectures, and collaborative activities that foster teamwork.

In-Person Immersions  are three- to four-day experiences that bring together classmates, instructors, and industry leaders to engage in discussions surrounding the future of the information field. In addition to forming closer connections with their peers, students have the opportunity to listen to groundbreaking presentations, network with professionals from top companies in the field, and participate in career advancement workshops.  *Due to COVID-19, immersion experiences are currently being held virtually.

Learn more about the full student experience.

A Personalized Experience

Students have the ability to personalize the virtual campus and course work to align with their unique learning styles. Features that create an individualized learning experience include dark mode for studying at night, varying video speeds for watching asynchronous course content, closed captioning, searchable video transcripts, streaming capabilities for viewing on a larger screen, and a mobile application for learning on the go.

Student Support

Support begins the moment students request information about our programs and continues beyond graduation. Our admissions team answers program-related questions, provides guided tours of the online classroom, and hosts informational webinars on topics like financial aid and application tips. Once students are enrolled, they are assigned a success adviser and continue to receive academic, tech, and career support.

Join a Community of Aspiring Leaders in Information

Request more information to learn what it’s like to be a UC Berkeley School of Information student.

The I School Advantage

The UC Berkeley School of Information attracts the best and brightest minds from across the world. Through our programs’ interactive online platform and in-person experiences, students are presented with many opportunities to develop close relationships with faculty, classmates, I School alumni, and industry leaders.

Accessible Faculty

Our faculty consist of tenured professors, adjunct professors, leading industry practitioners, lecturers, and postdoctoral scholars who are dedicated to shaping the next generation of information trailblazers. Faculty members are committed to remaining accessible, sharing cutting-edge research, and instilling curiosity, drive, and an advanced skill set in their students.

A Diverse Network

The I School’s online students collaborate with classmates who come from diverse professional backgrounds and learn from many unique perspectives on the world of information. While enrolled and long after graduation, students have access to the renowned UC Berkeley global alumni network of talented data and information professionals.

Far-Reaching Resources

Our proximity to Silicon Valley and the greater Bay Area gives students access to some of the brightest minds in information and tech. We are also home to the Center for Long-Term Cybersecurity (CLTC) and support its mission of developing a deeper and broader understanding of information technology security. The CLTC supports extensive research and an open dialogue between interdisciplinary experts.

Personalized Career Support Services

Our career services team helps students apply what they have learned and maximize the value of their degree outside of the classroom. We provide one-on-one coaching to help students tailor their strategy at every step, including:

• Forming career plans that help students reach their goals
• Researching organizations that fit their vision
• Developing plans to advance in their role at their current organization
• Networking in their chosen field
• Preparing for interviews
• Conducting interview postmortems
• Navigating the offer stage and negotiation process

Become Part of Our Ambitious Student Community

Our online master’s programs attract intelligent, creative information professionals who work for top companies in computer software, finance, and health care. Students learn alongside other professionals located all over the world who are balancing their course work with demanding careers and personal commitments.

“UC Berkeley offers incredible class diversity: Throughout the MIDS program, I worked with — and learned from — students located all over the world. My classmates also worked in fields vastly different from my own, which allowed me to gain perspective into areas well outside of my comfort and expertise.”

– Sombiri Enwemeka, engineering contractor, Long Beach, CA

Experience UC Berkeley, No Matter Where You Are

Since I School Online students attend classes and complete course work online, the program can be completed from anywhere in the world. Located in the San Francisco Bay Area, the UC Berkeley School of Information is at the forefront of innovation in the field of information, and our students gain connections to our vibrant network across the globe.

The map displays countries of residence for students enrolled in our online programs for the 2019–2020 academic year, including: Argentina, Australia, Bermuda, Brazil, Cambodia, Canada, Chile, China, Colombia, Denmark, Germany, Great Britain, Hong Kong, India, Italy, Japan, Kenya, Lebanon, Luxembourg, Mexico, Norway, Pakistan, Qatar, Russia, Singapore, South Korea, Spain, Taiwan, Thailand, Uganda, United Arab Emirates, United States, and Virgin Islands.

We’re seeking applicants who can make a positive impact on the I School community and beyond. To complete an application, submit the following:

Online application Official transcripts from all educational institutions attended Statement of Purpose and additional admissions statements Two professional letters of recommendation Current resume TOEFL scores (if applicable) Application fee Optional:  GRE or GMAT scores

Learn more about admissions requirements.

Let’s Discover the Information Solutions of Tomorrow

Complete the form to get connected with an Admissions Counselor.

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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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• Information Retrieval & Search
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• Social & Cultural Studies
• Technology for Developing Regions
• User Experience Research

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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The Goldman School of Public Policy, the CITRIS Policy Lab, and the School of Information hosted the inaugural UC...

Dr. Diag Davenport has been appointed as an assistant professor at UC Berkeley as part of a joint search in...

At the Women in Data Science conference held at UC Berkeley this past week, four educators affiliated with the...

When the Bancroft Library received over 100,000 Japanese-American internment “individual record” forms (WRA-26) from...

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Ph.D. Admissions

Next start date: August 2025

Application Deadline: December 4, 2024, 8:59 pm PST

We welcome students from a diverse set of backgrounds; some will be technically educated, some educated in the humanities and social sciences.

All application materials must be received by the deadline. We encourage you to apply early. The I School’s Ph.D. program does not accept applications for spring term admissions.

Admissions Requirements

• A bachelor’s degree or its recognized equivalent from an accredited institution
• Superior scholastic record, normally well above a 3.0 GPA
• Indication of appropriate research goals, described in the Statement of Purpose
• For applicants whose academic work has been in a language other than English, the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS)
• Not required: GRE/GMAT . Starting Fall 2021, we no longer require the GRE or GMAT. We recommend you put your time and effort towards the required application materials. Read more about our decision to drop the GRE/GMAT requirement .

Selection Criteria

The I School accepts 3–7 Ph.D. students each year from more than 100 applications. Applications are reviewed by a committee of faculty.

Applicants are evaluated holistically on a number of factors. A strong academic record is important, but not sufficient. A critical factor is the ability to demonstrate a research record and agenda that fit well with specific I School faculty. In a small, interdisciplinary program, it is important that applicants clearly indicate in their Statement of Purpose which faculty member(s) they are interested in researching with, and why.

Application Requirements

We encourage you to check out our Ph.D. Admissions FAQ for information about commonly asked application questions.

(1) Statement of Purpose & Personal History Essay

The Statement of Purpose and Personal History are two separate essays.

The Statement of Purpose should describe your aptitude and motivation for doctoral study in your area of specialization, including your preparation for this field of study, your academic plans and research interests, and your future goals. Please be sure to identify in your Statement of Purpose which faculty member(s) you are interested in researching with, and why. We expect that candidates are able to demonstrate a research record and agenda that fit well with specific I School faculty.

For additional guidance, please review the Graduate Division's Statement of Purpose Guide .

In addition to explaining how your personal experiences have influenced your decision to pursue graduate studies, your Personal History Essay may include any relevant information describing barriers to accessing higher education that you have overcome, efforts you have made to advance equitable access to higher education for women, racial minorities, and other groups historically underrepresented in higher education, or research that you have undertaken that focuses on underserved populations or related issues of inequality.

For additional guidance, please review the Graduate Division’s Personal Statement Guide . There is no minimum length for the Personal History Essay.

These two essays are used in part to evaluate the candidate’s writing skills. Pursuant to UC Berkeley policy, the statements must be written by the candidate her or himself. For admitted students, application materials must comply with the Code of Student Conduct .

Both essays should be uploaded as PDF documents, as part of the online application .

(2) Three Letters of Recommendation

Ph.D. applicants should provide letters which speak directly to their ability and potential to perform academic research at the doctoral level. Recommenders must submit their letters online; please follow the instructions in the online application .

(3) Current Curriculum Vitae

Please upload a current curriculum vitae (C.V.) as a PDF document as part of the online application .

(4) College Transcripts

As part of the online application, upload copies of the official transcripts or academic records for all university-level studies you have completed abroad and at U.S. institutions. Be sure to include a current transcript from every post-secondary school that you have attended, including community colleges, summer sessions, and extension programs.

Each transcript should be uploaded as a separate PDF document; please refer to the instructions on the online application .

Applicants who completed their undergraduate degree in a recognized academic institution outside the United States are required to upload a copy of their degree conferral certificate. If a degree conferral certificate has not yet been obtained, please upload a provisional certificate. Applicants who have not yet graduated from undergrad are not required to submit a provisional certificate at this time. For specific questions, please contact the School of Information at [email protected] .

(5) TOEFL or IELTS Scores

UC Berkeley Graduate Division requires that applicants who received their degrees in countries other than the U.S., U.K., Australia, or English-speaking Canada submit TOEFL (Test of English as a Foreign Language) or IELTS (International English Language Testing System) scores. This includes applicants with degrees from Bangladesh, Burma, Nepal, India, Pakistan, Latin America, the Middle East, North Africa, the People’s Republic of China, Taiwan, Japan, Korea, Southeast Asia, and most European countries. Only applicants who have completed a full year of U.S. university-level coursework with a grade of B or better are exempt from this requirement.

For students taking the TOEFL, UC Berkeley Graduate Division requires that your most recent score be at least 90 on the Internet-based version of the TOEFL.

For students taking the IELTS, UC Berkeley Graduate Division requires that your most recent score be at least 7.0 out of 9.0 on the IELTS Academic test.

UC Berkeley Graduate Division does not accept TOEFL ITP Plus for Mainland China, IELTS Indicator, or Duolingo scores. For more information, see  Graduate Division’s Evidence of English Language Proficiency .

Submitting Scores

To be valid, the TOEFL or IELTS must have been taken within the past 18 months: for applicants for Fall 2025 admission, test scores taken before June 2023 will not be accepted. Please have your test scores sent directly to UC Berkeley by the testing authorities prior to application submission, and at the latest, by the application deadline. It may take 10-15 days for official score reports to transfer to our system. For the TOEFL exam, the school code for UC Berkeley is 4833, and the department code for the I School is 99.

For the IELTS exam, please submit an electronic report from the testing center; no institution code is required. Here is the Graduate Division’s office address for identification purposes: University of California, Berkeley, Graduate Division, Sproul Hall Rm 318, MC 5900, Berkeley, CA 94720.

More information: TOEFL website ; IELTS website

(6) Application Fee

(submitted with the online application)

• Fee for domestic applicants: $135.
• Fee for international applicants: $155.

Application Fee Waiver : The I School is pleased to offer application fee waivers to eligible Ph.D. applicants. Prior to submitting your application, please complete our Application Fee Waiver request form , and we will contact you within two business days with further instructions.

All application materials must be received by the application deadline. Applications will be reviewed throughout December and January, and admissions decisions will be released by early February.

Please don’t hesitate to contact us with questions or for additional guidance: [email protected] or (510) 664-4742.

*Test Report Form must be sent directly from IELTS. IELTS Indicator scores are not accepted.

Computer Ownership Requirement

We require that students own a computer. No particular configuration or operating system is required. However, students will be expected to complete assignments using office productivity software (e.g., Microsoft Office, OpenOffice, etc.), web browsers, etc., and should own a computer capable of running such software. More specific guidance will be provided upon acceptance to the program.

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Contact the admissions team with questions about the Ph.D. program or application.

Ph.D. Applicant Feedback Program

The I School Ph.D. Applicant Feedback Program is a student-run initiative that aims to assist underrepresented students with their application essays and C.V. as they apply to the UC Berkeley School of Information Ph.D. program.

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Theory at Berkeley

This is the homepage of the Theory Group in the EECS Department at the University of California, Berkeley .

Berkeley is one of the cradles of modern theoretical computer science. Over the last thirty years, our graduate students and, sometimes, their advisors have done foundational work on NP-completeness, cryptography, derandomization, probabilistically checkable proofs, quantum computing, and algorithmic game theory. The mild weather, celebrated eateries (see here and here ), and collaborative atmosphere are known to be conducive to great theory-building and problem-solving.

In addition, Berkeley's Simons Institute for the Theory of Computing regularly brings together theory-oriented researchers from all over the world to collaboratively work on hard problems. The institute organizes a sequence of programs based on topics (see current & future programs and past ones ), supported by workshops (see current & future workshops and past ones ) and other events.

On Wednesdays, our group comes together for Theory Lunch , an event featuring an informal lunch followed by a whiteboard presentation; this allows for much mingling, including with our friends from Statistics and Math (and, occasionally, Physics and Chemistry). On Fridays, TGIF, the informal student seminar that is off-limits to faculty, provides a comfortable space for students to learn about each other's work.

Some of our current focus is on using computation as a lens to the sciences . Like probabilistic thinking in the last century, computational thinking will give mathematics and, more generally, science a new language to use and the ability to formulate new fundamental questions. We are studying the applications of theoretical computer science in many sciences, including economics (with our work on computational game theory and mechanism design), physics (with our work on random structures and quantum computing), biology, and pure mathematics (especially geometry, functional analysis, and additive number theory). The core problems in algorithms, compexity theory, and cryptography remain, of course, dear to our hearts.

If you would like to join Berkeley's EECS Department as a graduate student, apply to our Ph.D. program . If you are interested in postdoc opportunities at UC Berkeley to work with the theory group, click here .

• Theory Lunch on Wednesdays, 12:00-13:00, Wozniak Lounge
• Theory Seminar on (most) Mondays, 16:00-17:00, Wozniak Lounge
• TGIF on Fridays, 15:30-17:00, Theory Lounge

Undergraduate Courses

• CS 170: Efficient Algorithms and Intractable Problems
• CS 172: Computability and Complexity
• CS 174: Combinatorics and Discrete Probability
• CS 191: Qubits, Quantum Mechanics, and Computers
• CS 191: Quantum Information Science And Technology
• CS 194: Undergraduate Cryptography

Graduate Courses

• in Spring of 2023 (Jelani Nelson)
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Affiliated Faculty

• Venkat Anantharam
• Thomas Courtade
• Jiantao Jiao
• Martin Wainwright
• Abhishek Jain
• Meryem Essaidi
• William Hoza
• Michael Kim
• Andrea Lincoln
• Sai Sandeep
• Hsin-Po Wang
• Ishaq Aden-Ali
• Omar Alrabiah
• James Bartusek
• Thiago Bergamaschi
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• Meghal Gupta
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• Bhaskar Roberts
• Jonathan Shafer
• Abhishek Shetty
• Sriram Sridhar
• Francisca Vasconcelos
• Elizabeth Yang
• Yinuo Zhang

Recent alumni

• Jonah Brown-Cohen
• Arun Ganesh
• Fotis Iliopoulos
• Marc Khoury
• Jingcheng Liu
• Pasin Manurangsi
• Peihan Miao
• Chinmay Nirkhe
• Aaron Schild
• Nick Spooner
• Akshayaram Srinivasan
• Qiuyi Zhang

Best Computer Science Schools

Ranked in 2024, part of Best Science Schools

Earning a graduate degree in computer science can lead

Earning a graduate degree in computer science can lead to positions in research institutions, government agencies, technology companies and colleges and universities. These are the top computer science schools. Each school's score reflects its average rating on a scale from 1 (marginal) to 5 (outstanding), based on a survey of academics at peer institutions. Read the methodology »

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10 Most Affordable PhD in Computer Science Programs Online 2024

Find your perfect school.

Author: Josh Davidson Reviewed by: Melissa Anderson Reading Level: Grade 9 Reading Time: 11 minutes, 37 seconds Original Publication Date: February 2019 Updates: 2

Pursuing an affordable online PhD in computer science opens up exciting possibilities. According to the U.S. Census Bureau’s “Educational Attainment in the United States 2017” report, less than 3% in the US have a doctorate. Obtaining your Ph.D. can elevate you to the top of your field. If you want to be a cut above the rest, have opportunities in the newest technologies, and use your expert abilities to grow and work in the field you love, completing a Ph.D. in computer science online is right for you.

There are many areas of specialty in the computer sciences. One of the newest and trending is in artificial intelligence. With a doctorate in CS, there is an opportunity to work in this field to ensure the safest and most reliable research and development. If you desire to teach computer science at the college level, obtaining this Ph.D. is often required, and you can get a job in almost any technological school. With an online doctorate of CS, you will be researching the most advanced topics in the field. Many US Ph.D. in computer science degrees are taken online due to the independence it allows working professionals. The programs are comparable to the courses taken at a brick-and-mortar college, and you leave with a Ph.D. from the college providing online learning.

Featured Programs

Degrees Included in This Ranking:

Online Ph.D. in Information Systems

Ph.d. in computer science online, online doctor of philosophy (ph.d.) in technology.

• Online Computer and Information Science Ph.D
• Online Electrical and Computer Engineering PhD
• Ph.D. Online in Computer Science and Engineering
• Online Ph.D. in Information Technology

Ranking the 10 Most Affordable Online Computer Science PhD Programs

This list comprises the 10 Most Affordable Ph.D. in Computer Science Programs Online. Colleges are listed by tuition. Tuition numbers were taken from the NCES College Navigator tool. Only universities with a PhD in Computer Science with an online option were considered. Consider the following online PhD programs in computer science:

Note: This ranking was originally published in February 2019 and was last updated in September 2023. The 2023 updates may affect the ranking order, but the list has not been re-ordered.

#10 University of South Carolina

Columbia, sc.

Tuition : $13,374

The College of Engineering and Computing was established in 1961 and offers many degrees in many different technological facets. Graduates can have careers in many sectors. Some go on to be entrepreneurs, others work in cyber-security, while others work with artificial intelligence, and the list goes on. Many of the projects and research performed in this Ph.D. course are supported by government agencies or are collaborative efforts with local industries. The University of South Carolina is one of the top 25 graduate programs according to national publications such as US News and World Report.

• Top-ranked university
• Robust tech support for online learners
• Affordable tuition
• Less reputable computer science department
• Less diverse student body

#9 University of Rhode Island

Kingston, ri.

Tuition : $14,822

URI’s Ph.D. in computer science department serves undergraduates and postgraduates, including PhDs. The Ph.D. in computer science online is a research degree that provides the opportunity to complete a major research project that enhances the field of computer science. Their curriculum provides the expertise needed for a career in research-based innovation. Graduate courses are offered at convenient times for professionals. Students in the Ph.D. program typically conduct a major research project with one of the university’s research groups to produce new intellectual contributions to the computer science field.

• Highly ranked graduate programs in computer science
• Scheduling flexibility
• 24/7 tech support for distance learners

#8 University of California, Berkeley

Online phd computer science, berkeley, ca.

Tuition : $11,700

The University of California was founded in 1868. It has an impressive list of academic achievements and rankings. In the new rankings, Berkeley’s graduate programs placed first in the world from US News and World Report, including their Ph.D. program in computer science. Berkeley graduates have gone on to achieve high endeavors such as 20 Nobel Prizes, 30 recipients of the National Medal of Science and over 250 founders of companies. Graduate students represent 92 countries and all 50 states.

Berkeley’s EECS graduate programs have been ranked first and second in the US for excellence.

Berkeley’s graduates are highly satisfied with their education, and as a result, rank them very high on rating scale:

• 90% overall (and 85% of those who pursued non-academic careers) say they were well prepared for their careers by Berkeley
• 95% overall (and 93% of those who pursued non-academic careers) would pursue a doctoral degree again
• 86% would select the same field of study
• 95% would choose graduate study at Berkeley if they could start again
• High student satisfaction rates
• Top-ranked graduation programs
• Prestigious university
• More competitive

#7 Capitol Technical University

Tuition : $11,340

Capitol Technology University is a STEM-focused institution of higher education providing undergraduate and graduate degrees in engineering, information sciences, and technology leadership. CTU’s degrees offer flexibility with opportunities to grow and adapt to emerging workforce needs.

Capitol Technical University has three Ph.D. programs focusing on technology. Cybersecurity (DSc), technology Ph.D., and a technology combination program MS/Ph.D. (a unique program that offers a combination of a Ph.D. in technology and a master of science in research methods.)

Graduates will be positioned to contribute significantly to their fields by creating new knowledge and ideas. They’ll learn a skill set that will give them the tools to easily research and publish findings and present them in an accurate and professional manner.

Students work with Capitol Technical University first, then work independently on a research topic of publishable quality. They will gain knowledge in legal, political ethics and social aspects of their field. Some of CTU’s graduates are employed at government agencies and large corporations. Others have started their own tech companies. Out of hundreds of top-notch employers, a few standouts are the Department of Defense, Honeywell, and Lockheed Martin.

• Dual degree programs available
• Flexible curriculum
• Lower graduation rate
• Less prestigious university

#6 Indiana University Bloomington

Bloomington, in.

Tuition : $10,033

Maybe you immediately think of Hoosiers and basketball when you hear Indiana University. Along with that notoriety, Indiana University boasts of many other diverse achievements. Crest toothpaste was developed by three IU researchers. Olympic gold medalist Mark Spitz came from IU, as did Nobel prize winners Mark Cuban (American businessman and owner of the Mavericks basketball team), a dozen pro-sports players, and actors and actresses like Oscar-winning actor Steve Tesich.

This University has a computer department that is very competitive with other universities. Indiana University has a world-class faculty with expertise in foundations such as algorithms, programming languages, parallel and distributed systems, cloud computing, networks, hardware, data mining, machine learning, intelligent systems, and security. The cross-disciplinary approach exposes you not only to the latest research in high-performance computing, data and search, artificial intelligence, and computer security, but also gives you the opportunity to apply those insights to real-world problems, from controlling pandemic disease to tracking the effects of climate change on polar ice.

• Affordability
• High graduation rate
• Less diverse

#5 National University

San diego, ca.

Tuition : $15,912

Northcentral University’s Ph.D. in technology and innovation management, specializing in computer science, is a 100% online program. Its specialization in teaching how a computer functions from the inside out, giving the inside track to understand the foundational technology in any information system fully. The NCU Ph.D. program students are mentored by PhDs who are specialists in the field because they are 100% doctoral faculty. Students also receive practical experience by being in the online classroom with exposure to the research community and, in turn, receive chances to present at conferences and seminars.

This is one of the most flexible universities we reviewed, offering weekly course starts, no scheduled lecture hours, no group assignments, weekly assignments, and the ability to schedule courses around a student’s personal and professional obligations. There is not a requirement to start at the beginning of a semester. North Central University does not require students to be on campus at all. This makes it easy for working professionals, as well as for military personnel and international students who will not have to relocate. North Central’s programs provide students with the opportunity to partner with a member of their 100% doctoral faculty in each of the courses. During the online Ph.D. in the computer science program, the student focuses their research on contributing new knowledge and theory to the body of knowledge in their field. North Central University offers two programs in computer science. They have an applied doctorate program where the student will focus their research on the practical application of knowledge and theory that already exists within their field. If a student is considering continuing their work in their field of choice by implementing their research in the field, the applied doctorate might be the right choice. If the student plans to contribute to their field through research and analysis, the Ph.D. program seems like the right choice.

• Top-ranked graduate programs in information technology
• 100% doctoral faculty
• More diverse

#4 Mississippi State University

Mississippi state, ms.

Website Tuition: $9,398

Mississippi State ranks among the top 50 best colleges in the South in Money Magazine’s “Best Colleges for Your Money” 2017 listing.

MSU is in the top 9 schools in the U.S. to hold all three of the National Security Agency’s centers of academic excellence awards. They are working closely with Pacific Northwest National Laboratory, and their researchers are using big-data analytics and high-performance computing to solve some of the nation’s top cybersecurity problems. Fixed on defeating the full spectrum of cyber attacks, Mississippi State’s National Science Foundation CyberCorps program is the 3rd largest in the country. The Department of Computer Science and Engineering has a strong presence in computing. The Miss. State Ph.D. specializes in research in traditional areas of computer science, cross-specialty areas, and interdisciplinary projects.

Recent graduates hold jobs at places like:

• Microsoft Research
• Palo Alto Research Center
• Verari Systems Software
• Fairmont State University
• Jackson State University
• Mississippi Valley State University
• Nova Southeastern University
• Tennessee Tech University
• Smaller class sizes
• Limited tech support

#3 Aspen University

Denver, colorado.

Website Tuition: $3,900

Aspen’s Doctor of Science in Computer Science offers a responsibly priced distance Ph.D. in computer science. The program builds an understanding of theoretical concepts and practical applications of computer science in the context of advanced research and analysis methods relating to computer architecture and software design. Aspen also offers a special series of courses designed to aid them in developing, researching, and writing the doctoral dissertation.

Aspen offers tuition rates low enough that most of their students can afford to pay their tuition in cash or through a monthly payment plan, enabling their students to gain a financially responsible Ph.D.

• Highly affordable
• More diverse student body
• Less prestigious computer science programs

#2 University of Utah

Salt lake city, utah.

Website Tuition: $7,353

Dozens of University of Utah’s academic programs, including computer science, rank in the top 100 by U.S. News & World Report. About 50 students a year enter the Ph.D. program at the University of Utah. This University offers eight different tracks of computer science. These are:

• Computer Engineering
• Data Management and Analysis
• Graphics and Visualization
• Human-centered Computing (HCC) Track
• Image Analysis
• Networked Systems
• Scientific Computing

At least 50 hours of graduate coursework is required for the Ph.D. degree in computer science. This must comprise at least 27 hours of regular graduate coursework and at least 14 semester hours of dissertation research. Independent study and seminars cannot be used as part of the required 50 hours. Of the required 27 semester hours of regular courses, up to six hours may be graduate-level courses outside of the School of Computing.

• Numerous specializations
• Robust tech support

#1 Dakota State University

Madison, sd.

Tuition: $5,999

Dakota State University specializes in computer management, computer information systems, and other related undergraduate and graduate programs. DSU started in 1881 as a school for teacher education, and it makes sure to keep that as a central focus, along with many other offerings now as well.

DSU offers three doctorates: a doctor of philosophy in cyber operations a doctor of philosophy in information systems, and a doctor of philosophy in computer science. There is an online option available through Dakota State University. Their distance Ph.D. in computer science allows students to take online courses. Students use various resources like DSU’s course management system and Desire2Learn to get assignments, lecture notes, and exams. Students schedule and work with their classmates in a virtual classroom.

Through these advanced graduate courses, Dakota State University students learn about:

• Artificial Intelligence
• Programming Languages
• Mobile Applications
• Computer Networks
• Cybersecurity
• Information Assurance
• Office Automation
• Bioinformatics
• Software Development
• Data Mining
• Database Management Systems

Graduate coursework in operations research is offered and may be used to provide an operations research concentration to the Ph.D. program. Admissions to the Graduate College is a selective process based on those who are outstanding among recipients of baccalaureate degrees.

A distance Ph.D. in computer science from DSU is a great investment and is billed hourly. That means you’ll always only pay for the number of classes you can take.

• Very affordable

Earning Your Online Ph.D. in Computer Science FAQ:

What are my opportunities for a career with a computer science ph.d. online.

• Contract R&D organizations
• Government laboratories
• Lockheed-Martin, Pfizer, and Ford have high-tech or internal R&D sections that do PhD-level computer science work
• Professor in any major tech university
• Startup companies and consulting firms
• Finance companies and hedge funds
• Secure the teaching faculty position in some reputable institution
• Postdoc research and publish papers and research articles
• Dynamic organization working in the field of hardware and software development
• Own a software house and explore the new and innovative software ideas
• Organization working in the field of robotics and artificial intelligence
• Google, Inc., Apple Computer, Inc., Microsoft Corp
• Software engineer
• Research scientist
• Senior data scientist
• Staff software engineer
• Computer scientist
• Principal software engineer

What can I expect to earn annually?

Expect to earn salaries of at least $105,000, potentially earning up to $150,000 and beyond.

How long will it take to complete my Ph.D. in computer science online?

The average time it will take for your Ph.D. is between 5–7 years. You should be able to do it in 3–4 years. 10 years is the maximum number of years most schools will allow you to complete a Ph.D. Time will vary depending on students’ schedules, their field requirements, and their chosen school.

• NCES: College Navigator

Related Resources

• Highest Paying PhD
• Affordable Colleges for a STEM Degree
• What is the Difference Between an Information Tech and a Computer Science Degree?
• PhD Computer and Information Systems Security Online
• PhD Management Information Systems Online

This concludes our ranking of the 10 Most Affordable Online Ph.D. in Computer Science Programs.

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• Institution Home

Berkeley Berkeley Academic Guide: Academic Guide 2023-24

Information and cybersecurity: mics.

About the Program

The Master of Information and Cybersecurity (MICS) is an online, part-time professional degree program that provides the technical skills and contextual knowledge students need to assume leadership positions in private sector technology companies as well as government and military organizations. The interdisciplinary program offers students mastery of core technical skills and fluency in the business, political, and legal context for cybersecurity, as well as managing cyber risk in the service of strategic decision making.

Students attend weekly live ("synchronous") sessions with classmates and instructors via an online platform as well as engaging with online ("asynchronous") videos and assignments on their own time. 

The core MICS curriculum includes cryptography, secure programming, systems security, and the ethical, legal, and economic framework of cybersecurity. In addition, students may select from a wide variety of electives covering topics such as privacy engineering, managing cyber risk, and usable security. MICS features a project-based approach to learning and encourages the pragmatic application of a variety of different tools and methods to solve complex problems.

Graduates of the program will be able to:

• Understand the ethical and legal requirements associated with cybersecurity and data privacy;
• Know how to build secure systems and applications;
• Prepare to lead, manage, and contribute to building cybersecurity solutions; and
• Gain hands-on, practical cybersecurity experience.

The I School also offers a master's in  Information and Data Science  (MIDS), a master's in  Information Management and Systems  (MIMS), and a doctoral degree  (PhD) program in Information Science .

VISIT PROGRAM SITE

Masters Degree Requirements (MICS)

Unit requirements.

The Master of Information and Cybersecurity is designed to be completed in 20 months. Students will complete 27 units of course work over five terms, taking two courses (6 units) per term for four terms and a one 3-unit capstone course in their final term. MICS classes are divided into foundation courses (9 units), a systems security requirement (3 units), advanced courses (12 units), and a synthetic capstone (3 units). Students will also complete an immersion at the UC Berkeley campus.

As a Master of Information and Cybersecurity (MICS) student, the immersion is your opportunity to meet faculty and peers in person on the UC Berkeley campus. You will have the opportunity to gain on-the-ground perspectives from faculty and industry leaders, meet with cybersecurity professionals, and soak up more of the School of Information (I School) culture. Offered twice a year, each four- to five-day immersion will be custom-crafted to deliver additional learning, networking, and community-building opportunities.

Please refer to the cybersecurity@berkeley website for more information.

Applying for Graduate Admission

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. A complete list of graduate academic departments, degrees offered, and application deadlines can be found on the Graduate Division website .

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Admission to the Program

Applications are evaluated holistically on a combination of prior academic performance, work experience, essays, letters of recommendation, and goals that are a good fit for the program.

The UC Berkeley School of Information seeks students with the academic abilities to meet the demands of a rigorous graduate program.

To be eligible to apply to the  Master of Information and Cybersecurity  program, applicants must meet the following requirements:

• A bachelor’s degree or its recognized equivalent from an accredited institution.
• Superior scholastic record, normally well above a 3.0 GPA.
• A high level of quantitative ability as conveyed by significant work experience that demonstrates your quantitative abilities and/or academic coursework that demonstrates quantitative aptitude
• A high level of analytical reasoning ability and a problem-solving mindset as demonstrated in academic and/or professional performance.
• An understanding of – or, a proven aptitude for and commitment to learning – data structures and discrete mathematics which can be demonstrated by at least one of the following qualifications: ​ Completed coursework in data structures and discrete mathematics; w ork experience that demonstrates understanding of data structures and discrete mathematics; proven technical aptitude, demonstrated by high level technical work experience or academic coursework; and/or proven commitment to learning concepts, demonstrated by review of MICS self-assessment and preparatory resources, and clear indication in application of progress made towards gaining this foundational knowledge.
• The ability to communicate effectively, as demonstrated by academic performance, professional experience, and/or strong essays that demonstrate effective communication skills.
• Knowledge of at least one, and ideally two, programming languages, such as C, C++, Python, Java, Javascript, or machine/assembly language as demonstrated by work experience or coursework. Applicants who lack this experience in their academic or work background but meet all other admission requirements will be required to take the Programming Fundamentals for Cybersecurity course in their first term.
• Not Required :  Official Graduate Record Examination (GRE)  General Test or  Graduate Management Admission Test (GMAT)  scores. As of Fall 2020, we have eliminated the GRE/GMAT requirement. We recommend you put your time and effort towards the required application materials.
• Official Test of English as a Foreign Language (TOEFL)  scores for applicants whose academic work has been in a country other than the US, UK, Australia, or English-speaking Canada.

For more information and application instructions, prospective MICS students should visit the cybersecurity@berkeley Admissions Overview .

Related Courses

Cyber 200 beyond the code: cybersecurity in context 3 units.

Terms offered: Summer 2024, Spring 2024, Fall 2023 This course explores the most important elements beyond technology that shape the playing field on which cybersecurity problems emerge and are managed. The course emphasizes how ethical, legal, and economic frameworks enable and constrain security technologies and policies. It introduces some of the most important macro-elements (such as national security considerations and interests of nation-states) and micro-elements (such as behavioral economic insights into how people understand and interact with security features). Specific topics include policymaking, business models, legal frameworks, national security considerations, ethical issues, standards making, and the roles of users, government, and industry. Beyond the Code: Cybersecurity in Context: Read More [+]

Rules & Requirements

Prerequisites: MICS students only

Hours & Format

Fall and/or spring: 14 weeks - 3 hours of lecture per week

Summer: 14 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week for fourteen weeks. Three hours of lecture per week for fourteen weeks.

Additional Details

Subject/Course Level: Cybersecurity/Graduate

Grading: Letter grade.

Formerly known as: Information and Cybersecurity W200

Beyond the Code: Cybersecurity in Context: Read Less [-]

CYBER 202 Cryptography for Cyber and Network Security 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 This course focuses on both mathematical and practical foundations of cryptography. The course discusses asymmetric and symmetric cryptography, Kerchkoff’s Principle, chosen and known plaintext attacks, public key infrastructure, X.509, SSL/TLS (https), and authentication protocols. The course will include an in-depth discussion of many different cryptosystems including the RSA, Rabin, DES, AES, Elliptic Curve, and SHA family cryptosystems. This course also introduces advanced topics of applied cryptography, including a brief introduction to homomorphic encrypted computation and secure multi-party computation to protect sensitive data during arbitrary computation, cryptocurrency and its cryptographic building blocks, and quantum computing. Cryptography for Cyber and Network Security: Read More [+]

Prerequisites: MICS students only. CYBER 206

Credit Restrictions: Students will receive no credit for CYBER W202 after completing CYBER 202 . A deficient grade in CYBER W202 may be removed by taking CYBER 202 .

Formerly known as: Information and Cybersecurity W202

Cryptography for Cyber and Network Security: Read Less [-]

CYBER 204 Software Security 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 The course presents the challenges, principles, mechanisms and tools to make software secure. We will discuss the main causes of vulnerabilities and the means to avoid and defend against them. The focus is on secure programming practice, including specifics for various languages, but also covering system-level defenses (architectural approaches and run-time enforcement). We will also apply software analysis and vulnerability detection tools in different scenarios. Software Security: Read More [+]

Objectives & Outcomes

Course Objectives: *Apply and manage secure coding practices throughout software project development *Gain a good comprehension of the landscape of software security vulnerabilities, with specifics for various programming languages and types of software applications *Gain the ability to analyze the security of a software system and convincingly advocate about the significance of vulnerabilities *Know representative tools for software security analysis and testing, use them in practice and understand their capabilities and limitations *Recognize insecure programming patterns and know how to replace them with secure alternatives

Student Learning Outcomes: Students will be able to apply and manage secure coding practices throughout software project development Students will be able to recognize insecure programming patterns and know how to replace them with secure alternatives Students will gain a good comprehension of the landscape of software security vulnerabilities, with specifics for various programming languages and types of software applications Students will gain the ability to analyze the security of a software system and convincingly advocate about the significance of vulnerabilities Students will know representative tools for software security analysis and testing, use them in practice and understand their capabilities and limitations

Credit Restrictions: Students will receive no credit for CYBER W204 after completing CYBER 204 . A deficient grade in CYBER W204 may be removed by taking CYBER 204 .

Formerly known as: Information and Cybersecurity W204

Software Security: Read Less [-]

CYBER 206 Programming Fundamentals for Cybersecurity 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 This course is designed to provide students with the foundational math and programming skills required to be successful in the Master of Information and Cybersecurity (MICS) program. Upon completion of this course, students will be able to write programs in Python and will gain experience reading and interpreting C programs. Students will receive a comprehensive overview of algebraic principles and will explore quantitative concepts needed for cryptography. Additionally, this course will prepare students to apply logical thinking and decompose complex problems to create programmatic solutions. Programming Fundamentals for Cybersecurity: Read More [+]

Formerly known as: Information and Cybersecurity W206

Programming Fundamentals for Cybersecurity: Read Less [-]

CYBER 207 Applied Machine Learning for Cybersecurity 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 Machine learning is a rapidly growing field at the intersection of computer science and statistics concerned with finding patterns in data. It is responsible for tremendous advances in technology, from personalized product recommendations to speech recognition in cell phones. This course provides a broad introduction to the key ideas in machine learning, with a focus on applications and concepts relevant to cybersecurity. The emphasis will be on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra will be important. Applied Machine Learning for Cybersecurity: Read More [+]

Credit Restrictions: Students will receive no credit for CYBER W207 after completing CYBER 207 . A deficient grade in CYBER W207 may be removed by taking CYBER 207 .

Formerly known as: Information and Cybersecurity W207

Applied Machine Learning for Cybersecurity: Read Less [-]

CYBER 210 Network Security 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 Introduction to networking and security as applied to networks. Exercises cover network programming in a language of the student's choice, understanding and analyzing packet traces using tools like wireshark and mitmproxy, as well as applying security principles to analyze and determine network security. After this course, the student will have a fundamental understanding of networking, TLS and security as it applies to networked systems. Network Security: Read More [+]

Credit Restrictions: Students will receive no credit for CYBER W210 after completing CYBER 210 . A deficient grade in CYBER W210 may be removed by taking CYBER 210 .

Formerly known as: Information and Cybersecurity W210

Network Security: Read Less [-]

CYBER 211 Operating System Security 3 Units

Terms offered: Summer 2024, Fall 2023, Summer 2023 This survey of operating system security compares approaches to security taken among several modern operating systems. The course will teach how to conceptualize design issues, principles, and good practices in securing systems in today’s increasingly diverse and complex computing ecosystem, which extends from things and personal devices to enterprises, with processing increasingly in the cloud. We will approach operating systems individually and then build on them so that students learn techniques for establishing trust across a set of interoperating systems. Operating System Security: Read More [+]

Prerequisites: MICS students only. CYBER 200

Credit Restrictions: Students will receive no credit for CYBER W211 after completing CYBER 211 . A deficient grade in CYBER W211 may be removed by taking CYBER 211 .

Formerly known as: Information and Cybersecurity W211

Operating System Security: Read Less [-]

CYBER 215 Usable Privacy and Security 3 Units

Terms offered: Summer 2024, Summer 2023, Fall 2022 Security and privacy systems can be made more usable by designing them with the user in mind, from the ground up. In this course, you will learn many of the common pitfalls of designing usable privacy and security systems, techniques for designing more usable systems, and how to evaluate privacy and security systems for usability. Through this course, you will learn methods for designing software systems that are more secure because they minimize the potential for human error. Usable Privacy and Security: Read More [+]

Credit Restrictions: Students will receive no credit for CYBER W215 after completing CYBER 215 . A deficient grade in CYBER W215 may be removed by taking CYBER 215 .

Formerly known as: Information and Cybersecurity W215

Usable Privacy and Security: Read Less [-]

CYBER 220 Managing Cyber Risk 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 This course offers valuable perspective for both the non-technical business manager and the technical cybersecurity or IT manager. It is the vital connector between the technical world of threats, vulnerabilities, and exploits, and the business world of board-level objectives, enterprise risk management, and organizational leadership. Now more than ever, managers have a need and responsibility to understand cyber risk. Just as financial risks and other operational risks have to be effectively managed within an organization, cyber risk has to be managed. It spans far beyond information technology, with broad implications in the areas of organizational behavior, financial risk modeling, legal issues, and executive leadership. Managing Cyber Risk: Read More [+]

Student Learning Outcomes: Compare and employ approaches to cyber risk management and measurement. Develop a basic cybersecurity strategic plan and understand how it aligns with the core business value of the company. Navigate corporate structures to create a strong cyber security program and obtain senior leadership buy-in. Understand security product verticals, identify common use cases for those products, and define requirements for acquiring solutions relevant to a business use case. Understand the basic principles and best practices of responding to a cybersecurity incident

Credit Restrictions: Students will receive no credit for CYBER W220 after completing CYBER 220 . A deficient grade in CYBER W220 may be removed by taking CYBER 220 .

Formerly known as: Information and Cybersecurity W220

Managing Cyber Risk: Read Less [-]

CYBER 233 Privacy Engineering 3 Units

Terms offered: Spring 2024, Fall 2023, Spring 2023 This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms. Privacy Engineering: Read More [+]

Student Learning Outcomes: Students should be able to implement such privacy paradigms, and embed them in information systems during the design process and the implementation phase. Students should be familiar with the different technical paradigms of privacy that are applicable for systems engineering. Students should develop critical thinking about the strengths and weaknesses of the different privacy paradigms. Students should possess the ability to read literature in the field to stay updated about the state of the art.

Credit Restrictions: Students will receive no credit for CYBER W233 after completing CYBER 233 . A deficient grade in CYBER W233 may be removed by taking CYBER 233 .

Formerly known as: Information and Cybersecurity W233

Privacy Engineering: Read Less [-]

CYBER 242 New Domains of Competition: Cybersecurity and Public Policy 3 Units

Terms offered: Summer 2024, Spring 2024, Summer 2023 Cybersecurity is a primary national security and public policy concern. The government, military and private sector have various roles and responsibilities with regard to the protection of the cyber domain. In this course, students critically evaluate these roles and responsibilities, the manner in which government networks, systems, and data are secured, and the ability of national and international cybersecurity strategies and partnerships to mitigate the security risks introduced by society’s increased reliance on information. New Domains of Competition: Cybersecurity and Public Policy: Read More [+]

Course Objectives: Critically assess national and international cybersecurity strategies Describe and evaluate national and international public-private partnerships. Discuss the developments in the cyber domain and and its protection within the context of national security. Identify lessons learned and recommend ways to improve national and international approaches to cybersecurity. Identify the roles and responsibilities of the military, government, and the private sector in cybersecurity. Utilize an evidence-based approach to analyze the security of government networks and systems and privacy of retained data.

Credit Restrictions: Students will receive no credit for CYBER W242 after completing CYBER 242 . A deficient grade in CYBER W242 may be removed by taking CYBER 242 .

Formerly known as: Information and Cybersecurity W242

New Domains of Competition: Cybersecurity and Public Policy: Read Less [-]

CYBER 252 Security Operations 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 This course will focus on understanding key areas within Security Operations from a management perspective. Upon completion of this course, students will understand implementation and maintenance best practices for security operations services such as incident response, internal investigations, security analysis, threat intelligence and digital forensics. Students will not only get hands-on experience within each discipline but will also understand how to recruit and train others within a security operations center or security team. Security Operations: Read More [+]

Course Objectives: Demonstrate data analysis as it pertains to identifying and responding to cyber-attacks. Effectively apply knowledge in simulated real-world conditions to protect and defend complex networks and infrastructures, including in the cloud. Implement incident response and digital forensics techniques.

Prerequisites: MICS students only. CYBER 200 , CYBER 204 , and CYBER 210

Security Operations: Read Less [-]

CYBER 284 Web Application Security Assessment 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 Web applications play a vital role in every modern organization. If an organization does not properly test its web applications to identify security flaws, adversaries may be able to compromise these applications damaging functionality and accessing sensitive data. The focus of this course is on developing practical web application security testing skills required to assess a web application's security posture and convincingly demonstrate the business impact of discovered vulnerabilities, if exploited. The course includes both lectures and a variety of demonstrations and hands-on exercises in finding web application security vulnerabilities. During the course, students learn about assessment tools and methodologies. Web Application Security Assessment: Read More [+]

Course Objectives: Develop skills in writing web application security assessment reports Discover and exploit key web application flaws Gain a good comprehension of web application security vulnerabilities Learn to apply a repeatable methodology to deliver enterprise-level web application security assessment Learn to explain potential impact of web application vulnerabilities

Prerequisites: MICS students only. CYBER 204

Repeat rules: Course may be repeated for credit with instructor consent.

Web Application Security Assessment: Read Less [-]

CYBER 289 Public Interest Cybersecurity: The Citizen Clinic Practicum 3 Units

Terms offered: Spring 2024, Fall 2023, Spring 2023 This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. In the classroom, students study basic theories and practices of digital security, intricacies of protecting largely under-resourced organizations, and tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic , students work in teams supervised by Clinic staff to provide direct cybersecurity assistance to civil society organizations. We emphasize pragmatic, workable solutions that take into account the unique needs of each partner organization. Public Interest Cybersecurity: The Citizen Clinic Practicum: Read More [+]

Credit Restrictions: Students will receive no credit for CYBER W289 after completing CYBER 289 . A deficient grade in CYBER W289 may be removed by taking CYBER 289 .

Formerly known as: Information and Cybersecurity W289

Public Interest Cybersecurity: The Citizen Clinic Practicum: Read Less [-]

CYBER 290 Special Topics 3 Units

Terms offered: Fall 2022, Summer 2022, Fall 2021 Specific topics, may vary from section to section, year to year. Special Topics: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.

Special Topics: Read Less [-]

CYBER 295 Capstone 3 Units

Terms offered: Summer 2024, Spring 2024, Fall 2023 This capstone course will cement skills and knowledge learned throughout the Master of Information and Cybersecurity program: core cybersecurity technical skills, understanding of the societal factors that impact the cybersecurity domain and how cybersecurity issues impact humans, and professional skills such as problem-solving, communication, influencing, collaboration, and group management – to prepare students for success in the field. The centerpiece is a semester-long group project in which teams of students propose and select a complex cybersecurity issue and apply multi-faceted analysis and problem-solving to identify, assess, and manage risk and deliver impact. Capstone: Read More [+]

Student Learning Outcomes: Engage in a highly collaborative process of idea generation, information sharing, and feedback that replicates key aspects of managing cybersecurity in an organizational setting. Learn or reinforce communication, influencing, and management skills. Practice using multi-faceted problem-solving skills to address complex cybersecurity issues.

Prerequisites: MICS students only. CYBER 200 , CYBER 202 , CYBER 204 , CYBER 206 , and CYBER 210 . Must be taken in final term of the MICS program

Formerly known as: Information and Cybersecurity W295

Capstone: Read Less [-]

Contact Information

School of information.

Phone: 510-642-1464

Fax: 510-642-5814

Senior Director of Student Affairs

Siu Yung Wong

[email protected]

Phone: 855-860-5259

[email protected]

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University of California - Berkeley PhD in Computer Science

Computer Science is a concentration offered under the computer science major at University of California - Berkeley. Here, you’ll find out more about the major doctor’s degree program in computer science, including such details as the number of graduates, ethnicity of students, related majors and concentrations, and more.

If there’s something special you’re looking for, you can use one of the links below to find it:

• Graduate Cost
• Online Learning
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Featured Programs

Learn about start dates, transferring credits, availability of financial aid, and more by contacting the universities below.

AS in Computer Science

Learn the applied programming skills needed to fill in-demand tech roles when you earn your online AS in Computer Science at Southern New Hampshire University.

BS in Computer Science

Learn the front-end design and back-end development skills employers look for in full stack software developers with this online bachelor's degree in computer science from Southern New Hampshire University.

BS in Computer Science - Software Engineering

With a software engineering degree, you'll learn the fundamental concepts and principles – a systematic approach used to develop software on time, on budget and within specifications – throughout your online college classes at SNHU.

How Much Does a Doctorate in Computer Science from UC Berkeley Cost?

Uc berkeley graduate tuition and fees.

The average full-time tuition and fees for graduate students are shown in the table below.

Related Programs

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MS in Information Technology - Software Application Development

Learn to manage the development process for a software program with this specialized online master's from Southern New Hampshire University.

Does UC Berkeley Offer an Online PhD in Computer Science?

Online degrees for the UC Berkeley computer science doctor’s degree program are not available at this time. To see if the school offers distance learning options in other areas, visit the UC Berkeley Online Learning page.

UC Berkeley Doctorate Student Diversity for Computer Science

Male-to-female ratio.

About 18.4% of the students who received their PhD in computer science in 2019-2020 were women. This is about the same as the countrywide number of 19.1%.

Racial-Ethnic Diversity

Racial-ethnic minority graduates* made up 28.9% of the computer science doctor’s degrees at UC Berkeley in 2019-2020. This is higher than the nationwide number of 10%.

*The racial-ethnic minorities count is calculated by taking the total number of students and subtracting white students, international students, and students whose race/ethnicity was unknown. This number is then divided by the total number of students at the school to obtain the racial-ethnic minorities percentage.

• National Center for Education Statistics
• O*NET Online

More about our data sources and methodologies .

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Best Online Doctorates in Computer Science: Top PhD Programs, Career Paths, and Salary

The field of computer science has an immense predicted growth over the next few years. According to the Bureau of Labor Statistics, computer science and information technology jobs will see a 13 percent employment growth by 2026. The best online PhD in Computer Science can help you learn the most in-demand tech skills needed to get one of these lucrative jobs.

The growing e-learning environment makes it now possible to earn a computer science PhD from the comfort of your home. An online PhD in Computer Science is highly flexible and will give you the same competitive edge as an in-person PhD . In this guide, we’ve put together a detailed list of the best online computer science PhD programs and best computer science jobs for PhD holders.

Find your bootcamp match

Can you get a phd in computer science online.

Yes, you can get a PhD in Computer Science online. Many universities offer a 100 percent online program for computer science doctoral degree students interested in distance learning education. These doctoral programs are flexible and can be customized to your career goals. This flexibility lets online students work while they study, making them great for upskilling computer science professionals.

Most online computer science programs require students to complete a required number of credit hours. Doctoral students can specialize in artificial intelligence, machine learning, database management, or project management, depending on the program they’ve enrolled in and the subject of their doctoral dissertation.

Is an Online PhD Respected?

Yes, an online PhD is respected. Online learning is gaining popularity and, according to Franklin University, there are now about 1,000 online doctorate programs in the country . A respected online PhD degree has been accredited by external agencies recognized by the US Department of Education, meaning it’s been thoroughly reviewed and maintains high quality standards.

Online doctoral programs are an excellent, affordable option for working professionals who can’t attend on campus classes. These programs are as intensive as any traditional doctoral program and students get access to various online resources, guides, and career counseling services. Employers have also started recognizing online degrees, making them even more valuable.

What Is the Best Online PhD Program in Computer Science?

The best online PhD program in computer science is offered by the University of California, Berkeley. The reason it’s the best is the extremely high quality of education it provides as well as the prestige that comes with having attended this university. This graduate program is open to both bachelor’s and master’s graduates. Doctoral students can complete it in three to six years.

Why the University of California, Berkeley Has the Best Online PhD Program in Computer Science

The University of California, Berkeley has the best online PhD program in computer science because it offers ten specializations. Open to both bachelor’s and master’s graduates, UC Berkeley also offers two online options covering a wide range of subjects in electrical and computer engineering.

This graduate program focuses on providing both research and teaching experience. According to US News and World Report, UC Berkeley is not only one of the best national universities but is also the fourth-best university in the world and has excellent faculty and online resources for all of its students. If you can pass the application process, there is no better graduate program you could attend.

Best Online Master’s Degrees

[query_class_embed] online-*subject-masters-degrees

Online PhD in Computer Science Admission Requirements

The admission requirements for an online PhD in Computer Science are different from school to school, but commonly require having a master’s degree from an accredited institution, work experience, and letters of recommendation. Most schools require students to submit their official transcripts, resumes, and a statement of purpose indicating their interest in the program. 

Each program has its own admission process and many universities only consider applications from students who’ve scored a minimum college GPA of 3.0. Others require students to have a solid background in computer science. Students usually need to send GRE scores, although some universities have waived this requirement due to the pandemic.

• A Master’s Degree in Computer Science or a related field from an accredited institution
• Relevant work experience
• A minimum college GPA of 3.0 on a scale of 4.0
• Letters of recommendation
• A personal essay or statement of purpose

Best Online PhDs in Computer Science: Top Degree Program Details

Best online phds in computer science: top university programs to get a phd in computer science online.

Finding the right online PhD program in computer science can be challenging. The program should align with your career goals and help you excel. You should also need to be able to afford the program. To help you begin your school search, we’ve listed our picks of the best online PhDs in Computer Science.

Auburn University is one of the best universities in Alabama . It is also one of the top 50 public universities in the country , according to US News and World Report. AU was set up in 1856 and is a public land-grant research institution consisting of 15 schools and colleges which offer over 150 majors. The university is home to over 300 clubs and student organizations. 

PhD in Computer Science and Software Engineering

This PhD program at Auburn University helps students develop their research skills using cutting-edge computer technology and is one of the few doctoral programs that allow undergraduate students to enroll. Students with a bachelor's or master's degree in computer science, software engineering, or cyber security engineering are eligible to apply. 

PhD in Computer Science and Software Engineering Overview

• Accreditation: The Commission on Colleges of the Southern Association of Colleges and Schools
• Program Length: 4 years
• Acceptance Rate: N/A
• Tuition and Fees: $630/credit

PhD in Computer Science and Software Engineering Admission Requirements

• Your official transcripts
• A Bachelor’s or Master’s degree in Computer Science , Software Engineering, or Cybersecurity Engineering with a minimum GPA of 3.0 
• A written statement of purpose
• Your resume
• Three letters of recommendation
• A minimum cumulative GRE score of 300 with a minimum of 150 in the verbal and quantitative sections, and three in the written section

Established in 1993, Capella University is a four-year private institution located in Minnesota. It offers 53 degree programs with over 140 specializations in the areas of business, nursing, health sciences, information technology, social work, and psychology. The university currently has over 38,000 students from around the country enrolled in its various programs.  

Doctor of Information Technology

Students in this graduate program learns to develop technical solutions for troubleshooting complex business problems. This PhD program’s graduation requirements include the completion of 70 credits from eight core courses, four specialization courses, two virtual residencies, and one dissertation. 

Doctor of Information Technology Overview

• Accreditation: Higher Learning Commission
• Program Length: 4 years 
• Tuition and Fees: $750/credit 

Doctor of Information Technology Admission Requirements

• A master’s degree from an accredited US institution or an internationally recognized institution
• A minimum GPA of 3.0 on a 4.0 scale
• For international students, a minimum acceptable score on an English proficiency test

Dakota State University was originally founded in 1881 as a teacher's college and is one of the most affordable universities in the country. According to the university website, 74 percent of students receive an average of $10,160 in financial aid annually. Dakota State University offers half of its undergraduate and graduate programs online. 

Doctor of Philosophy in Information Systems

The PhD in Information Systems at Dakota State University is best suited for those who want a career in a data-intensive industry like banking or finance. Students learn the foundation of information systems, applied statistics, and emerging technologies through a wide range of coursework and a 12-credit dissertation to help them solve real-world problems.

Doctor of Philosophy in Information Systems Overview

• Program Length: Up to 7 years
• Tuition and Fees: $580.60/credit

Doctor of Philosophy in Information Systems Admission Requirements

• A degree from a regionally accredited institution
• A minimum undergraduate GPA of 3.0 on a scale of 4.0 
• GRE scores, no minimum specified and can be waived if the applicant has a GPA above 3.25
• Background in business and information systems

Founded in 1820, Indiana University is one of the top public research universities in the country. More than 71,000 undergraduate and 19,000 graduate students are enrolled in Indiana University’s over 930 academic programs spread across its online campus and seven in-person campuses.

PhD in Computer Science

Indiana University offers a unique multidisciplinary graduate program. Students can conduct research in computer science or study it along with other disciplines like statistics or biology. Some of the subjects students will study include artificial intelligence, programming languages, data science, bioinformatics, and security. Students also get access to supercomputer resources. 

PhD in Computer Science Overview

• Accreditation: The Higher Learning Commission
• Program Length: 5 years
• Tuition and Fees: $418.03/credit (in-state); $1,330.51/credit (out-of-state)

PhD in Computer Science Admission Requirements

• A three or four-year bachelor's degree
• A master's degree
• A GPA of 3.5 (B+) or above
• Applicants must have completed coursework in data structures, machine organization, assembly language, and discrete structures 
• Letters of Recommendation
• Excellent GPA in undergraduate and graduate degree programs 
• GRE scores, no minimum specified

Mississippi State University (MSU) is a public-land grant state university founded in 1878. Consisting of eight colleges offering over 160 undergraduate and graduate programs, MSU has a total enrollment of around 20,000 students. According to the National Science Foundation, MSU is one of the top 100 research institutions in the country. 

Doctor of Philosophy in Computer Science

In this program, students learn advanced algorithms, machine learning, and artificial intelligence concepts through live and pre-recorded classes. The program curriculum includes core courses, a primary specialization, and a secondary specialization. Recent graduates work for Microsoft, Palo Alto Research Center, Fairmont State University, and Jackson State University.

Doctor of Philosophy in Computer Science Overview

• Accreditation: Southern Association of Colleges and Schools Commission on Colleges
• Program Length: N/A
• Tuition and Fees: $539/credit

Doctor of Philosophy in Computer Science Admission Requirements

• A master's degree from a recognized university in the US or equivalent
• Your official university transcripts
• An online application as an unclassified student
• A minimum GPA of 3.0 on a scale of 4.0

Northcentral University was established in 1996 and has been a part of the National University System since 2019. This private university offers over 60 bachelor's, master's, and doctoral programs on campus and online across its six schools of study. The university is home to approximately 10,500 students.

NCU's PhD in Computer Science is a flexible program with no scheduled lecture hours. Students can schedule the courses as per their professional and personal requirements. During the PhD, students develop a deep understanding of information systems. They also learn about the current theories and applications of computer science. 

• Tuition and Fees: $1,094/credit
• A master's degree from an accredited academic institution
• A copy of your current resume 
• An online application form

The University of California, Berkeley is a public research university founded in 1868. With a student-to-faculty ratio of 17.8 to one, the university offers over 350 degree programs in various career fields. As previously mentioned, this school is ranked fourth among global universities by US News and Global reports and is among the top national universities.

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Berkeley's PhD in Computer Science focuses on providing a thorough research and teaching experience to its students. With ten specializations, students can research artificial intelligence, database management systems, human-computer interaction, and programming systems.  Students with a bachelor's degree can also enroll in the program and finish in five to six years. 

• Accreditation: Western Association of Schools & Colleges (WASC)
• Program Length: 3-6 years
• Tuition and Fees: $10,123.75/semester (in-state); $17,674.75/student (out-of-state)
• A bachelor's degree or equivalent from an accredited institution
• A statement of purpose
• A copy of your current resume  

The University of North Dakota was founded in 1883 and is the oldest and largest university in the state. The university welcomes over 13,000 students every year and offers over 225 majors across the fields of business, engineering, law, and more. In fact, it is the only university in North Dakota to have law and medical schools. 

In this PhD program, students develop the skills they need to be able to solve real-world problems using computational technology. Some of the program's core courses include data engineering and management, computer forensics, and computer networks. Students can also specialize in dynamic branches in bioinformatics, atmospheric science, and software design.

• Accreditation: Higher Learning Commission of the North Central Association of Colleges and Schools
• Program Length: 4-5 years
• Tuition and Fees: $616.59/credit (North Dakota resident); $763.77/credit (Minnesota resident); $889.16/credit (non-resident)
• Master's or bachelor's degree in an engineering or science-related field
• Your official college transcripts
• An undergraduate or graduate GPA of 3.0 on a scale of 4.0 
• A written statement of goals
• Three references
• GRE scores, no minimum specified 

The University of South Carolina is a 200-year-old public research university located in Columbia, South Carolina. The university is made up of nine schools and six colleges offering a wide range of undergraduate and graduate degree programs. It has a total enrolment of 35,388 in 2021.

This program’s curriculum includes core coursework covering computer architecture, compiler construction, and algorithms analysis. Students also need to complete a 12-credit dissertation, including a dissertation proposal and defense. The university offers ten specialization options including artificial intelligence, computer vision, machine learning, quantum computing, and robotics.

• Accreditation: The Southern Association of Colleges and Schools Commission on Colleges
• Tuition and Fees: 6,867.00/semester (in-state, full-time); $14,880.00/semester (out-of-state, full-time)
• An undergraduate degree from a recognized university
• A GRE quantitative score of 165 or higher and a GRE verbal score of 150 or better 
• Two letters of recommendation
• A copy of your resume
• A written personal statement

Wright State University was established in 1964 and is a public university that offers 276 different undergraduate and graduate degree programs. The university is composed of five colleges and two schools and has a student-to-faculty ratio of 15:1. 

PhD in Computer Science and Engineering

This PhD program is an excellent option for those wanting to focus on learning advanced concepts in researching, designing, and testing computer systems. Some of the program’s core courses cover topics in computer programming, operating systems, computer organization, and data structures and algorithms. 

PhD in Computer Science and Engineering Overview

• Accreditation: Higher Learning Commission (HLC)
• Tuition and Fees: $7,149/semester (in-state, full-time); $12,143/semester (out-of-state, full-time)

PhD in Computer Science and Engineering Admission Requirements

• A Bachelor of Science or a Master of Science in computer science, computer engineering, or a similar related discipline from an accredited institution with a grade point average of 3.3 or higher
• Strong knowledge of high-level programming languages and data structures, computer organization, and architecture 
• Understanding of operating systems, calculus, probability and statistics, linear algebra, and discrete mathematics

Online Computer Science PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Computer Science?

It is extremely hard to complete an online PhD in Computer Science. According to a recent study, almost half of all students don’t graduate from their online PhD programs. A computer science PhD requires extensive independent study and is characterized by extended program length, intensive research, and complex courses.

In fact, the total number of doctoral candidates who’ve earned a PhD in Computer Science is low. According to Statista, only 2414 computer and information science PhD candidates graduated in 2019-20, further demonstrating that earning your doctorate online is challenging.

How Long Does It Take to Get a PhD in Computer Science Online?

It takes about three to seven years to get a PhD in Computer Science online, depending on the university and its graduation requirements. Factors like the school’s PhD timeline, dissertation process, and policies can impact the amount of time it takes for you to complete your PhD. According to Statista, the average time to earn a PhD is 7.5 years .

Many students choose to enroll in an online degree program because of the increased flexibility, as many of them can be customized according to your needs. Online students can more easily work while they earn their PhD, which can add to the time it takes to complete. That being said, this also allows you to build work experience and a PhD degree. Most universities set a cap on the amount of time you get to finish PhD, but you can always apply to extend your PhD timeline.

How Hard Is an Online Doctorate in Computer Science?

An online PhD in Computer Science is very hard. A PhD is an advanced degree where you develop a deep and specialized body of knowledge. Students are required to complete 60-70 credits of advanced courses on top of conducting independent research for their dissertation and passing comprehensive examinations.

While online PhDs are more flexible than on-campus programs, doctoral students still have to spend a lot of time reading online resources and doing laboratory work. Many students have to dedicate up to 50 hours a week to their PhD program to finish their work in a timely manner.

Before starting their dissertation, students have to pass a qualifying exam and later a preliminary exam. These comprehensive examinations are used by the teaching staff at colleges and universities to make sure that students can handle the work that comes with completing their dissertations.

Best PhD Programs

[query_class_embed] phd-in-*subject

What Courses Are in an Online Computer Science PhD Program?

The courses in an online PhD in Computer Science include computer programming, algorithm design, artificial intelligence, network architecture and security, and technology management. Computer science is an extremely vast field pertinent to many industries, meaning that every PhD degree will have its own unique curriculum and graduate courses.

During your school search, you should check each program’s core courses, elective courses, and thesis courses to find the one that is most in line with your interests. You can also talk to the admission counselor to help you determine if their PhD program fits you. Below are some of the most common areas of study you’ll encounter in a PhD program in this field.

Main Areas of Study in a Computer Science PhD Program

• Machine learning.
• Artificial intelligence.
• Information management.
• Database management.
• Operating systems.
• Network architecture.

How Much Does Getting an Online Computer Science PhD Cost?

On average, it costs $19,314 per year to get a PhD in Computer Science according to the National Center for Education Statistics (NCES). Keep in mind that the average tuition at a public research institution will be significantly lower than the tuition fees of a private research institution.

Some schools also charge non-billable expenses like technology fees or e-library fees from students studying online programs. That being said, an online PhD or even a hybrid program will typically be a lot cheaper than a fully on-campus PhD program in computer science.

How to Pay for an Online PhD Program in Computer Science

You can pay for an online PhD program in computer science by applying for financial aid options such as scholarships, grants, and fellowships. Prospective students are eligible to apply for several fully-funded scholarships. Several universities offer a tuition minimum support which allows students to get a PhD degree without having to pay the tuition.

Most universities also offer some apprenticeships or hire students to assist professors in conducting research work. Students can also apply for paid teaching assistantships to lower their tuition rates.

Students should also submit a Free Application for Federal Student Aid (FAFSA) application. The federal government offers much financial aid to help talented students. Most universities recommend students fill out their FAFSA application during their admission process.

If you are a working professional, you can pay for your online PhD program through your salary or by getting your employer to fund your higher education. Online PhD programs are highly flexible and allow their students to design their programs so they can work side-by-side. Getting a bank loan is also another option.

How to Get an Online PhD for Free

You cannot get an online PhD in Computer Science for free. PhD programs are advanced degree programs that use a university’s top-of-the-line research facilities. That said, some universities offer minimum tuition support to PhD students.

The best way to reduce your tuition is to apply for scholarships, fellowships, and grant options provided by your university. While some universities, like Brown University and Rhode Island University, offer fully-funded PhD programs in computer science, they are rare and only for on-campus programs, and have extremely selective application processes.

What Is the Most Affordable Online PhD in Computer Science Degree Program?

The most affordable online PhD degree program is PhD in Computer Science offered by the University of Utah. This university’s tuition fees sit at around $300.58 per credit before other expenses like technology fees and graduation fees. Even when considering additional fees, the tuition for this school is significantly less expensive than that of UC Berkeley, the most expensive on our list.

Most Affordable Online PhD Programs in Computer Science: In Brief

Why you should get an online phd in computer science.

You should get an online PhD in Computer Science because it is a growing field with a wide variety of promising career opportunities. With a PhD, you can get access to various high-paying job positions thanks to your expertise in a specialized topic and in-demand tech skills.

A PhD will also help you build problem-solving skills and teach you how to create innovative tech solutions. Additionally, having a doctorate is an excellent way to demonstrate your skills to potential employers, allowing you to pursue a career as a computer scientist and create advanced technology that can improve the lives of people around the world.

Top Reasons for Getting a PhD in Computer Science

• Advanced skills. Computer science is the fastest-growing field in the world. Thanks to the highly advanced skillset provided by a PhD, PhD grads are eligible for senior-level positions. Additionally, PhDs are research-based programs that teach students how to conduct research and develop new skills which is extremely attractive to employers.
• Higher chances of getting a high-paying job. The more advanced your degree is, the more you are likely to be considered for a high-paying job. Earning a doctorate makes you eligible for managerial and leadership roles and provides you with a higher earning potential than other degrees.
• Wide range of Career Opportunities. Students with a PhD can explore a wide variety of career opportunities unavailable to bachelor’s and master’s degree graduates. In this field, there are many job opportunities that require a PhD.
• Gain specialization and build expertise. A PhD program allows students to specialize and understand a particular subject in-depth. For instance, you can gain expertise in cyber security, machine learning, artificial intelligence, or technology management through a PhD program.

Best Master’s Degree Programs

[query_class_embed] *subject-masters-degrees

What Is the Difference Between an On-Campus Computer Science PhD and an Online PhD in Computer Science?

The primary difference between an online and on-campus computer science PhD is the delivery format. Students must attend in-person classes and follow a strict schedule when attending an on-campus PhD. In contrast, students learning online watch pre-recorded or live lectures from wherever they have access to the Internet.

It is in a university’s best interest to ensure that online and on-campus programs offer similar learning and other student facilities. That being said, not all degrees are suitable for everyone. Choosing whether to attend online or on-campus can be challenging. Below are some factors that can help you decide which type of program is better for you.

Online PhD vs On-Campus PhD: Key Differences

• Personalization . Online PhDs are often designed for working professionals and allow students to more easily customize their schedules to their needs. On-Campus PhDs follow a more strict structure.
• End Goal . The student’s focus during an online PhD is to work on independent research. In contrast, an on-campus PhD allows students to work on more collaborative innovative research projects to improve the field of computer science.
• Learning Format . In an online PhD, students can complete the program without ever having to visit the campus. Contrary to this, the delivery of coursework for on-campus PhD programs is done entirely through in-person lectures and labs.
• Cost of Attendance . Online PhDs are often cheaper than on-campus PhDs as students use fewer of the school’s facilities. Students also incur fewer costs indirectly related to their studies like transportation or student housing.

How to Get a PhD in Computer Science Online: A Step-by-Step Guide

To get a PhD in Computer Science online, you first need to find and apply to a program that fits your educational and career goals. The next step is to complete the degree completion requirements set by the university. These requirements include earning a specific number of credits from courses and conducting research for your dissertation project before submitting it.

Having a bachelor's or a master's degree is a mandatory admission requirement of all PhD programs, meaning you’ll first need to earn a bachelor's or master's degree in computer science or a related field before applying. While you’re earning your bachelor's or master's degree program, make sure you keep in mind any prerequisite course requirements from your chosen  PhD program.

Once you've earned your degree, you can start your graduate school search. Make a list of the schools you're interested in and submit your admission forms. Keep in mind that the admission process can be lengthy, so start early. Additionally, you will have to send in your official transcripts, GRE scores, personal essays, and letters of recommendation.

Universities also require prospective PhD students to undertake a qualifying exam after being accepted. This qualifying examination usually takes place before a student starts working on a dissertation and is an oral presentation of their research proposal. During it, the university committee evaluates whether the student can conduct the research required to qualify for the degree.

After gaining admission into a PhD program, your first milestone will be to meet the credit hour requirements. Usually, students have to finish 60 to 90 credits to earn a PhD degree. These are earned from core courses on topics such as database management, programming language, network architecture, machine learning, and systems design.

Once you've completed your coursework, you’ll be required to conduct an independent study. This means you will have to conduct extensive research to propose a practical solution for a real-world proposal. This will typically require you to write a number of research papers. 

Finally, you will have to work on your dissertation project. A dissertation project is the culmination of all the research you’ve done up to this point in your PhD and demonstrates the testing of an existing theory and your proposed solution. You will also have to successfully defend your dissertation before a university committee to earn your PhD. 

Online PhD in Computer Science Salary and Job Outlook

According to Payscale, a computer science PhD holder has a median salary of $131,000 . An online PhD can make you eligible for jobs that require intensive research and work experience. You could work in academia or as a research scientist, software engineer, or machine learning expert.

While PhD graduates usually work in the tech industry, one of the fastest-growing industries in the world, they can also work in a wide variety of other industries. According to the US Bureau of Labor Statistics, computer science and information technology jobs will witness a growth of 13 percent by 2030.

What Can You Do With an Online Doctorate in Computer Science?

With an online doctorate in computer science, you can get a high-paying tech job as an IT manager, computer research scientist, machine learning engineering, or a DevOps Engineer. You could also become a professor or specialize in machine learning or artificial intelligence. You’ll need to be able to show your original research when applying for any of these positions.

We’ve prepared a list of the best-paying jobs in the tech industry to help you start your research about career outcomes. These mid-senior-level positions usually require higher education, such as a doctoral degree, and a significant amount of work experience.

Best Jobs with a PhD in Computer Science

• Computer and information systems manager
• Computer and information research scientist
• Computer hardware engineer
• Computer network architects
• Software developer

Potential Careers With a Computer Science Degree

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What Is the Average Salary for an Online PhD Holder in Computer Science? 

The average salary for a PhD in Computer Science is $133,000, according to Payscale. Computer science is a growing field in which PhD holders are eligible for just about any position relevant to their specialization. Your actual annual salary will depend on your job title, location, employer, and skillset.

Highest-Paying Computer Science Jobs for PhD Grads

Best computer science jobs for online phd holders.

The best computer science jobs for online PhD holders make full use of the advanced skills and experience you’ll have developed during your online PhD program. Your body of knowledge will  qualify you for leadership roles and managerial positions across the tech industry. Below is a detailed list of best-paying jobs online computer science PhD holders can get after graduating.

Computer and information systems managers analyze the technological need of the company they work for. Also known as IT managers, they plan and oversee the installation and maintenance of computer hardware and software and make efforts to ensure the security of an organization's networks and databases. To become an IT manager , you’ll need advanced skills and work experience. 

• Salary with a Computer Science PhD: $159,010
• Job Outlook: 11% job growth from 2020 to 2030
• Number of Jobs: 42,400
• Highest-Paying States: New York, California, New Jersey, Washington, District of Colombia

Computer and information scientists identify challenges in the field of computer science and software development then perform research to create innovative ways to solve those challenges. Computer scientists also work on developing modern programming languages. 

• Salary with a Computer Science PhD: $131,490 
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 3,200
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, and Washington 

Computer hardware engineers are responsible for researching, designing, building, and testing computer systems. They upgrade the existing computer models and make sure that the upgrades integrate perfectly with the software. They also oversee the manufacturing process for the computer hardware.

• Salary with a Computer Science PhD: $128,170
• Job Outlook: 2%job growth from 2020 to 2030
• Number of Jobs: 4,500
• Highest-Paying States: California, Washington, Virginia, Oregon, and Nevada

Computer network architects are responsible for modeling, securing, and installing networks in local area networks (LANs) and wide-area networks (WANs). They also conduct extensive research to create new networking technologies. You can become a computer network architect with the right technical and business skills. 

• Salary with a Computer Science PhD: $120,520
• Job Outlook: 5% job growth from 2020 to 2030
• Number of Jobs: 11,000
• Highest-Paying States: New Jersey, Rhode Island, Delaware, Virginia, and Maryland 

Software developers create, test, and maintain software programs. They also upgrade existing software to smoothen the user experience. Software developers create a wide range of models to demonstrate their software applications and have a solid knowledge of advanced programming languages. 

• Salary with a Computer Science PhD: $110,140
• Number of Jobs: 189,200
• Highest-Paying States: California, Washington, Maryland, New York, and Rhode Island

Is It Worth It to Do a PhD in Computer Science Online?

Yes, it is worth it to do a PhD in Computer Science online. Getting a doctoral degree can open you up to various opportunities in the tech industry. You can learn advanced skills and gain expertise in machine learning, artificial intelligence, or network security. An online PhD can set you up for the best tech jobs .

Computer science is a booming career option. According to Statista, the number of tech workers is likely to reach 5.2 million in 2020 and 6 million by 2030 . With a PhD, you can kickstart your career in tech. Keep in mind that a PhD is no piece of cake, so you can always consider other alternatives like coding bootcamp.

Additional Reading About Computer Science

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Online PhD in Computer Science FAQ

Yes, you can get a PhD in Computer Science online. Online doctoral programs are becoming more popular due to being highly flexible and customizable. When looking for an online PhD, you should only consider accredited online schools . Studying at an accredited university ensures that you will get a quality education.

Yes, a PhD in Computer Science is worth it. Computer science is one of the fastest-growing fields in the world. According to the Bureau of Labor Statistics, computer science and information technology jobs will witness a 13 percent growth by 2030. A PhD can help you learn advanced skills and get you a high-level position with a competitive salary.

A PhD in Computer Science is usually three to seven years long. Doctoral students must complete 60-70 credit hours to finish the graduate program. The program length depends on your chosen university. Some universities offer personalized programs that students can design according to their career goals.

Yes, a PhD in Computer Science is harder than an undergraduate and master’s degree program. However, with consistent hard work and dedication, you can gain a solid knowledge of computer science. You will also develop advanced skills and work on a research project.

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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]

253 Cory Hall

Berkeley, CA 94720

At a Glance

Admit Term(s)

Application Deadline

December 11, 2023

Degree Type(s)

Doctoral / PhD

Degree Awarded

GRE Requirements

CS Faculty List

Pieter Abbeel

Professor 746 Sutardja Dai Hall, (510) 642-7034; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 2008, Ph.D., Computer Science, Stanford University; 2000, M.S., Electrical Engineering, KU Leuven, Belgium Office Hours: arrange via email Teaching Schedule (Spring 2024): CS 294-158. Deep Unsupervised Learning , Th 14:00-16:59, Sutardja Dai 250 Teaching Schedule (Fall 2024): CS 188. Introduction to Artificial Intelligence , TuTh 15:30-16:59, Dwinelle 155

Below The Line Assistant Professor [email protected] Education: 2020, PhD, EECS, UCLA

Cameron Allen

Lecturer [email protected] Research Interests: Artificial Intelligence (AI) Education: 2023, Ph. D., Computer Science, Brown University; 2018, M.S., Computer Science, Brown University; 2011, B.S., Electrical Engineering, Tufts University Teaching Schedule (Spring 2024): CS 188. Introduction to Artificial Intelligence , TuTh 12:30-13:59, Wheeler 150

Krste Asanović

Professor Emeritus, Professor in the Graduate School 579B Soda Hall, 510-642-6506; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Integrated Circuits (INC) ; Operating Systems & Networking (OSNT) ; Design, Modeling and Analysis (DMA) Education: 1998, PhD, Computer Science, UC Berkeley; 1987, BA, Electrical and Information Sciences, University of Cambridge, UK Office Hours: Tuesday 10-11am Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected]; Ria Melendres Briggs, 563 Soda, (510) 643-1455, [email protected]

Babak Ayazifar

Teaching Professor 517 Cory Hall, 510-642-9945; [email protected] Research Interests: Education (EDUC) ; Signal Processing (SP) Education: 2003, Ph.D., Electrical Engineering and Computer Science, Massachusetts Institute of Technology; 1989, B.S., Electrical Engineering, Caltech Teaching Schedule (Spring 2024): EECS 16A. Designing Information Devices and Systems I , MoWe 18:30-19:59, Pimentel 1 Teaching Schedule (Fall 2024): EECS 16A. Designing Information Devices and Systems I , MoWe 18:30-19:59, Pimentel 1 EE 197-16. Field Study

Ruzena Bajcsy

Professor Emerita 719 Sutardja Dai Hall, 510-642-9423; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) ; Control, Intelligent Systems, and Robotics (CIR) ; Graphics (GR) ; Human-Computer Interaction (HCI) ; Security (SEC) Education: 1972, Ph.D., Computer Science, Stanford University; 1968, Ph.D., Electrical Engineering, Slovak Technical University, Bratislava, Slovak Republic; 1957, M.S., Electrical Engineering, Slovak Technical University, Bratislava, Slovak Republic Office Hours: M W 9-10, 719 Sutardja Dai

Michael Ball

Lecturer 784 Soda Hall; [email protected] Research Interests: Education (EDUC) ; Human-Computer Interaction (HCI) Education: 2016, MS, Computer Science, UC Berkeley; 2015, BA, Computer Science, UC Berkeley Office Hours: By appointment, please email me., 784 Soda Teaching Schedule (Spring 2024): CS 169L. Software Engineering Team Project , MoFr 10:30-11:59, Soda 405 Teaching Schedule (Fall 2024): CS 169A. Introduction to Software Engineering , MoWe 17:00-18:29, Genetics & Plant Bio 100 CS 375. Teaching Techniques for Computer Science , We 14:00-15:59, Wheeler 212

David Bamman

Below The Line Associate Professor [email protected] Education: 2015, Ph.D., Computer Science (Language Technologies Institute), Carnegie Mellon University; 2006, M.A., Applied Linguistics, Boston University; 1998, B.A., Classics, University of Wisconsin-Madison

Brian A. Barsky

Professor Emeritus, Professor in the Graduate School 443 Soda Hall; [email protected] Research Interests: Graphics (GR) ; Biosystems & Computational Biology (BIO) ; Human-Computer Interaction (HCI) ; Signal Processing (SP) Education: 1981, Ph.D., Computer Science, University of Utah, Salt Lake City; 1978, M.S., Computer Graphics/Computer Science, Cornell University; 1976, B.Sc., Mathematics/Computer Science, McGill University; 1973, D.C.S., Engineering, McGill University Office Hours: By email appointment only Teaching Schedule (Spring 2024): CS 198-57. Directed Group Studies for Advanced Undergraduates , Mo 14:00-15:59, Soda 438 CS 298-57. Assistive Technology , Mo 14:00-15:59, Soda 438

Peter Bartlett

Professor 723 Sutardja Dai Hall, 510-642-7780; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 1992, Ph.D., Electrical Engineering, University of Queensland, Australia

Alexandre Bayen

Professor [email protected] Research Interests: Control, Intelligent Systems, and Robotics (CIR) ; Artificial Intelligence (AI) ; Cyber-Physical Systems and Design Automation (CPSDA) Education: 2003, Ph.D., Aeronautics and Astronautics, Stanford University; 1999, M.S., Aeronautics and Astronautics, Stanford University; 1998, Engineering, Applied Mathematics, Ecole Polytechnique, France

Manuel Blum

Professor Emeritus 621 Soda Hall; [email protected] Education: 1964, Ph.D., Mathematics, MIT; 1961, M.S., Electrical Engineering, MIT; 1959, B.S., Electrical Engineering, MIT

Christian Borgs

Professor 8060 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Theory (THY) Education: 1987, PhD, Mathematical Physics, Max-Planck-Institute and Universitat Munchen Teaching Schedule (Spring 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 15:30-16:59, Li Ka Shing 245

Eric Brewer

Professor Emeritus 417 Sutardja Dai Hall, 510-642-8143; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Power and Energy (ENE) ; Security (SEC) Education: 1994, Ph.D., EECS, MIT; 1989, B.S., EECS, UC Berkeley Office Hours: M 2:30-3:30, Th 1-2, 623 Soda

Aydin Buluç

Adjunct Assistant Professor [email protected] Research Interests: Scientific Computing (SCI) ; Programming Systems (PS) ; Biosystems & Computational Biology (BIO) ; Computer Architecture & Engineering (ARC) Education: 2010, Ph.D., Computer Science, University of California, Santa Barbara Teaching Schedule (Spring 2024): CS C267. Applications of Parallel Computers , TuTh 11:00-12:29, Soda 306

John F. Canny

Professor 637 Soda Hall, 510-642-9955; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Graphics (GR) ; Human-Computer Interaction (HCI) ; Security (SEC) Education: 1987, Ph.D., Electrical Engineering, MIT; 1983, M.S., Electrical Engineering, MIT; 1980, B.E. (Hons), Electrical Engineering, Adelaide University; 1979, B.Sc., Computer Science and Theoretical Physics, Adelaide University

Sarah Chasins

Assistant Professor Research Interests: Programming Systems (PS) ; Human-Computer Interaction (HCI) Education: 2019, Ph.D., Computer Science, University of California, Berkeley; 2012, B.A., Computer Science, Psychology, Swarthmore College Teaching Schedule (Spring 2024): CS 294-184. Building User-Centered Programming Tools , TuTh 14:00-15:29, Soda 320

Jennifer Chayes

Professor, Dean [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS) ; Theory (THY) ; Biosystems & Computational Biology (BIO) Education: 1983, PhD, Mathematical Physics, Princeton University; 1979, BA, Biology and Physics, Wesleyan University

Below The Line Assistant Professor 120D Warren Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) Education: 2022, PhD, Computer Science and Electrical Engineering, MIT; 2014, AB/SM, Applied Math, Harvard

Alvin Cheung

Associate Professor 785 Soda Hall; [email protected] Research Interests: Database Management Systems (DBMS) ; Programming Systems (PS) Education: 2015, Ph.D., Computer Science, MIT Teaching Schedule (Fall 2024): CS 186. Introduction to Database Systems , MoWe 10:00-11:29, Soda 306

Alessandro Chiesa

Adjunct Associate Professor 683 Soda Hall; [email protected] Research Interests: Security (SEC) ; Theory (THY) Education: 2014, Ph.D., Computer Science, MIT; 2010, M.Eng., Computer Science, MIT; 2009, B.S., Computer Science and Mathematics, MIT

Michael J. Clancy

Teaching Professor Emeritus 784 Soda Hall, 510-642-7017; [email protected] Research Interests: Education (EDUC) Education: 1971, B.S., Mathematics, University of Illinois, Champaign/Urbana Office Hours: by appointment, 784 Soda

Michael Cohen

Lecturer [email protected] Research Interests: Artificial Intelligence (AI) Education: 2023, Ph. D., Engineering Science, Oxford University; 2019, M.S., Computing, Australian National University; 2015, B.A., Chemistry, Yale Teaching Schedule (Spring 2024): CS 188. Introduction to Artificial Intelligence , TuTh 12:30-13:59, Wheeler 150

Phillip Colella

Professor in Residence Emeritus MS50A-1148 Lawrence Berkeley National Laboratory, 486-5412; [email protected]

Natacha Crooks

Assistant Professor [email protected] Research Interests: Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 2019, Ph.D., Distributed Systems, University of Texas; 2012, BA, Computer Science and Law, University of Cambridge Teaching Schedule (Spring 2024): CS 294-234. Distributed Systems and Distributed Computing , Mo 09:00-11:59, Soda 320

David E. Culler

Professor Emeritus 783 Soda Hall; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Power and Energy (ENE) ; Operating Systems & Networking (OSNT) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Programming Systems (PS) ; Security (SEC) Education: 1989, Ph.D., MIT; 1985, M.S., MIT; 1980, B.A., U.C. Berkeley

Trevor Darrell

Professor in Residence 8010 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 1996, PhD, MAS, MIT; 1988, BSE, CS, U.Penn. Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310

James Demmel

Professor 564 Soda Hall, 510-643-5386; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Scientific Computing (SCI) Education: 1983, Ph.D., Computer Science, UC Berkeley; 1975, B.S., Mathematics, Caltech Office Hours: Wed, 8:30 - 9:30 am, 564 Soda Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected] Teaching Schedule (Spring 2024): CS C267. Applications of Parallel Computers , TuTh 11:00-12:29, Soda 306

John DeNero

Associate Teaching Professor [email protected] Research Interests: Artificial Intelligence (AI) ; Education (EDUC) Education: 2010, Ph.D., Computer Science, University of California, Berkeley; 2002, M.A., Philosophy, Stanford University; 2001, B.S., Mathematical & Computational Science and Symbolic Systems, Stanford University Office Hours: See Homepage, 781 Soda Teaching Schedule (Spring 2024): CS 47A. Completion of Work in Computer Science 61A CS 61A. The Structure and Interpretation of Computer Programs , MoWeFr 14:00-14:59, Pimentel 1 Teaching Schedule (Fall 2024): CS 61A. The Structure and Interpretation of Computer Programs , MoWeFr 13:00-13:59, Wheeler 150

Anca Dragan

Associate Professor Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Human-Computer Interaction (HCI) Education: 2015, PhD, Robotics, Carnegie Mellon University; 2009, B.S., Computer Science, Jacobs University, Bremen

Prabal Dutta

Associate Professor 550C Cory Hall; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Power and Energy (ENE) ; Operating Systems & Networking (OSNT) Education: 2009, PhD, Computer Science, University of California, Berkeley; 1997, B.S., Electrical and Computer Engineering, Ohio State University Assistants: Sarah Gaugler, [email protected] Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310 EE 375. Teaching Techniques for Electrical Engineering Teaching Schedule (Fall 2024): CS C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306 EECS 149. Introduction to Embedded and Cyber Physical Systems , TuTh 14:00-15:29, Soda 306 EE C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306 EE 375. Teaching Techniques for Electrical Engineering

Alexei (Alyosha) Efros

Professor 724 Sutardja Dai Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Graphics (GR) Education: 2003, PhD, Computer Science, UC Berkeley; 1997, BS, Computer Science, University of Utah Teaching Schedule (Spring 2024): CS C280. Computer Vision , MoWe 12:30-13:59, Berkeley Way West 1102 Teaching Schedule (Fall 2024): CS 180. Intro to Computer Vision and Computational Photography , MoWe 17:00-18:29, Li Ka Shing 245 CS 280A. Intro to Computer Vision and Computational Photography , MoWe 17:00-18:29, Li Ka Shing 245

Laurent El Ghaoui

Professor Emeritus 421 Sutardja Dai Hall; [email protected] Research Interests: Control, Intelligent Systems, and Robotics (CIR) Education: 1990, Ph.D., Aeronautics and Astronautics, Stanford; 1985, B.S., Mathematics, Ecole Polytechnique Office Hours: Wed., 9:00-10:00am, 421 Sutardja Dai

Professor 203A South Hall; [email protected] Research Interests: Graphics (GR) Education: 1997, PhD, Computer Science, University of Pennsylvania

Richard J. Fateman

Professor Emeritus 441 Soda Hall, 510-847-2368; [email protected] Research Interests: Artificial Intelligence (AI) ; Scientific Computing (SCI) Office Hours: BY APPT, 441 Soda

Jerome A. Feldman

Professor Emeritus 739 Soda Hall, 510-666-2900; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) ; Security (SEC) Education: 1964, Ph.D., Computer Science and Mathematics, Carnegie Mellon University; 1961, M.S., Mathematics, University of Pittsburgh; 1960, B.S., Physics, University of Rochester

Domenico Ferrari

Professor Emeritus

Christopher Fletcher

Associate Professor [email protected] Research Interests: Computer Architecture & Engineering (ARC) Education: 2016, Ph.D., EECS, MIT; 2013, S.M., EECS, MIT; 2010, B.S., EECS, UC Berkeley Teaching Schedule (Spring 2024): CS 152. Computer Architecture and Engineering , TuTh 11:00-12:29, North Gate 105 CS 252A. Graduate Computer Architecture , TuTh 11:00-12:29, North Gate 105 Teaching Schedule (Fall 2024): EECS 151. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 151LA. Application Specific Integrated Circuits Laboratory , Mo 17:00-19:59, Cory 111 EECS 151LA-2. Application Specific Integrated Circuits Laboratory , Th 14:00-16:59, Cory 111 EECS 151LA-3. Application Specific Integrated Circuits Laboratory , Fr 11:00-13:59, Cory 111 EECS 151LB. Field-Programmable Gate Array Laboratory , Tu 14:00-16:59, Cory 111 EECS 151LB-2. Field-Programmable Gate Array Laboratory , We 17:00-19:59, Cory 111 EECS 151LB-3. Field-Programmable Gate Array Laboratory , Fr 08:00-10:59, Cory 111 EECS 151LB-4. Field-Programmable Gate Array Laboratory , Tu 17:00-19:59, Cory 111 EECS 251A. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 251LA-101. Introduction to Digital Design and Integrated Circuits Lab , Mo 17:00-19:59, Cory 111 EECS 251LA-102. Introduction to Digital Design and Integrated Circuits Lab , Th 14:00-16:59, Cory 111 EECS 251LA-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 11:00-13:59, Cory 111 EECS 251LB-101. Introduction to Digital Design and Integrated Circuits Lab , Tu 14:00-16:59, Cory 111 EECS 251LB-102. Introduction to Digital Design and Integrated Circuits Lab , We 17:00-19:59, Cory 111 EECS 251LB-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 08:00-10:59, Cory 111

Armando Fox

Professor 581 Soda Hall, 510-642-6820; [email protected] Research Interests: Programming Systems (PS) ; Education (EDUC) ; Human-Computer Interaction (HCI) Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected] Teaching Schedule (Spring 2024): CS 169L. Software Engineering Team Project , MoFr 10:30-11:59, Soda 405 CS 194-244. Special Topics , Mo 14:30-15:59, Soda 606 CS 194-245. Special Topics , Mo 14:30-15:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 294-245. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 375. Teaching Techniques for Computer Science , Fr 13:00-14:59, Soda 438

Gerald Friedland

Adjunct Assistant Professor 424 Sutardja Dai Hall; [email protected] Research Interests: Signal Processing (SP) ; Artificial Intelligence (AI) ; Information, Data, Network, and Communication Sciences (IDNCS) Education: 2006, Ph.D., Computer Science, Freie Universitat Berlin; 2002, MSc, Computer Science, Freie Universitat Berlin Teaching Schedule (Spring 2024): CS 294-82. Experimental Design for Machine Learning on Multimedia Data , Fr 15:00-16:29, Soda 306

Jack Gallant

Below the Line, Professor [email protected] Research Interests: Biosystems & Computational Biology (BIO) Education: 1995, Post-doc, Systems & Computational Neuroscience, Caltech & Wash Univ. Med. Schl.; 1988, PhD, Experimental Psychology, Yale University

Teaching Professor 777 Soda Hall, 510-517-4041; [email protected] Research Interests: Education (EDUC) ; Graphics (GR) Education: 2000, Ph.D., Computer Science, UC Berkeley; 1995, M.S., Computer Science, UC Berkeley; 1990, B.S., Computer Science, MIT; 1990, B.S., Electrical Engineering, MIT Office Hours: CS10: W 2-3pm, 777 Soda Teaching Schedule (Spring 2024): CS 10. The Beauty and Joy of Computing , MoWe 13:00-13:59, Soda 306 CS 194-244. Special Topics , Mo 14:30-15:59, Soda 606 CS 194-245. Special Topics , Mo 14:30-15:59, Soda 606 CS 198-2. Gamescrafters , MoWeFr 11:00-11:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 294-245. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 Teaching Schedule (Fall 2024): CS 10. The Beauty and Joy of Computing , MoWe 13:00-13:59, Hearst Field Annex A1 CS 61C. Great Ideas of Computer Architecture (Machine Structures) , MoWeFr 10:00-10:59, Dwinelle 155 CS 194-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606 CS 198-2. Directed Group Studies for Advanced Undergraduates , MoWeFr 11:00-11:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:00-15:29, Soda 606

Sanjam Garg

Associate Professor 685 Soda Hall; [email protected] Research Interests: Theory (THY) ; Security (SEC) Education: 2013, Ph.D., Computer Science, University of California, Los Angeles; 2008, B.Tech, Computer Science and Engineering, Indian Institute of Technology, Delhi Office Hours: See Homepage Teaching Schedule (Spring 2024): CS 171. Cryptography , MoWe 11:30-12:59, Soda 306 Teaching Schedule (Fall 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 14:00-15:29, Valley Life Sciences 2050

Adjunct Assistant Professor [email protected] Research Interests: Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 2006, PhD, Computer Science, KTH/Royal Institute of Technology; 2002, MBA, Logistics and Marketing, Mid-Sweden University; 2002, MSc, Computer Engineering, Mid-Sweden University Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310

Ken Goldberg

Professor 425 Sutardja Dai Hall, 510-643-9565; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Human-Computer Interaction (HCI) Education: 1990, PhD, Computer Science, Carnegie Mellon University; 1984, BSEE, Electrical Engineering, University of Pennsylvania; 1984, BSE, Economics, UPenn - Wharton Office Hours: see personal homepage

Shafi Goldwasser

Professor 689 Soda Hall; Research Interests: Theory (THY) Education: 1984, Ph.D., Computer Science, UC Berkeley; 1981, M.S., Computer Science, UC Berkeley; 1979, B.S., Mathematics and Science, Carnegie Mellon

Joseph Gonzalez

Associate Professor 773 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 2012, Ph.D., Machine Learning, Carnegie Mellon University Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Fall 2024): CS 294-162. Machine Learning Systems , MoWe 14:00-15:29, Soda 310

Susan L. Graham

Professor Emerita 751 Soda Hall, 510-642-2059; [email protected] Research Interests: Graphics (GR) ; Human-Computer Interaction (HCI) ; Programming Systems (PS) ; Scientific Computing (SCI) Office Hours: by appointment, 751 Soda

Venkatesan Guruswami

Professor [email protected] Research Interests: Theory (THY) ; Information, Data, Network, and Communication Sciences (IDNCS) Education: 2001, PhD, Computer Science, MIT

Nika Haghtalab

Assistant Professor 8028 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Theory (THY) Education: 2018, Ph.D., Computer Science, Carnegie Mellon University

Moritz Hardt

Associate Professor [email protected] Education: 2011, PhD, Computer Science, Princeton University

Michael A. Harrison

Professor Emeritus [email protected] Research Interests: Programming Systems (PS) ; Theory (THY) Education: 1963, Ph.D., Communication Sciences, University of Michigan; 1959, M.S., Electrical Engineering and Computing, Case Western Reserve University; 1958, B.S., Electrical Engineering, Case Western Reserve University

Björn Hartmann

Associate Professor 220A Jacobs Hall, 415 868 5720; [email protected] Research Interests: Human-Computer Interaction (HCI) ; Programming Systems (PS) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Graphics (GR) Education: 2009, Ph.D., Computer Science, Stanford University; 2002, MSE, Computer and Information Science, University of Pennsylvania; 2001, BSE/B.A., Digital Media Design/Communication, University of Pennsylvania Office Hours: Fall'19: Thu 5-6pm, 220A Jacobs Teaching Schedule (Spring 2024): CS 160. User Interface Design and Development , TuTh 14:00-15:29, Jacobs Hall 310 CS 260A. User Interface Design and Development , TuTh 14:00-15:29, Jacobs Hall 310 Teaching Schedule (Fall 2024): CS 260B. Human-Computer Interaction Research , TuTh 12:30-13:59, Soda 405

Brian Harvey

Teaching Professor Emeritus 441 Soda Hall; [email protected] Research Interests: Education (EDUC) Education: 1990, MA, Clinical Psychology, New College of California; 1985, PhD, Science & Mathematics Education, UC Berkeley; 1975, MS, Computer Science, Stanford; 1969, BS, Mathematics, MIT Office Hours: by appointment, 441 Soda

Marti Hearst

Professor 307b South Hall, 510-642-8016; [email protected] Research Interests: Human-Computer Interaction (HCI) ; Artificial Intelligence (AI) Education: 1994, Ph.D., Computer Science, UC Berkeley; 1989, M.S., Computer Science, UC Berkeley; 1985, B.A., Computer Science, UC Berkeley Office Hours: See home page, 307B South

Joseph M. Hellerstein

Professor 789 Soda Hall, 510-643-4011; Research Interests: Database Management Systems (DBMS) ; Operating Systems & Networking (OSNT) Education: 1995, Ph.D., Computer Science, University of Wisconsin-Madison; 1992, MS, Computer Science, UC Berkeley; 1990, AB, Computer Science, Harvard University Teaching Schedule (Spring 2024): CS 286B. Implementation of Data Base Systems, TuTh 14:00-15:29, Soda 310 CS 298-12. Group Studies Seminars, or Group Research , We 11:00-11:59, Soda 380 Teaching Schedule (Fall 2024): CS 298-12. Database Seminar , We 11:00-11:59, Soda 438

Paul N. Hilfinger

Teaching Professor, Retired 787 Soda Hall, 510-642-8401; [email protected] Research Interests: Programming Systems (PS) ; Scientific Computing (SCI) Education: 1980, Ph.D., Computer Science, Carnegie-Mellon University; 1973, AB, Mathematics, Princeton University

Associate Teaching Professor 779 Soda Hall; [email protected] Research Interests: Education (EDUC) Education: 2011, Ph.D., Electrical Engineering And Computer Science, UC Berkeley; 2003, B.S., Electrical and Computer Engineering, University of Texas at Austin Office Hours: No office hours, on sabbatical Teaching Schedule (Fall 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 17:00-18:29, Pimentel 1

Christopher Hunn

Lecturer [email protected] Teaching Schedule (Spring 2024): CS 370. Adaptive Instruction Methods in Computer Science , Tu 17:00-18:59, Wheeler 212 CS 370-2. Adaptive Instruction Methods in Computer Science , Th 17:00-18:59, Social Sciences Building 110 Teaching Schedule (Fall 2024): CS 370. Adaptive Instruction Methods in Computer Science , Tu 17:00-18:59, Wheeler 212 CS 370-2. Adaptive Instruction Methods in Computer Science , Th 17:00-18:59, Wheeler 212

Nilah Ioannidis

Assistant Professor 513 Soda Hall; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Artificial Intelligence (AI) Education: 2013, Ph.D., Biophysics, Harvard University

Lakshya Jain

Lecturer [email protected] Education: 2020, M.S., Computer Science, University of California, Berkeley Teaching Schedule (Spring 2024): CS 186. Introduction to Database Systems , MoWe 09:30-10:59,

Jiantao Jiao

Assistant Professor 257M Cory Hall; Research Interests: Artificial Intelligence (AI) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Control, Intelligent Systems, and Robotics (CIR) ; Theory (THY) ; Signal Processing (SP) ; Operating Systems & Networking (OSNT) ; Database Management Systems (DBMS) ; Cyber-Physical Systems and Design Automation (CPSDA) ; Security (SEC) ; Power and Energy (ENE) ; Programming Systems (PS) Education: 2018, Ph.D., Electrical Engineering, Stanford University Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected] Teaching Schedule (Spring 2024): EECS 126. Probability and Random Processes , TuTh 14:00-15:29, Physics Building 4 Teaching Schedule (Fall 2024): EECS 126. Probability and Random Processes , TuTh 11:00-12:29, Valley Life Sciences 2040

Michael Jordan

Professor 387 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Biosystems & Computational Biology (BIO) ; Control, Intelligent Systems, and Robotics (CIR) ; Signal Processing (SP) ; Theory (THY) Education: 1985, Ph.D., Cognitive Science, UC San Diego; 1980, M.S., Mathematics, Arizona State University; 1978, B.S., Psychology, Louisiana State University Office Hours: by appointment

Anthony D. Joseph

Professor 465 Soda Hall, 510-643-7212; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 1998, Ph.D., Computer Science, MIT; 1988, S.M./S.B., Electrical Engineering and Computer Science/Computer Science and Engineering, MIT Office Hours: By appointment only - please email for appointment Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

William M. Kahan

Professor Emeritus 513 Soda Hall, 510-642-5638; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Scientific Computing (SCI) Education: 1958, Ph.D., Mathematics, University of Toronto; 1956, Master's, Mathematics, University of Toronto; 1954, B.A., Mathematics, University of Toronto Office Hours: Irregular- phone for app't

Angjoo Kanazawa

Assistant Professor 8014 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Graphics (GR) Education: 2017, Ph.D., Computer Science, University of Maryland, College Park

Lecturer [email protected] Education: 2022, M.S., Electrical Engineering and Computer Science, UC Berkeley; 2021, B.A., Computer Science, UC Berkeley; 2021, B.A., Data Science, UC Berkeley Teaching Schedule (Spring 2024): CS 47B. Completion of Work in Computer Science 61B CS 61B. Data Structures , MoWeFr 13:00-13:59, Dwinelle 155 CS 161. Computer Security , MoWe 18:30-19:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 61B. Data Structures , MoWeFr 14:00-14:59, Wheeler 150

Richard M. Karp

Professor Emeritus 621 Soda Hall, 510-642-5799; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Operating Systems & Networking (OSNT) ; Theory (THY) Education: 1959, Ph.D., Applied Mathematics, Harvard; 1956, S.M., Applied Mathematics, Harvard; 1955, A.B., Mathematics, Harvard Office Hours: M 1:30-2:30, 621 Soda Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected]

Randy H. Katz

Professor Emeritus 751 Soda Hall, 510-642-8778; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Operating Systems & Networking (OSNT) Education: 1980, PhD, Computer Science, UC Berkeley; 1978, MS, Computer Science, UC Berkeley; 1976, AB, Computer Science & Math, Cornell University Office Hours: By appointment. Contact [email protected] Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

Kurt Keutzer

Professor Emeritus, Professor in the Graduate School [email protected] Research Interests: Artificial Intelligence (AI) ; Computer Architecture & Engineering (ARC) ; Scientific Computing (SCI) Education: 1984, PhD, Computer Science, Indiana University Office Hours: by appointment only Assistants: Roxana Infante, 563 Soda, 643-1455, [email protected] Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310

Daniel Klein

Professor 8058 Berkeley Way West, 510-643-0805; [email protected] Research Interests: Artificial Intelligence (AI) Education: 2004, M.S./Ph.D., Computer Science, Stanford University; 1999, M.St, Linguistics, Oxford University; 1998, B.A., Math, CS, Linguistics, Cornell University Office Hours: Tuesday 2pm-3:30pm (may be in 778 SDH), 730 Sutardja Dai

Aditi Krishnapriyan

Below The Line Assistant Professor [email protected] Research Interests: Artificial Intelligence (AI) ; Scientific Computing (SCI) Education: 2020, Ph.D., ㅤ, Stanford University Teaching Schedule (Spring 2024): CS 294-254. Physics Inspired Deep Learning , TuTh 12:30-13:59, Moffitt Library 103

John D. Kubiatowicz

Professor 673 Soda Hall, 510-643-6817; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 1998, Ph.D., Electrical Engineering and Computer Science (minor in Physics), M.I.T.; 1993, M.S., Electrical Engineering and Computer Science, M.I.T.; 1987, B.S., EE and Physics, M.I.T. Office Hours: T/Th 3pm-4pm, 673 Soda Teaching Schedule (Spring 2024): CS 162. Operating Systems and System Programming , TuTh 12:30-13:59, Dwinelle 155

Edward A. Lee

Professor Emeritus, Professor in the Graduate School 545Q Cory Hall, 510-643-3728; [email protected] Research Interests: Cyber-Physical Systems and Design Automation (CPSDA) ; Programming Systems (PS) ; Signal Processing (SP) ; Computer Architecture & Engineering (ARC) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Design, Modeling and Analysis (DMA) Education: 1986, PhD, EECS, UC Berkeley; 1981, SM, EECS, MIT; 1979, BS, CS and Eng. & Applied Science, Yale Office Hours: By appointment, 545Q Cory

Sergey Levine

Associate Professor 8056 Berkeley Way West; Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 2014, Ph.D., Computer Science, Stanford University; 2009, B.S/M.S., Computer Science, Stanford University

Jennifer Listgarten

Professor 8022 Berkeley Way West; Research Interests: Biosystems & Computational Biology (BIO) ; Artificial Intelligence (AI) Education: 2007, Ph.D., Computer Science, University of Toronto Teaching Schedule (Spring 2024): CS 294-150. AI meets Biology and Chemistry , Mo 14:00-16:59, Berkeley Way West 1217 Teaching Schedule (Fall 2024): CS 189. Introduction to Machine Learning , TuTh 14:00-15:29, Haas Faculty Wing F295 CS 289A. Introduction to Machine Learning , TuTh 14:00-15:29, Haas Faculty Wing F295

Michael Lustig

Professor 506 Cory Hall; [email protected] Education: 2008, PhD, EE, Stanford University; 2004, MSc, EE, Stanford University; 2001, BSc, EE, 🇮🇱💔 Technion, Israel Institute of Technology Office Hours: 🇮🇱🇮🇱 Bring those kidnapped by Hammas home! 💔💔 Teaching Schedule (Spring 2024): EECS 16B. Designing Information Devices and Systems II , TuTh 09:30-10:59, Pimentel 1 Teaching Schedule (Fall 2024): EE 197-13. Field Study

Jitendra Malik

Professor 8012 Berkeley Way West, 510-642-7597; 389 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Signal Processing (SP) Education: 1980, B.Tech, Electrical Engineering, Indian Institute of Technology, Kanpur; 1975, Ph.D., Computer Science, Stanford University Office Hours: By appointment only, 722 Sutardja Dai Assistants: Angie Abbatecola, 741 Soda, (510) 643-6413, [email protected]; Alex Sandoval, 510 642-0253, [email protected]

Igor Mordatch

Lecturer [email protected] Research Interests: Artificial Intelligence (AI) Education: 2016, Post-Doctoral Associate, EECS, UC Berkeley; 2015, PhD, Computer Science & Engineering, University of Washington Teaching Schedule (Fall 2024): CS 188. Introduction to Artificial Intelligence , TuTh 15:30-16:59, Dwinelle 155

Jelani Nelson

Professor 633 Soda Hall; [email protected] Research Interests: Theory (THY) ; Database Management Systems (DBMS) Education: 2011, Ph.D., Computer Science, MIT; 2006, M.Eng., Computer Science, MIT; 2005, S.B., Computer Science, Mathematics, MIT Teaching Schedule (Fall 2024): CS 298-3. Group Studies Seminars, or Group Research , We 16:00-16:59, Soda 306

Associate Professor 8062 Berkeley Way West; [email protected] Research Interests: Graphics (GR) ; Signal Processing (SP) ; Artificial Intelligence (AI) Education: 2006, Ph.D., Computer Science, Stanford University; 2006, M.S., Computer Science, Stanford University; 2001, B.S., Mathematical and Computational Science, Stanford University Office Hours: Thursday 1 - 2pm or by appointment Teaching Schedule (Spring 2024): CS 184. Foundations of Computer Graphics , TuTh 11:00-12:29, Dwinelle 145 CS 284A. Foundations of Computer Graphics , TuTh 11:00-12:29, Dwinelle 145 Teaching Schedule (Fall 2024): CS 194-164. Computational Human Vision , Tu 13:00-15:59, Berkeley Way West 1217 CS 294-164. Computational Human Vision , Tu 13:00-15:59, Berkeley Way West 1217

Narges Norouzi

Assistant Teaching Professor 775 Soda Hall; [email protected] Research Interests: Artificial Intelligence (AI) ; Education (EDUC) ; Biosystems & Computational Biology (BIO) Education: 2017, PhD, Computer Engineering, University of Toronto; 2014, MS, Computer Engineering, University of Toronto; 2012, BS, Computer Engineering, Sharif University of Technology Teaching Schedule (Spring 2024): CS 194-244. Special Topics , Mo 14:30-15:59, Soda 606 CS 194-245. Special Topics , Mo 14:30-15:59, Soda 606 CS 294-244. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 CS 294-245. STAR Assessments for Proficiency-Based Learning , Mo 14:30-15:59, Soda 606 Teaching Schedule (Fall 2024): CS 194-271. Research in AI Education , Tu 14:00-15:29, Soda 606 CS 294-271. Research in AI Education , Tu 14:00-15:29, Soda 606

James O'Brien

Professor [email protected] Research Interests: Graphics (GR) ; Artificial Intelligence (AI) ; Human-Computer Interaction (HCI) ; Scientific Computing (SCI) Education: 2000, Ph.D., Computer Science, Georgia Institute of Technology; 1997, M.S., Computer Science, Georgia Institute of Technology; 1992, B.S., Computer Science, Florida International University Teaching Schedule (Spring 2024): CS 284B. Advanced Computer Graphics Algorithms and Techniques , TuTh 12:30-13:59, Soda 405

Christos Papadimitriou

Professor Emeritus [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Theory (THY) Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected]

Aditya Parameswaran

Associate Professor 212 South Hall; [email protected] Research Interests: Database Management Systems (DBMS) ; Human-Computer Interaction (HCI) Education: 2013, PhD, Computer Science, Stanford; 2007, BTech, Computer Science and Engineering, IIT Bombay Office Hours: by appointment Teaching Schedule (Spring 2024): CS 298-12. Group Studies Seminars, or Group Research , We 11:00-11:59, Soda 380 Teaching Schedule (Fall 2024): CS 298-12. Database Seminar , We 11:00-11:59, Soda 438

Beresford N. Parlett

Professor Emeritus 799 Evans Hall;

David A. Patterson

Professor Emeritus 579 Soda Hall, 642-6587; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Operating Systems & Networking (OSNT) Education: 1976, PhD, Computer Science, UCLA; 1970, MS, Computer Science, UCLA; 1969, AB, Mathematics, UCLA Office Hours: Mondays, by appointment, 579 Soda Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

Eric Paulos

Professor [email protected] Research Interests: Human-Computer Interaction (HCI) Office Hours: See Homepage www.paulos.net

Vern Paxson

Professor Emeritus, Professor in the Graduate School 737 Soda Hall, 3-4209; 630 International Computer Science Institute, 666-2882; [email protected] Research Interests: Security (SEC) ; Operating Systems & Networking (OSNT) Office Hours: By appointment via Zoom

Kristofer Pister

Professor 512 Cory Hall; [email protected] Research Interests: Micro/Nano Electro Mechanical Systems (MEMS) ; Control, Intelligent Systems, and Robotics (CIR) ; Integrated Circuits (INC) Education: 1992, Ph.D., EECS, UC Berkeley; 1989, M.S., EECS, UC Berkeley; 1986, B.A., Applied Physics, UC San Diego Office Hours: W 11-12, Th 4:30-5:30, 512 Cory

Raluca Ada Popa

Associate Professor 729 Soda Hall; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 2014, Doctor of Philosophy, Computer Science, Massachusetts Institute of Technology; 2010, Masters of Engineering, Computer Science, Massachusetts Institute of Technology; 2009, Bachelor's degree, Computer Science and Mathematics, Massachusetts Institute of Technology Office Hours: Tuesday 2-3pm, 729 Soda Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Spring 2024): CS 161. Computer Security , MoWe 18:30-19:59, Dwinelle 155

Prasad Raghavendra

Professor 623 Soda Hall; [email protected] Research Interests: Theory (THY) Education: 2009, PhD, Computer Science and Engineering, University of Washington, Seattle; 2007, M.S., Computer Science and Engineering, University of Washington, Seattle; 2005, B.S., Computer Science, Indian Institute of Technology , Madras, India Office Hours: Wed 11-noon, 623 Soda Teaching Schedule (Spring 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 15:30-16:59, Li Ka Shing 245 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438 Teaching Schedule (Fall 2024): CS 170. Efficient Algorithms and Intractable Problems , TuTh 14:00-15:29, Valley Life Sciences 2050 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438

Kannan Ramchandran

Professor 269 Cory Hall, 510-642-2353; [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS) ; Signal Processing (SP) ; Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) Education: 1993, Ph.D., Computer Science, Columbia University; 1984, M.S., Computer Science, Columbia University; 1982, B.E., Computer Science, City College of New York Office Hours: Tu. 3:00-4:00, or by appt., 258 Cory Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected] Teaching Schedule (Fall 2024): EE 120. Signals and Systems , MoWe 15:00-16:59, Valley Life Sciences 2060

Professor 687 Soda Hall, 510-642-4328; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Theory (THY) Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected] Teaching Schedule (Fall 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 17:00-18:29, Pimentel 1 CS 197-70. Field Study CS 270. Combinatorial Algorithms and Data Structures , TuTh 11:00-12:29, Soda 306

Sylvia Ratnasamy

Professor 413 Soda Hall, 2-8905; [email protected] Research Interests: Operating Systems & Networking (OSNT) Assistants: Carlyn Chinen, 510-990-5109, [email protected]; Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Spring 2024): CS 168. Introduction to the Internet: Architecture and Protocols , TuTh 15:30-16:59, Dwinelle 145 Teaching Schedule (Fall 2024): CS 168. Introduction to the Internet: Architecture and Protocols , TuTh 11:00-12:29, Haas Faculty Wing F295

Benjamin Recht

Professor 8008 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Signal Processing (SP) Teaching Schedule (Spring 2024): CS 294-269. The Philosophy and History of Automated Decision Making , Th 14:00-16:59, Teaching Schedule (Fall 2024): EE 227BT. Convex Optimization , TuTh 14:00-15:29, Anthro/Art Practice Bldg 155

Lawrence A. Rowe

Professor Emeritus [email protected] Education: 1976, Ph.D., Information and Computer Science, University of California, Irvine; 1970, B.A., Mathematics, University of California, Irvine

Jaijeet Roychowdhury

Professor 545E Cory Hall, 643-5664; [email protected] Research Interests: Cyber-Physical Systems and Design Automation (CPSDA) ; Integrated Circuits (INC) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Computer Architecture & Engineering (ARC) ; Control, Intelligent Systems, and Robotics (CIR) ; Artificial Intelligence (AI) Education: 1993, PhD, EECS, Berkeley; 1989, MS, EECS, Berkeley; 1987, B.Tech., EE, IIT Kanpur Teaching Schedule (Spring 2024): EECS 219A. Numerical Simulation and Modeling , MoWe 14:00-15:59, Cory 531 EE 290-9. Oscillator Ising Machines: Special Topics , MoWeFr 11:00-11:59, Cory 531 Teaching Schedule (Fall 2024): EE 290-7. Oscillator Ising Machines: Special Topics , MoWeFr 10:00-10:59, Cory 531

Stuart J. Russell

Professor, CS Division Chair 8040 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Biosystems & Computational Biology (BIO) Education: 1986, PhD, Computer Science, Stanford University; 1982, BA Hons (1st class), Physics, Oxford University Teaching Schedule (Spring 2024): CS 298-3. EECS Colloquium , We 16:00-17:29, Soda 306

Anant Sahai

Professor 267 Cory Hall; [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS) ; Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Theory (THY) ; Signal Processing (SP) Education: 2001, PhD, EECS, Massachusetts Institute of Technology; 1996, SM, EECS, Massachusetts Institute of Technology; 1994, BS, EECS, University of California, Berkeley Office Hours: TuTh 11-12, 258 Cory Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected] Teaching Schedule (Spring 2024): EE 226A. Random Processes in Systems , TuTh 09:30-10:59, Cory 540AB EE 290-12. InContext: Understanding in-context learning in language models via simple function classes , We 14:00-15:59, Cory 540AB

Niloufar Salehi

Below The Line Assistant Professor 313 South Hall; [email protected] Research Interests: Human-Computer Interaction (HCI) Education: 2019, Ph.D., Computer Science, Stanford University

Koushik Sen

Professor 735 Soda Hall, 510-642-2420; [email protected] Research Interests: Programming Systems (PS) ; Security (SEC) Office Hours: Fridays 2pm-3pm, 735 Soda Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected] Teaching Schedule (Spring 2024): CS 164. Programming Languages and Compilers , MoWe 10:00-11:29, Soda 306 Teaching Schedule (Fall 2024): CS 164. Programming Languages and Compilers , MoWe 14:00-15:29, Soda 306

Carlo H. Séquin

Professor Emeritus 639 Soda Hall, 510-642-5103; [email protected] Research Interests: Graphics (GR) ; Human-Computer Interaction (HCI) Education: 1969, Ph.D., Experimental Physics, University of Basel, Switzerland Office Hours: see homepage for currently valid time slots, 639 Soda

Sanjit A. Seshia

Professor 566 Cory Hall, 510-643-6968; [email protected] Research Interests: Cyber-Physical Systems and Design Automation (CPSDA) ; Programming Systems (PS) ; Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Security (SEC) ; Theory (THY) Education: 2005, Ph.D., Computer Science, Carnegie Mellon University; 2000, M.S., Computer Science, Carnegie Mellon University; 1998, B.Tech., Computer Science, Institute of Technology, Bombay Office Hours: MW 2:30-3 PM and by appointment, 566 Cory Assistants: Charlotte Jones, 550 Sutardja Dai, 510-664-4203, [email protected] Teaching Schedule (Spring 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 15:30-16:59, Dwinelle 155 EECS 219C. Formal Methods: Specification, Verification, and Synthesis , MoWe 13:00-14:29, Cory 299 Teaching Schedule (Fall 2024): CS C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306 EECS 149. Introduction to Embedded and Cyber Physical Systems , TuTh 14:00-15:29, Soda 306 EE C249A. Introduction to Embedded Systems , TuTh 14:00-15:29, Soda 306

Sophia Shao

Assistant Professor 570 Cory Hall; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Integrated Circuits (INC) ; Cyber-Physical Systems and Design Automation (CPSDA) Education: 2016, Ph.D., Computer Science, Harvard University Teaching Schedule (Spring 2024): EE 290-2. Hardware for Machine Learning , MoWe 09:30-10:59, Cory 293 Teaching Schedule (Fall 2024): EECS 151. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 151LA. Application Specific Integrated Circuits Laboratory , Mo 17:00-19:59, Cory 111 EECS 151LA-2. Application Specific Integrated Circuits Laboratory , Th 14:00-16:59, Cory 111 EECS 151LA-3. Application Specific Integrated Circuits Laboratory , Fr 11:00-13:59, Cory 111 EECS 151LB. Field-Programmable Gate Array Laboratory , Tu 14:00-16:59, Cory 111 EECS 151LB-2. Field-Programmable Gate Array Laboratory , We 17:00-19:59, Cory 111 EECS 151LB-3. Field-Programmable Gate Array Laboratory , Fr 08:00-10:59, Cory 111 EECS 151LB-4. Field-Programmable Gate Array Laboratory , Tu 17:00-19:59, Cory 111 EECS 251A. Introduction to Digital Design and Integrated Circuits , TuTh 09:30-10:59, Mulford 159 EECS 251LA-101. Introduction to Digital Design and Integrated Circuits Lab , Mo 17:00-19:59, Cory 111 EECS 251LA-102. Introduction to Digital Design and Integrated Circuits Lab , Th 14:00-16:59, Cory 111 EECS 251LA-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 11:00-13:59, Cory 111 EECS 251LB-101. Introduction to Digital Design and Integrated Circuits Lab , Tu 14:00-16:59, Cory 111 EECS 251LB-102. Introduction to Digital Design and Integrated Circuits Lab , We 17:00-19:59, Cory 111 EECS 251LB-103. Introduction to Digital Design and Integrated Circuits Lab , Fr 08:00-10:59, Cory 111

Scott Shenker

Professor Emeritus, Professor in the Graduate School 415 Soda Hall, 510-643-3043; [email protected] Research Interests: Operating Systems & Networking (OSNT) Education: 1983, Ph.D., Physics, University of Chicago; 1978, Sc.B., Physics, Brown University Assistants: Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected]

Jonathan Shewchuk

Professor 529 Soda Hall, 510-642-3936; [email protected] Research Interests: Scientific Computing (SCI) ; Theory (THY) ; Graphics (GR) Teaching Schedule (Spring 2024): CS 189. Introduction to Machine Learning , MoWe 18:30-19:59, Wheeler 150 CS 289A. Introduction to Machine Learning , MoWe 18:30-19:59, Wheeler 150

Alistair Sinclair

Professor 677 Soda Hall, 510-643-8144; [email protected] Research Interests: Theory (THY) Office Hours: M 1:30-2:30, Tu 12:45-1:45, 677 Soda Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected] Teaching Schedule (Spring 2024): CS 70. Discrete Mathematics and Probability Theory , TuTh 15:30-16:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 271. Randomness and Computation , TuTh 09:30-10:59, Wheeler 200

Alan J. Smith

Professor Emeritus 192 Soda Hall, 510-642-5290; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Operating Systems & Networking (OSNT) Office Hours: by appointment only - send email or call, 192 Soda

Professor 675 Soda Hall, 510-642-8282; [email protected] Research Interests: Artificial Intelligence (AI) ; Operating Systems & Networking (OSNT) ; Security (SEC) ; Programming Systems (PS) Education: 2002, Ph.D., Computer Science, UC Berkeley; 1999, M.S., Computer Science, Carnegie Mellon University Teaching Schedule (Spring 2024): CS 194-267. Special Topics , Tu 15:30-16:59, Soda 306 CS 294-267. Understanding Large Language Models (LLMs): Foundations and Safety , Tu 15:30-16:59, Soda 306 Teaching Schedule (Fall 2024): CS 194-177. Special Topics on Decentralized Finance , Mo 10:00-11:59, Joan and Sanford I. Weill 101D CS 194-196. Special Topics on Decentralized Intelligence: Large Language Model Agents , Mo 15:00-16:59, Latimer 120 CS 294-177. Special Topics on Decentralized Finance , Mo 10:00-11:59, Joan and Sanford I. Weill 101D CS 294-196. Special Topics on Decentralized Intelligence: Large Language Model Agents , Mo 15:00-16:59, Latimer 120

Yun S. Song

Professor 304B Stanley Hall, 510-642-2351; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Artificial Intelligence (AI) ; Theory (THY) Education: 2001, PhD, Physics, Stanford University; 1997, B.S., Mathematics, MIT; 1996, B.S., Physics, MIT Office Hours: On sabbatical leave for Spring 2024

Costas J. Spanos

Professor Emeritus 510 Cory Hall, 510-643-6776; Research Interests: Power and Energy (ENE) ; Integrated Circuits (INC) ; Physical Electronics (PHY)

Jacob Steinhardt

Assistant Professor 8026 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) ; Information, Data, Network, and Communication Sciences (IDNCS) Education: 2018, PhD, Computer Science, Stanford; 2012, BSc, Mathematics, MIT

Professor 481-2 Soda Hall, 510-643-4007; [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Security (SEC) Education: 2000, Ph.D., Electrical and Computer Engineering, Carnegie Mellon University; 1989, M.S., Computer Science and Control Engineering, Polytechnic University Bucharest Office Hours: Monday 11-12 PM, Location TBD Assistants: Kattt Atchley, 465 Soda, 510-643-3499, [email protected]; Ivan Ortega, 465A Soda Soda, (510) 708-8604, [email protected] Teaching Schedule (Spring 2024): CS 294-194. From Research to Startup , We 17:00-18:29, Soda 310 Teaching Schedule (Fall 2024): CS 162. Operating Systems and System Programming , TuTh 18:30-19:59, Dwinelle 155

Michael Stonebraker

Professor Emeritus Education: 1971, Ph.D., Computer, Information and Control Engineering, University of Michigan; 1966, M.S.E., Electrical Engineering, University of Michigan; 1965, B.S.E., Electrical Engineering, Princeton

Bernd Sturmfels

Professor Emeritus 925 Evans Hall, 510-642 6550; [email protected] Research Interests: Biosystems & Computational Biology (BIO) ; Theory (THY) Education: 1987, Dr.rer.nat., Mathematics, Technical University Darmstadt, Germany; 1987, Ph.D., Mathematics, University of Washington, Seattle; 1985, Diplom, Mathematics and Computer Science, Technical University Darmstadt, Germany Office Hours: T 9:45-11am, F 10:30-11:30am, 925 Evans

Assistant Professor 8052 Berkeley Way West; [email protected] Research Interests: Artificial Intelligence (AI) Education: 2022, PhD, Computer Science, Cornell University; 2016, BS, Computer Science and Engineering, Ohio State University Teaching Schedule (Spring 2024): CS 294-258. Language Agents in Interaction , TuTh 15:30-16:59, Soda 310 Teaching Schedule (Fall 2024): CS 288. Natural Language Processing , TuTh 12:30-13:59, Donner Lab 155

Avishay Tal

Assistant Professor 635 Soda Hall; [email protected] Research Interests: Theory (THY) Education: 2015, PhD, Computer Science, Weizmann Institute of Science; 2012, MS, Computer Science, The Technion, Haifa, Israel; 2007, BA, Mathematics, The Technion, Haifa, Israel; 2005, BS, Software Engineering, The Technion, Haifa, Israel Teaching Schedule (Spring 2024): CS 278. Machine-Based Complexity Theory , TuTh 14:00-15:29, Soda 405 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438 Teaching Schedule (Fall 2024): CS 172. Computability and Complexity , TuTh 17:00-18:29, Lewis 9 CS 298-2. Group Studies Seminars, or Group Research , We 12:00-13:29, Soda 438

Claire Tomlin

Professor, Chair 721 Sutardja Dai Hall, 510-643-6610; [email protected] Research Interests: Control, Intelligent Systems, and Robotics (CIR) ; Biosystems & Computational Biology (BIO) Education: 1998, Ph.D., EECS, UC Berkeley; 1993, M.Sc., Electrical Engineering, Imperial College, London; 1992, B.A.Sc., Electrical Engineering, University of Waterloo Office Hours: By Appointment, 721 Sutardja Dai Assistants: Jessica Gamble, 337 Cory, 510-643-5105, [email protected]; Alex Sandoval, 510 642-0253, [email protected]

Umesh Vazirani

Professor 671 Soda Hall, 510-642-0572; [email protected] Research Interests: Theory (THY) ; Security (SEC) Education: 1986, Ph.D., Computer Science, UC Berkeley; 1981, B.S., MIT Assistants: Olivia Chen, 695 Soda, (510) 642-9467, [email protected]

Allon Wagner

Assistant Professor 304A Stanley Hall; [email protected] Research Interests: Biosystems & Computational Biology (BIO) Education: 2021, PhD, Computer Science, UC Berkeley

David A. Wagner

Professor 733 Soda Hall, 510-642-2758; [email protected] Research Interests: Security (SEC) Education: 2000, Ph.D., Computer Science, UC Berkeley; 1999, M.S., Computer Science, UC Berkeley; 1995, A.B., Mathematics, Princeton University Teaching Schedule (Fall 2024): CS 161. Computer Security , TuTh 09:30-10:59, Hearst Field Annex A1

Martin Wainwright

Professor 263 Cory Hall, 510-643-1978; [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Information, Data, Network, and Communication Sciences (IDNCS) ; Signal Processing (SP) ; Theory (THY) Office Hours: by appointment Assistants: Kim Kail, 253 Cory, 510-643-6633, [email protected]

Laura Waller

Professor 514 Cory Hall, (510) 642-2753; [email protected] Research Interests: Physical Electronics (PHY) ; Signal Processing (SP) ; Biosystems & Computational Biology (BIO) ; Graphics (GR) Education: 2010, Ph.D., Electrical Engineering and Computer Science, MIT; 2005, M.Eng., Electrical Engineering and Computer Science, MIT; 2004, B.S., Electrical Engineering and Computer Science, MIT Office Hours: Tuesday and Thursdays 11:00-11:30am, 514 Cory

Jean Walrand

Professor Emeritus 257 Cory Hall, 510-219-5821; [email protected] Research Interests: Information, Data, Network, and Communication Sciences (IDNCS)

John Wawrzynek

Professor 631 Soda Hall, 510-643-9434; [email protected] Research Interests: Computer Architecture & Engineering (ARC) ; Design, Modeling and Analysis (DMA) Office Hours: Tues., 1:00-2:00pm and by appointment, 631 Soda Teaching Schedule (Spring 2024): EECS 151. Introduction to Digital Design and Integrated Circuits , MoWe 14:00-15:29, Soda 306 EECS 151LA-101. Application Specific Integrated Circuits Laboratory , Tu 11:00-13:59, Cory 111 EECS 151LA-102. Application Specific Integrated Circuits Laboratory , Tu 14:00-16:59, Cory 111 EECS 151LB. Field-Programmable Gate Array Laboratory , Mo 11:00-13:59, Cory 111 EECS 151LB-2. Field-Programmable Gate Array Laboratory , Tu 08:00-10:59, Cory 111 EECS 151LB-3. Field-Programmable Gate Array Laboratory , Mo 17:00-19:59, Cory 111 EECS 151LB-4. Field-Programmable Gate Array Laboratory , Mo 08:00-10:59, Cory 111 EECS 251A. Introduction to Digital Design and Integrated Circuits , MoWe 14:00-15:29, Soda 306 EECS 251LA-101. Introduction to Digital Design and Integrated Circuits Lab , Tu 11:00-13:59, Cory 111 EECS 251LA-102. Introduction to Digital Design and Integrated Circuits Lab , Tu 14:00-16:59, Cory 111 EECS 251LB-101. Introduction to Digital Design and Integrated Circuits Lab , Mo 11:00-13:59, Cory 111 EECS 251LB-103. Introduction to Digital Design and Integrated Circuits Lab , Mo 17:00-19:59, Cory 111 EECS 251LB-104. Introduction to Digital Design and Integrated Circuits Lab , Mo 08:00-10:59, Cory 111

Max Willsey

Assistant Professor 725 Soda Hall; [email protected] Research Interests: Programming Systems (PS) Education: 2021, PhD, Computer Science, University of Washington Teaching Schedule (Spring 2024): CS 294-260. Declarative Program Analysis and Optimization , MoWe 14:30-15:59, Soda 405 Teaching Schedule (Fall 2024): CS 265. Compiler Optimization and Code Generation , TuTh 14:00-15:29, Soda 405

John Wright

Assistant Professor [email protected] Research Interests: Theory (THY) Education: 2016, Ph.D., Computer Science, Carnegie Mellon University Teaching Schedule (Spring 2024): CS 294-242. Quantum Coding Theory , MoFr 10:00-11:29, Hearst Mining 410 Teaching Schedule (Fall 2024): CS 294-261. Learning Problems in Quantum Computing , MoWe 10:30-11:59, Soda 405

Below The Line Assistant Professor [email protected] Research Interests: Artificial Intelligence (AI) Education: 2022, PhD, Computer Science, MIT

Assistant Teaching Professor 783 Soda Hall; [email protected] Research Interests: Education (EDUC) Education: 2019, PhD, Electrical Engineering, Stanford University; 2015, MS, Electrical Engineering, Stanford University; 2013, BS, Electrical Engineering and Computer Science, University of California, Berkeley Office Hours: (CS61C) M 2-3pm, 783 Soda; (Tea Hours, Data 375) Th 1-2:30pm, 783 Soda Teaching Schedule (Spring 2024): CS 47C. Completion of Work in Computer Science 61C CS 61C. Great Ideas of Computer Architecture (Machine Structures) , MoWeFr 10:00-10:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 195. Social Implications of Computer Technology , Tu 15:30-16:59, Physics Building 1 CS H195. Honors Social Implications of Computer Technology , Tu 15:30-16:59, Physics Building 1

Katherine A. Yelick

Professor 50A Lawrence Berkeley National Laboratory, 510-495-2431; [email protected] Research Interests: Programming Systems (PS) ; Scientific Computing (SCI) ; Biosystems & Computational Biology (BIO) Education: 1991, Ph.D., EECS, MIT; 1985, S.M., EECS, MIT; 1982, B.S., EECS, MIT Assistants: Tammy Johnson, 565 Soda, 643-4816, [email protected]

Justin Yokota

Lecturer [email protected] Education: 2022, M.S., Computer Science, UC Berkeley; 2021, B.A., Computer Science, Mathematic, UC Berkeley Teaching Schedule (Spring 2024): CS 47B. Completion of Work in Computer Science 61B CS 47C. Completion of Work in Computer Science 61C CS 61B. Data Structures , MoWeFr 13:00-13:59, Dwinelle 155 CS 61C. Great Ideas of Computer Architecture (Machine Structures) , MoWeFr 10:00-10:59, Dwinelle 155 Teaching Schedule (Fall 2024): CS 61B. Data Structures , MoWeFr 14:00-14:59, Wheeler 150

Adjunct Associate Professor 629 Soda Hall; [email protected] Research Interests: Biosystems & Computational Biology (BIO)

Professor 367 Evans Hall, 510-642-2021; [email protected] Research Interests: Signal Processing (SP) Education: 1990, Ph.D., Statistics, University of California, Berkeley; 1987, M.A., Statistics, University of California, Berkeley; 1984, B.S., Mathematics, Peking University

Adjunct Professor [email protected] Research Interests: Artificial Intelligence (AI) ; Control, Intelligent Systems, and Robotics (CIR) ; Graphics (GR) ; Signal Processing (SP) Education: 2003, Ph.D., Robotics, Carnegie Mellon University

Matei Zaharia

Associate Professor [email protected] Research Interests: Operating Systems & Networking (OSNT) ; Artificial Intelligence (AI) ; Database Management Systems (DBMS) Education: 2013, PhD, Computer Science, UC Berkeley Teaching Schedule (Fall 2024): CS 294-162. Machine Learning Systems , MoWe 14:00-15:29, Soda 310

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