computer science phd fields

How to Choose a Computer Science Specialization

Industry Advice Computing and IT

Jobs in computer science are in high demand. Of the 50 positions on Glassdoor’s list of the best jobs in the United States—which is based on earning potential, job satisfaction, and the number of job openings in 2021—17 of the positions listed are in the computer science and technology field.

According to the Bureau of Labor Statistics, the average salary for these roles exceeds $126,000 per year. Even more promising is that job growth for computer and information science roles is expected to increase by 22 percent by 2030—more than three times the national average for growth across all jobs. The average unemployment rate for many STEM roles is also well below the national average, according to U.S. News & World Report.

Computer Science Degrees and Specializations

Due to the high demand and required skills for jobs in this field, many employers seek candidates with advanced computer science degrees to fill these high-paying roles . In fact, Burning Glass Labor Insight data shows that nearly four in 10 computer science job listings request candidates that have master’s degrees.

Below we offer a look at some of the most common specializations within this field, the career options that best align with these specializations, and the annual earning potential for individuals within these concentrations.

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11 Common Computer Science Specializations

1. artificial intelligence.

Description: Artificial intelligence (AI) refers to a computing system’s ability to solve problems, make predictions, or complete complex tasks. AI applications use emerging technology such as natural language processing, which interprets written and spoken words, and machine learning, which enables applications to make predictions and recommendations.

Skills: Mathematics and analysis, algorithms, predictive modeling

Common Roles: Artificial Intelligence Architect, Artificial Intelligence Researcher, Machine Learning Engineer

Average Annual Salary for AI Skills: $125,000

Learn More: Top 5 Careers in Artificial Intelligence

2. Computer-Human Interface

Description: This specialization considers the many ways that people interact with computers, from websites and mobile phones to voice-enabled speakers and virtual reality. Effective interface development and deployment requires the use of standard libraries to ensure the compatibility and usability of applications across systems.

Skills: Communication, interpersonal skills, attention to visual detail, mapping how people use software and systems

Common Roles: User Experience (UX) Designer, User Experience Researcher, Interaction Designer

Average Annual Salary for HCI jobs: $118,942

Learn More: What is Human-Computer Interaction?

3. Game Design

Description: In addition to the realistic images that make today’s computer games so appealing, the computer science specialization of game design looks at the AI and machine learning that determines how players progress through a game. Game design also considers how the work of front-end designers and back-end developers should come together for a cohesive product experience.

Skills: Attention to visual detail, collaboration, coding, and scripting

Common Roles: Video Game Designer, Video Game Developer, Software Engineer

Average Annual Salary for Game Design Skills: $115,846

Learn More: Tips for a Video Game Design Career

4. Networks

Description: This specialization focuses on how organizations use both wired and wireless networks to exchange information with internal and external stakeholders. Responsibilities include managing bandwidth, traffic, user access, and the security of networks themselves, as well as any devices connected to the network.

Skills: Diagnose and troubleshoot network issues, design network architecture

Common Roles: Network Administrator, Network Analyst, Network Architect

Average Annual Salaries for Network Management Skills: Ranges from $59,865 (Network Analyst) to $121,412 (Network Architect)

5. Computer Graphics

Description: This specialization focuses on two- and three-dimensional images used in a variety of software applications, including games, computer-assisted design, manufacturing, and multimedia publishing. Beyond the concepts of creating realistic images, effective computer graphic design also considers the best way to display those images given limitations such as screen size, system memory, and bandwidth. Job options include in-house, agency, and freelance roles.

Skills: Attention to visual and artistic detail, collaboration, creativity

Common Roles: Mobile Application Developer, iOS Developer, Android Developer

Average Annual Salaries for Computer Graphics Skills: $116,192

6. Information Security

Description: Information security professionals manage all aspects of an organization’s security, including software applications, networks, storage hardware, devices, and so on. This computer science specialization requires a deep understanding of security vulnerabilities and the various methods that internal and external attackers use to exploit them. These roles must also balance security requirements with the need for employee or end-user productivity.

Skills: Communication, threat/vulnerability management, knowledge of security compliance rules and regulations

Common Roles: Security Engineer, Network Security Analyst, System Security Analyst

Average Annual Salary for Information Security Skills: $124,506

7. Data Science

Description: Data science refers to the ability to “mine” large data sets to gain useful information or insight. Organizations benefit most from data science as a practice when a variety of techniques are used to retrieve and analyze data, and when it is used to process large, complex, and sometimes unstructured sets of information, commonly referred to as “big data.”

Skills: Mathematics and analytics, attention to detail, predictive modeling

Common Roles: Data Scientist , Data Analyst , Business Intelligence Analyst , Machine Learning Engineer, Information Scientist, Database Administrator

Average Annual Salaries for Data Science Skills: Ranges from $123,419  (Data Scientist) to $145,549 (Machine Learning Engineer)

Learn More: What Does a Data Scientist Do?

8. Programming Languages

Description: Professionals who specialize in programming languages understand the key differences between common languages such as JavaScript, Python, Ruby, Visual Basic .NET, SQL, R, and C#. This includes knowledge about the types of applications, databases, or other use cases for which each language is best suited.

Skills: Coding and scripting in multiple languages, collaboration

Common Roles: Full-Stack Web Developer, Front-End Developer, Game Developer, Software Programmer

Average Annual Salary for Programming Skills: $105, 240

Learn More: Top 10 Programming Languages to Learn

9. Software Engineering

Description: In addition to application development, the computer science specialization of software engineering focuses on the systems and protocols for using these applications. Professionals in these roles may have a number of different specialties, such as debugging and testing, security and scalability, or the ability of an application to add users or features without a negative impact on performance.

Skills: Coding and scripting, communication, collaboration

Common Roles: Software Development Engineer, Software Engineer

Average Annual Salary for Software Engineering Skills: $119,923

Learn More:   The 11 Top-Paying Computer Science Jobs

10. Systems

Description: This computer science specialization helps an organization make the most of the hardware, software, and services that employees use every day. These products can include home-grown systems as well as a wide range of third-party products. Key concerns in this role include performance, security, and productivity of both the systems themselves as well as the employees working with them.

Skills: Diagnosing and troubleshooting hardware and software issues, patching and updating systems, designing system architecture

Common Roles: Systems Engineer

Average Annual Salary for Systems Management Skills: $122,180

Description: This specialization focuses on advanced mathematical theories and principles that apply to computer science. These theories can include advanced cryptography, approximation algorithms, computational algebra, and randomness. Other theories examine techniques for data and system processing such as distributed computing and parallel computing.

Skills: Mathematics and analytics, predictive modeling and probability, big-picture planning

Common roles: Algorithm Scientist, Machine Learning Engineer

Average Annual Salary for Computer Theory Skills: $102,754

Choosing the Right Computer Science Specialization

A rewarding and well-paying job in computer science is attainable for individuals with an interest in any of the above computer science concentrations. However, mid-career professionals may be hesitant to transition into the field due to common misconceptions about computer science , including that it requires exceptional math skills, or that it means sitting in front of a computer screen all day and night.

It is true that some of the skills needed for a career in computer science are technical. The application development company, BHW Group , notes that reading code, evaluating frameworks, using debuggers, and practicing source control are important skills for anyone directly involved in the process of building software, for example.

However, many computer science jobs require skills beyond writing and evaluating code. According to O*Net Online , these roles also require creativity, critical thinking, problem-solving, project or proposal evaluation, and communication with software end-users, management, or leadership staff. Individuals in these positions must also be comfortable working with teams of various sizes, as much of their work is done in groups.

Ultimately, choosing the right computer science specialization comes down to your personal interests and professional goals. Consider some of the if/then scenarios below to see if your specific preferences align well with a particular career path:

• If you have an eye for visual design and keen attention to detail, then consider a role as a programmer, game designer, computer graphics designer, or UX designer. These roles also require creativity and flexibility for meeting the requirements of internal and external stakeholders.
• If you have a background in technical training and back-end operations, consider a role as an engineer, system administrator, or network administrator.
• If you have technical expertise as well as an ability to negotiate diplomatically, consider a role in information security. Another possible role is a DevOps—shorthand for “developer operations”—engineer, a role that helps organizations bridge the gap between speed and stability in the development lifecycle.
• If you like to analyze data, detect patterns and gain insights that lead to more informed business decisions, consider a career in artificial intelligence, data science, data analysis, or computer theory. Note that these roles also require presenting the outcome of data analysis to key stakeholders in clear language.

Due to the array of specializations within the computer science industry, it’s important to find a career path that aligns with your abilities, technical background, and long-term goals.

Computer Science at Northeastern

Revised in 2021, the Master of Science in Computer Science curriculum at Northeastern is specifically designed to help you identify the computer science area that best matches your career interests, and to continue to develop the technical skills necessary to secure a fulfilling job within that specialization. In addition to the core curriculum, MSCS students take classes from three “breadth areas”—modeled after our PhD program—that enable students to gain a wider range of specialized skills, thus preparing them to work in many roles. Students must take three courses from at least two breadth areas during their studies. 

These breadth areas include: 

• Systems and Software: This breadth area builds on the use of software to solve practical problems. Students gain broad experiences and knowledge in software engineering processes, system-level programming, programming languages, and compilers. You can choose where you want to build your skills, like writing a small compiler, or designing a web experiment that illustrates web technologies.
• Theory and Security: This breadth area allows students to gain a strong foundation in the theory of computation and systems security issues. By taking courses focused on security vulnerabilities in software, privacy, and cryptography, students will understand the pervasiveness of security in computer science.
• Artificial Intelligence and Data Science: This breadth area introduces students to the fundamental problems, theories, and algorithms of artificial intelligence, while presenting techniques in machine learning and data mining. Students can take courses focused on the collection of data, gathering information from data for a variety of applications including games and natural language processing.

With your focused master’s degree, you’ll be prepared to chart a bold future in computer science. Learn more about Northeastern’s Master’s in Computer Science program and explore with path is right for you.

Editor’s note: This article was originally published in June 2019. It has since been updated for recency and accuracy.

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Computer science professionals see an average $30,000 salary increase after earning a master's degree. (Georgetown Center on Education)

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Computer Science & Engineering

Doctoral Programs in Computer Science and Engineering

Updated January 2023

PhD Program Overview

The following requirements are for students who entered the program starting Fall 2018 or later. If you entered Doctoral Program  prior to Fall 2018  see our  Former Curriculum Requirements .

CSE offers Doctor of Philosophy degrees in Computer Science and in Computer Engineering, providing a research-oriented education in preparation for a research, industrial, or entrepreneurial career. These programs explore both the fundamental aspects and application of computation, spanning theory, software, hardware, and applications.

The 37-unit coursework requirement is intended to ensure that students are exposed to (1) fundamental concepts and tools, (2) advanced, up-to-date views in topics outside their area (the breadth requirement), and (3) a deep, up-to-date view of their research area (the elective requirement). Doctoral students are expected to complete the breadth and elective requirements within the first three years of the program. All required coursework must be taken for a letter grade, with the exception of CSE 292 (Faculty Research Seminar), which is only offered S/U.

To access the CSE PhD Program Course Planner worksheet:   click here

Units obtained from a single course cannot count towards both the breadth and the elective requirements; they may only be applied towards one or the other. Doctoral students who have taken similar courses elsewhere may petition for a waiver of the required courses or for substitution by alternative courses.

The breadth requirement ensures that doctoral students share knowledge of fundamental concepts and tools from across broad areas of computer science and computer engineering. Each doctoral student must take each of these courses for a letter grade and maintain an overall breadth course GPA of 3.3 (except for CSE 292, for which a letter grade is not assigned). A student will typically complete all breadth courses within the first two years of graduate study.  Breadth courses are categorized into ten areas and are listed here alphabetically:

• Artificial Intelligence 
• Bioinformatics
• Computer Engineering
• Computer Systems & Security
• Database Systems
• Graphics & Vision
• Human-computer Interaction 
• Programming Languages, Compilers, and Software Engineering
• Theoretical Computer Science

To fulfill the breadth requirement, students will select four out of the ten areas and take a single course from each of these four areas.

For courses approved to fulfill the breadth requirement, please see the CSE Graduate Course Structure for PhD Students.

Additionally, students are required to take CSE 292, a 1-unit Faculty Researcher Seminar, where CSE faculty present one-hour seminars of their current research work in their areas of interest.  This course is only taught in Fall quarters and offered for S/U grade only.

The elective requirement ensures that doctoral students acquire some depth of knowledge in a general research area early in their career, but it also does not preclude them from pursuing a breadth of topics, if it serves their research interests. The elective requirement is designed to be flexible and nimble enough to respond to the rapidly and constantly evolving dynamic disciplines of computer science and computer engineering. 

The elective requirement is also designed with heavy faculty mentorship in mind.  Students will consult with their faculty advisors to develop an academic plan that will include four courses from the aforementioned four separate breadth areas and five elective courses that may be selected from an approved set of courses featured in the  CSE Graduate Course Structure for PhD Students.

Units obtained in the CSE 209 series, 229 series, 239 series, 249 series, 259 series, 269 series, 279 series, 289 series, 219, 290, 292, 293, 294, 298, 299, 500, and 599 do not count toward the elective requirement.

The research exam in the first milestone in the Ph.D. program.  It has three goals:

• Depth . The research exam verifies the student's ability to identify challenges and open problems in a focused area.  The exam should teach students how to navigate, acquire depth of knowledge, and perform critical analysis in a given research area; the exam should verify such abilities.
• Communication . The research exam will verify the student's ability to communicate past and proposed research, orally and in writing.
• Provide diverse feedback . The research exam provides the student with feedback on their research synthesis, analysis, and communication skills from CSE faculty beyond their advisor and outside their immediate research area.

As part of the exam, the student prepares and makes a presentation to their research exam committee.  The presentation can present results of their research and must also place that work in the context of related work in the field.

The exam committee comprises three faculty members (not including the student’s advisor), and the committee evaluates the student based on the goals above.

Student should complete the exam before the end of their second year of study.

Teaching is an important part of a doctoral student’s training. All students enrolled in the doctoral program must have one quarter of training as a teaching assistant. This is a formal degree requirement and must be completed before the student is permitted to graduate. The requirement is met by serving as a 50 percent teaching assistant and taking CSE 500 (Teaching Assistantship). CSE 599 (Teaching Methods in Computer Science) examines theoretical and practical communication and teaching techniques particularly appropriate to computer science, and students usually take it prior to or concurrent with the teaching assistantship.

The qualifying examination is a requirement for advancement to candidacy. Prior to taking the qualifying examination, a student must have satisfied the departmental course and research exam requirements and must have been accepted by a CSE faculty member as a doctoral thesis candidate. All doctoral students are expected to advance to candidacy by the end of their third year, and advancement is mandatory by the end of the fourth year. The examination is administered by a doctoral committee appointed by the dean of the Graduate Division and consists of faculty from CSE and other departments. More information on the composition of the committee can be obtained from the CSE graduate office. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work.

The dissertation defense is the final doctoral examination. A candidate for the doctoral degree is expected to write a dissertation and defend it in an oral examination conducted by the doctoral committee. 

Students must be advanced to candidacy by the end of four years. Total university support cannot exceed seven years. Total registered time at UC San Diego cannot exceed eight years.

PhD students may obtain an MS Degree along the way or a terminal MS degree by completing the PhD coursework requirements (see details in the section “Doctoral Degree Program”); AND completing four units of CSE 299/298/293 OR an additional 4-unit, letter-graded, approved course from the CSE Graduate Course Structure; AND passing the PhD Research Exam.  Please note that completion of CSE 292 is not required for PhD students to earn the MS along the way or a terminal MS.

Financial support is available to qualified graduate students in the form of fellowships, loans, and assistantships. For questions about financial support, please see our website: http://cse.ucsd.edu/graduate/financial-opportunities .

PhD in Computer Science

The PhD in Computer Science program provides students with the advanced coursework and groundbreaking research opportunities they need to contribute at the forefront of the world’s fastest-growing fields. Forging knowledge in 15 core areas like artificial intelligence, data science, programming languages, and human-centered computing, you’ll gain significant expertise in conducting and presenting the results of your research. Ultimately, you’ll produce and defend original work that contributes to critical discourse in your chosen area.

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Khoury College doctorate students gain deep knowledge and invaluable experience—preparing you for a research career in academia or industry.

Khoury Computer Science PhD graduates have found prestigious positions across industry and academia.

Tenure-track faculty:

• University of Michigan, Ann Arbor
• University of British Columbia (UBC)
• Indiana University
• University of Maryland
• University College London
• NC State University
• UMass Boston
• City University of Hong Kong

Postdoc research scientists:

• University of Paris
• Virginia Tech
• Microsoft Research
• GE Global Research

Senior software engineers and industry leaders:

Students graduating with a PhD in Computer Science will:

• Gain a broad understanding of computer science fundamentals, spanning a substantial portion of the following core areas: artificial intelligence and data science, human-centered computing, software, systems, and theory
• Gain significant expertise in at least one research area in computer science
• Produce and defend original research in an area of computer science
• Be able to communicate research results effectively in both oral and written forms

Our flagship campus in Boston is just minutes away from esteemed universities, exciting start-ups, and leaders in tech, finance, health care, and more.

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PhD Admissions

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The Computer Science Department PhD program is a top-ranked research-oriented program, typically completed in 5-6 years. There are very few course requirements and the emphasis is on preparation for a career in Computer Science research. 

Eligibility

To be eligible for admission in a Stanford graduate program, applicants must meet:

• Applicants from institutions outside of the United States must hold the equivalent of a United States Bachelor's degree from a college or University of recognized good standing. See detailed information by region on  Stanford Graduate Admissions website. 
• Area of undergraduate study . While we do not require a specific undergraduate coursework, it is important that applicants have strong quantitative and analytical skills; a Bachelor's degree in Computer Science is not required.

Any questions about the admissions eligibility should be directed to  [email protected] .

Application Checklist

An completed online application must be submitted by the CS Department application deadline and can be found  here .

Application Deadlines

The online application can be found here  and we will only one admissions cycle for the PhD program per respective academic term.

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Academics   /   Graduate PhD in Computer Science

The doctor of philosophy in computer science program at Northwestern University primarily prepares students to become expert independent researchers. PhD students conduct original transformational research in extant and emerging computer science topics. Students work alongside top researchers to advance the core CS fields from Theory to AI and Systems and Networking . In addition, PhD students have the opportunity to collaborate with CS+X faculty who are jointly appointed between CS and disciplines including business, law, economics, journalism, and medicine.

Joining a Track

Doctor of philosophy in computer science students follow the course requirements, qualifying exam structure, and thesis process specific to one of five tracks :

• Artificial Intelligence and Machine Learning
• Computer Engineering

Within each track, students explore many areas of interest, including programming languages , security and privacy and human-computer interaction .

Learn more about computer science research areas

Curriculum and Requirements

The focus of the CS PhD program is learning how to do research by doing research, and students are expected to spend at least 50% of their time on research. Students complete ten graduate curriculum requirements (including COMP_SCI 496: Introduction to Graduate Studies in Computer Science ), and additional course selection is tailored based on individual experience, research track, and interests. Students must also successfully complete a qualifying exam to be admitted to candidacy.

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Download a PDF program guide about your program of interest and get in contact with our graduate admissions staff.

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Opportunities for PhD Students

Cognitive science certificate.

Computer science PhD students may earn a specialization in cognitive science by taking six cognitive science courses. In addition to broadening a student’s area of study and improving their resume, students attend cognitive science events and lectures, they can receive conference travel support, and they are exposed to cross-disciplinary exchanges.

The Crown Family Graduate Internship Program

PhD candidates may elect to participate in the Crown Family Graduate Internship Program. This opportunity allows the doctoral candidate to gain practical experience in industry or in national research laboratories in areas closely related to their research.

Management for Scientists and Engineers Certificate Program

The certificate program — jointly offered by The Graduate School and Kellogg School of Management — provides post-candidacy doctoral students with a basic understanding of strategy, finance, risk and uncertainty, marketing, accounting and leadership. Students are introduced to business concepts and specific frameworks for effective management relevant to both for-profit and nonprofit sectors.

Career Paths

Recent graduates of the computer science PhD program are pursuing careers in industry & research labs, academia, and startups.

• Georgia Institute of Technology
• Illinois Institute of Technology
• Northeastern
• University of Pittsburgh
• University of Rochester
• University of Washington
• Naval Research Laboratory
• Northwestern University

Industry & Research Labs

• Adobe Research
• Narrative Science
• Oak Ridge National Laboratory

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What Students Are Saying

"One great benefit of Northwestern is the collaborative effort of the CS department that enabled me to work on projects involving multiple faculty, each with their own diverse set of expertise.

Northwestern maintains a great balance: you will work on leading research at a top-tier institution, and you won't get lost in the mix."

— Brian Suchy, PhD Candidate, Computer Systems

What Alumni Are Saying

"In the early stage of my PhD program, I took several courses from the Department of Economics and the Kellogg School of Management and, later, I started collaborating with researchers in those areas. The experience taught me how to have an open mind to embrace and work with people with different backgrounds."

— Yiding Feng (PhD '21), postdoctoral researcher, Microsoft Research Lab – New England

Read an alumni profile of Yiding Feng

"My work at IBM Research involves bringing together symbolic and deep learning techniques to solve problems in interpretable, effective ways, which means I must draw upon the research I did at Northwestern quite frequently."

— Maxwell Crouse (PhD '21), AI Research Scientist, IBM Research

Read an alumni profile of Maxwell Crouse

The theory group here is very warm and close-knit. Starting a PhD is daunting, and it is comforting to have a community I can lean on.

— Vaidehi Srinivas, PhD Candidate, CS Theory

Computer Science, PhD

• Program description
• At a glance
• Degree requirements
• Admission requirements
• Tuition information
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Algorithms, Artificial Intelligence, Big Data, Computer Science, Cybersecurity, Technology, approved for STEM-OPT extension, computing, database, enggradcs, systems

Take the next step in your journey to become an effective leader, innovator, entrepreneur or educator in your community and the world.

The PhD program in computer science prepares students to undertake fundamental and applied research in computer science. The program is available for those of high ability who seek to develop and implement their own research studies.

Students pursuing the doctorate in computer science learn to analyze, understand and apply key theories and algorithms used in the field and to generate and evaluate new theories, algorithms and software modules that can advance the field of computer science.

The program provides students with research opportunities in a wide variety of areas, including:

• artificial intelligence, machine learning and statistical modeling
• big data and data mining
• computational biology
• computer design and architecture, including nonvolatile memory computing
• computer system security, cybersecurity and cryptography
• cyber-physical systems and Internet of Things (commonly abbreviated as IoT), and robotics
• distributed computing and consensus protocols
• networking and computer systems
• novel computing paradigms (e.g., biocomputing, quantum computation)
• social computing
• theory, algorithms and optimization
• visualization and graphics

This program may be eligible for an Optional Practical Training extension for up to 24 months. This OPT work authorization term may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.

The OPT extension only applies to students on an F-1 visa and does not apply to students completing a degree through ASU Online.

• College/school: Ira A. Fulton Schools of Engineering
• Location: Tempe

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core Areas (9 credit hours) foundations (3) systems (3) applications (3)

Depth (3 credit hours) three additional credit hours in one core area (3)

Research (18 credit hours) CSE 792 Research (18)

Electives and Additional Research (42 credit hours)

Culminating Experience (12 credit hours) CSE 799 Dissertation (12)

Additional Curriculum Information Courses that are used to satisfy the core area requirement cannot be used to satisfy electives or other requirements. A grade of "B" or better is required for core courses.

Eighteen credit hours of CSE 792 Research are required, and up to 54 credit hours are allowed on the plan of study. Students with research credit hours in excess of 18 add these credit hours to their electives and additional research.

Electives include:

• additional CSE 792 Research credit hours (up to 36 credit hours allowed beyond the required 18)
• computer science courses, of which up to 18 credit hours of CSE 590 and CSE 790: Reading and Conference are allowed
• up to six credit hours of interdisciplinary electives in other academic units that are subject to program chair approval

When approved by the academic unit and the Graduate College, this program allows 30 credit hours from a previously awarded master's degree to be used for this degree.

A maximum of three credit hours of 400-level coursework may be applied to the plan of study.

Applicants must fulfill the requirements of both the Graduate College and the Ira A. Fulton Schools of Engineering.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in computer science, computer engineering or a closely related area. Most applicants should have earned a master's degree, but exceptional undergraduate applicants may be admitted directly into the doctoral program.

Applicants must have a minimum cumulative GPA of 3.50 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program, or they must have a minimum cumulative GPA of 3.50 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

• graduate admission application and application fee
• official transcripts from every university attended
• three letters of recommendation
• a statement of purpose
• curriculum vitae or resume
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

Submission of GRE scores is optional.

Students assigned any deficiency coursework upon admission must complete those classes with a grade of "C" or higher (scale is 4.00 = "A") within two semesters of admission to the program. Deficiency courses commonly taken include:

CSE 230 Computer Organization and Assembly Language Programming CSE 310 Data Structures and Algorithms CSE 330 Operating Systems CSE 340 Principles of Programming Languages or CSE 355 Introduction to Theoretical Computer Science

The applicant's undergraduate GPA and depth of preparation in computer science and engineering are the primary factors affecting admission.

Graduates are prepared to pursue careers in research and education, including academia, government and industry.

Career examples include:

• computer science professor or researcher
• data scientist or engineer
• machine learning, AI or computer vision scientist or engineer

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Top 10 Best PhD in Computer Science Programs

Are you looking to further your studies in computer science? Perfect! I’ve researched and put together these ten computer science PhD programs, so keep reading to find your next course!

As you know, PhD in computer science holders are sought-after specialists, with current demand far outstripping supply. According to the Bureau for Labor Statistics , jobs for computer and information research scientists are estimated to grow by an impressive 22% between 2020 and 2030. You might score similar positions with a PhD in mathematics , too. But a PhD in computer science can open doors to some of the most lucrative jobs in the industry.

Let’s explore some of the best computer science PhD courses in the US!

Table of Contents

Best Computer Science PhD Programs and Universities

Stanford university.

PhD in Computer Science

Stanford University is one of the most famous research institutions in the world, and its Computer Science programs have been ranked second  in the USA. This PhD program involves a research rotation scheme where students participate in different research groups during the first year. This exposes them to different subjects and lets them know the faculty and fellow students.

• Courses : Analysis of algorithms, programming languages, and computer network & security.
• Credits : 135 course units
• Duration : 5-6 years
• Delivery : On-campus
• Tuition : Refer tuition page  (full funding available)
• Financial aid : Research assistantships, teaching assistantships, fellowships, and grants
• Acceptance rate:  5.2%
• Location : Stanford, California

Massachusetts Institute of Technology

PhD in Computer Science and Engineering

MIT is known as the best technical institution in the world, and its computer science programs were ranked first  in the country by the US & News report. Students can work with all schools and departments throughout their studies.

• Courses : Software & computation for simulation, process data analytics & machine learning, and numerical computing & interactive software.
• Credits : 60
• Tuition : Refer tuition page
• Financial aid:  Scholarships, federal work-study, fellowships, assistantships, grants, and veteran benefits
• Acceptance rate:  7.3%
• Location : Cambridge, Massachusetts

Carnegie Mellon University, School of Computer Sciences

CMU is a globally acclaimed private research university, home to conducting cutting-edge technology research across its seven prestigious schools. This is one of the few PhD programs in computer science with an optional dual degree arrangement enabling you to study programs with one of seven Portuguese universities.

• Courses : Algorithms & complexity, artificial intelligence, and software systems.
• Credits : 96 university units
• Tuition : $48,250 per year
• Financial aid:  Full funding, fellowships, and scholarships
• Acceptance rate : 17.3%
• Location : Pittsburgh, Pennsylvania

Harvard University, Harvard John A. Paulson School of Engineering and Applied Sciences

Harvard University is a world-renowned research institution that aims to achieve the perfect mix of scholarship and innovation. Across the university, every PhD student is given a field advisor right from the beginning of the program. Then, the student identifies a research area and a potential research advisor in their first two semesters. Upon qualifying, the research advisor nominates a research committee to assist the student throughout their graduate career.

• Courses : Algorithms & their limitations, data structures & algorithms, and cryptography.
• Credits : 16 half-courses
• Duration : 2 years minimum
• Financial aid : Full funding, fellowships, teaching assistantships, and research assistantships
• Acceptance rate:  5%

Duke University, The Graduate School

Duke University is an internationally acclaimed private research university known for its inclusivity. Its Center for Exemplary Mentoring aims to increase the number of PhD graduates from underrepresented and minority communities. Its PhD of Computer Science program is flexible and allows students to choose between a coursework-only option of 30 credits or a thesis.

• Courses : Programming & problem solving, computational microeconomics, and software design & implementation.
• Credits : 8 courses
• Duration : 5 years
• Tuition : $4,325  per semester
• Financial aid : Grants, fellowships, teaching assistantships, and research assistantships
• Acceptance rate : 7.7%
• Location : Durham, North Carolina

The University of California Berkeley, Department of Electrical Engineering and Computer Sciences (EECS)

The University of California Berkeley is a prestigious university committed to student diversity and has a dedicated Office for Graduate Diversity to support students from all backgrounds. This PhD program offers research opportunities in biosystems and computational biology, cyber-physical systems and design automation (CPSDA), and computer architecture and engineering.

• Courses : Combinatorial algorithms & data structures, design of programming languages, and implementation of database systems.
• Credits : 24 units minimum
• Duration : 5.5 – 6 years
• Tuition : $6,132 per semester
• Financial aid : Fellowships, scholarships, grants, research stipends, loans, and work-study
• Acceptance rate : 17%
• Location : Berkeley, California

California Institute of Technology, Computing & Mathematical Sciences Department

The California Institute of Technology, also known as Caltech, is one of the most renowned technology institutions in the world despite its comparatively small size. This PhD program allows students to develop an in-depth understanding of and conduct research in areas related to mathematical and algorithmic foundations of computer science.

• Courses : Quantum cryptography, information theory, and network control systems.
• Credits : 135 units
• Duration : 6 years
• Tuition : $63,063  per year
• Financial aid : Fellowships, assistantships, loans, stipends, scholarships, and work-study
• Acceptance rate : 6.7%
• Location : Pasadena, California

Cornell University, Bowers College of Computing and Information Science

Cornell University is home to several famous technology schools, such as Cornell Tech, which conducts cutting-edge research to find solutions to the challenges of our modern, digital world. In this PhD program, students choose a minor from one of the 90 available fields outside of computer science to widen their knowledge base.

• Courses : Parallel computing, programming environments, and natural language processing.
• Duration : 12 semesters
• Tuition : $24,800  per year
• Financial aid:  Assistantships, fellowships, loans, and stipends
• Acceptance rate : 10.7%
• Location : Ithaca, New York

The University of Illinois Urbana-Champaign, The Grainger College of Engineering

The Grainger College of Engineering focuses on research to improve quality of life through innovation, entrepreneurship, and societal engagement. In this flexible PhD program, students are assigned three committee members during the first semester. The student can then consult with these mentors to plan their studies to meet their career goals.

• Courses : Programming language semantics, machine learning for signals, and learning-based robotics.
• Credits : 96, or 64 if you already have an MS
• Duration : 5-7 years
• Financial aid : Grants, fellowships, waivers, loans, and employment
• Acceptance rate : 63.3%
• Location : Urbana, Illinois

Princeton University, The Graduate School

Princeton University is one of the top universities in the country in computer science doctorate programs. This PhD program involves studying six courses, including one each from the three main areas of Artificial Intelligence, Systems, and Theory, which form the program’s core.

• Courses : Programming languages, advanced computer systems, and information theory & applications.
• Credits : 6 courses
• Tuition : $62,860  per year
• Financial aid : Teaching assistantships, research assistantships, and fellowships
• Acceptance rate : 5.6%
• Location : Princeton, New Jersey

What Do I Need to Get a PhD in Computer Science?

For most programs, you’ll need a bachelor’s or master’s degree in computer science or a related field; however, exact eligibility requirements vary depending on the school. To earn your PhD, you’ll typically need to complete coursework, qualifying exams, and a dissertation.

What to Consider When Choosing a Computer Science PhD Program

Several US schools and universities offer PhD in computer science programs — choosing the right program for you can feel overwhelming. So, take your time and research the curriculum and specialties for different programs to make sure they match your areas of interest.

If you’re unsure about the areas you want to specialize in, I advise you to read research papers across different fields and discuss career opportunities with people in the industry. It’s also a good idea to look up the faculty from the programs you’re interested in and review their recent papers.

Here are some key factors to keep in mind when choosing a computer science PhD:

• Curriculum and specialties offered
• The program’s reputation
• Faculty, their specialties, and reputation
• Cost of tuition and other fees
• Delivery mode: on-campus, online, or hybrid
• Funding options

Related Reading:  Top 10 Best Online PhDs in Computer Science

Why Get a Doctor of Computer Science Degree?

A doctorate in computer science will equip you to become a leading researcher in today’s digital technologies. You’ll also be eligible for senior academic positions with a PhD in the field.

Furthermore, a PhD in computer science allows you to work in various respectable roles. Here are some of the top jobs and average yearly salaries for computer science PhD holders:

• Data Scientist – $99,710
• Chief Data Scientist –  $211,702
• Software Architect – $136,541
• Software Development Manager – $133,534
• Senior Researcher – $107,657

Approximately 2,000 students earn a computer science PhD from US universities each year. The industry demand is much greater than the supply. Furthermore, many PhD candidates are international students who return home after graduation. Therefore, graduates with a PhD in computer science are in high demand in the US.

PhD in Computer Science: Key Facts

How much does a phd in computer science cost.

Tuition varies depending on the program but generally falls between $15,000 and $60,000 per year. Besides the tuition fees, you will also need to factor in additional costs like academic fees, books, other educational resources, and living expenses.

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

A PhD in computer science usually takes 3-7 years to complete.

What Skills Do You Gain from Doing a PhD in Computer Science?

A PhD in computer science allows students to develop leadership, problem-solving, and research skills related to complex topics like artificial intelligence, machine learning, and robotics.

Key Takeaways

A PhD in computer science is one of the most in-demand qualifications in today’s hyper-digital world. It can equip you with specialized skills and knowledge to address modern tech problems with innovative solutions.

If you found this article helpful, take a look at our other guides, including the best Master’s in Computer Science programs , the top online PhD programs , and the highest paying PhDs .

Frequently Asked Questions

What is a phd in computer science like.

A PhD in computer science typically involves research, coursework, thesis preparation, teaching, and seminars related to computer science subjects. Individual programs may have other requirements.

Can You Get a PhD in Computer Science?

Yes, many universities offer computer science PhD programs . You’ll usually need a bachelor’s or a master’s degree in computer science or a related field to apply.

Is a PhD in Computer Science Worth It?

While a PhD in computer science requires considerable financial investment, it is a valuable qualification in today’s tech-forward world. Plenty of job opportunities and high remuneration levels await a computer science PhD graduate, with the average annual salary of $133,000 per year.

What Does a PhD in Computer Science Do?

A qualified PhD computer science graduate may conduct research in complex computer systems, design and develop programs and applications, or study human-computer interaction to find innovative solutions for society. They may also work in academics, either in teaching, research, or both.

What Can I Do with a PhD in Computer Science?

With a PhD in Computer Science on your resume, you’ll be hired for roles like data scientist, software architect, researcher, or academic professor and build a prestigious, high-paid career.

Which Subject is Best for a PhD in Computer Science?

A wide range of subjects will qualify you to apply for a doctoral degree in computer science. Popular topics include data structures and algorithms, computer systems and organization, and discrete computer science mathematics. The best subject for you depends on your interests and career ambitions.

Lisa Marlin

Lisa is a full-time writer specializing in career advice, further education, and personal development. She works from all over the world, and when not writing you'll find her hiking, practicing yoga, or enjoying a glass of Malbec.

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PhD in Computer Science: Specializations & Best Degrees

A PhD in computer science is a highly versatile degree, and many students choose it for that reason. However, with the wide range of Ph.D. specializations and programs available, it isn’t easy to select the best fits.

There are several popular specializations in the United States and other world, including engineering, information technology, security, and more.

It’s essential to get familiar with the types of courses and research methods used in that field. It can help you decide if it will be the right fit for you.

Therefore, I have compiled a brief of the best specializations and degrees for Ph.D. students in computer science. So, this blog includes their typical core necessary, subject, and descriptions.

Why Earn A Computer Science Ph.D.?

You’ve probably heard that you need an advanced degree in a difficult job market to get anywhere these days. But what kind of degree?

Computer science degrees are in high demand, and for a good reason. Some of today’s hottest employers constantly look for talented computer scientists and programmers. So, if you want to take your career to another level, consider earning a doctor of philosophy degree.

A PhD in computer science opens up many job opportunities, from research and teaching to data science and software development. Still, depending on your specific area of expertise and how your interests grow during your graduate studies.

You may find yourself more interested in some options than others. To make sure you have a comprehensive understanding of all your career options.

Career paths to consider

A Ph.D. in computer science can lead to a wide range of career opportunities . One option is becoming an academic and teaching and conducting research at a university.

Another option is to work in the private sector, in a role such as software engineering, data science, or cybersecurity. Finally, some people choose to pursue a career in the public sector, such as government IT or digital communications.

Above all, research is a common career choice for degree holders because of their training in analyzing complex problems and testing hypotheses through experimentation or data collection.

Area for Specialization

Machine learning.

Machine learning incorporates advanced math, logic, and algorithm to allow computers to process data and make predictions based on algorithms. A key focus is creating machines that can learn to adapt with experience.

For example, a computer can improve speech recognition or vision, as it’s exposed to more data about speech or human faces. The ability for computers to learn goes beyond simple pattern recognition. Instead, it’s about building systems that teach themselves from real-world inputs, commonly referred to as artificial intelligence (AI).

Hence, Ph.D. in machine learning focuses on artificial intelligence and computer science. The program trains researchers in theoretical and applied machine learning, with an eye toward the real-world applications of this technology.

Students learn how to design and build algorithms that can learn from data and perform tasks from image recognition to recommendation systems. That is designed to help students become leading practitioners, ready to tackle the challenges of innovative machine learning research, industry, and academia.

Neural Networks

You may have heard of artificial neural networks, or ANNs, which are used to mimic human cognition. ANNs use a computer system to learn from data. For example, let’s say you want your neural network to recognize all the images on Google’s homepage for you.

ANNs can be trained to recognize any data as long as they have enough information. They use algorithms designed to pick up patterns between what they’re looking at and how it’s been labeled. The Ph.D. in Neural Networks trains the next generation of researchers and scientists to advance the field of machine learning.

Bioinformatics

Bioinformatics is a branch of computer science and a tool used by researchers. Usually, students who enroll in PhDs that combine bioinformatics with computer science may learn to use algorithms to analyze DNA sequence data.

Other students may be interested in developing new software applications and hardware platforms that enable scientists to study various diseases and health conditions better. Either way, you’ll likely spend a lot of time conducting research, which is certainly different from many other types of doctoral programs out there.

Luckily, if you’re passionate about health care or programming, you can still pursue your dreams as a doctor of bioinformatics by earning your degree online with an accredited university.

Network and Security (NSP)

Network protocols and security ensure that humans and computers communicate securely over a network. It involves ensuring that only allowed users can access a network or system and keep sensitive information private.

The study of network protocols is concerned with the rules and languages computers use to communicate over a network. It is the job of network engineers to design and build network systems that can communicate with each other securely and efficiently.

Vision and Pattern Recognition

The Ph.D. in Computer Vision and Pattern Recognition goal is to equip students with the theoretical, methodological, and practical expertise to become technology leaders.

It develops a strong foundation in math, algorithms, and the scientific method while introducing students to the latest technology.

As a result, students think critically and quantitatively while providing training and mentorship to prepare students for postdoctoral and independent research careers. As a result of this specialization, students will be equipped for careers in academia, industry, or government research.

Also Read: High Paying Engineering Degrees

Game theory.

In computer science, game theory is applied to a variety of fields. A typical example is designing optimal strategies for competing companies. One famous example used game theory when creating optimal strategies for auctions, where companies can make certain bids or not bid at all.

The idea behind using game theory here is that each company will use its competitors’ past actions to predict what they’ll do next and thus base their actions on that information. The military also uses game theory when planning strategies for both defensive and offensive moves.

Computer Architecture

Today, there are four main computer architectures: Von Neumann, Harvard, Ternary, and Connection. Most computers in today’s market use either Von Neumann or Harvard architecture.

A Von Neumann architecture is used by desktop and laptop computers. This architecture uses a separate processor (CPU) and memory chips with a control unit to coordinate communication.

In contrast, a Harvard architecture places both processing power and memory on one chip—what’s known as an SoC (system-on-chip).

The field of computer architecture will continue to be a vital one as the world’s biggest tech companies compete to build the most extensive and most advanced computers.

Operating Systems

Operating systems are software that runs on computers and mobile devices . They serve as the interface between users and their computers, allowing people to access and use software and data. Operating systems are also responsible for managing and coordinating the resources and hardware of a computer.

Most computer science students learn to write programs on existing operating systems. But there are alternatives to becoming a software engineer. As long as you don’t plan on devoting your entire life to writing code, consider a career as an IT manager or analyst.

You will learn what it takes to install and maintain operating systems, which is valuable information for anyone in data security or network infrastructure.

Natural Language Processing

The field of Natural Language Processing explores how computers can understand language. NLP draws from computational linguistics, artificial intelligence, and computer science fields.

Consequently, students studying NLP focus on building algorithms. That can process written text or spoken words and carry out tasks. For example, understanding human emotion, summarizing data, searching for relevant information online, or translating one language into another.

From manufacturing to medicine, robotics can assist in a wide range of fields. However, the domain is also seeing a rise in degree programs, so it is helpful to specialize in a particular area. Ph.D. in robotics is among the most popular specializations for individuals interested in the field.

Robotics is poised to make significant advances in society by improving the quality of life for people around the globe. However, to realize this, a strong foundation in the fundamentals of robotics is required.

Specialization in the ACO

The field of algorithms, combinatorics, and optimization (ACO) is an extraordinary place in computer science. The lot offers a unique mixture of theory, practice, and applications in an astonishing number of areas.

Besides, students learn the efficiency and effectiveness of algorithms, the design of valuable objects, and the best way to achieve a goal. The Ph.D. in ACO focuses on the mathematical and computational foundations and their applications to a broad range of problems.

This specialization gives students a strong background in these fields and can conduct innovative research. The doctor of philosophy in ACO is a highly quantitative program and requires a significant amount of research.

PI & ADS Specialization

Photonic integration (PI) and advanced data storage technology (ADS) come together to facilitate high-speed electronics for a variety of applications, including data centers, supercomputers, 5G networks, autonomous vehicles, and more.

Advances in photonics and storage technologies have made it possible to combine these technologies into one device with an architecture called multi-layer stack (MLS).

Designing and manufacturing advanced electronics requires a complex process involving multiple subsystems, including circuit design, packaging, interconnects, and thermal management.

These speeds, efficiency, and size improvements enable new applications for supercomputers, data centers, autonomous vehicles, and 5G networks.

Computer Engineering

A computer engineer typically requires either a doctoral degree or a master’s degree in computer science, electrical engineering, software engineering, or another related area of study.

A doctorate is typical of working as an engineer and developing new technology, especially computers. Moreover, they perform research that has practical applications, although an advanced degree isn’t necessary for research-only positions.

A doctorate also may be helpful if you wish to teach computer science at the college level. The majority of colleges and universities prefer faculty members with advanced degrees.

Software Engineer

In reality, software engineers are among the highest-paid professionals. In addition, ph. D. work can prepare the students for senior-level careers at leading companies or startups.

So, you can choose to specialize in artificial intelligence, network security, or mobile apps. If you want something a little more practical, try applying your coding skills to biomedical applications like medical imaging and genomics analysis.

The major caveat with software engineering is that you’ll have to put up with many grunts of work. It also maintains computer networks and does other tedious systems work that can be hard to get excited about as a doctoral student.

Famous Universities

What are the requirements for a PhD in computer science?

To enter a PhD in the computer science program, have a bachelor’s or master’s degree or equivalent. Additionally, GRE scores and a letter of recommendation are required. Each university sets its requirements and the particular minimum score for admittance.

You must also have an undergraduate degree in mathematics or engineering, along with some familiarity with computers. Sometimes, work experience in computer science may be required.

Typically, students need to meet specific backgrounds, experiences, and other factors. For example, the requirements for the Doctor of Philosophy in computer science offered at a university in the United States will differ from a school in another country.

Similarly, the requirements of a large, private university will differ from a smaller, public university. In general, the requirements will vary based on the institution. Above all, some schools require IELTS or TOEFL scores from international students.

Online Doctor of Philosophy Programs

The best online Ph.D. programs in computer science offer the same curriculum and content as their on-campus counterparts. You can access the same lectures and materials, interact with the same faculty, and utilize the same resources.

In this case, you can study at home or any other location of your choice. This flexibility is especially beneficial for students with limited education or financial resources.

The first step toward earning an online PhD in computer science is to find the right online program for you. Above all, many schools are offering Doctor of Philosophy specializations in computer science.

The format of an online program allows you to access course materials and complete assignments whenever is most convenient for you. Some programs offer the option to take face-to-face classes on campus or through videoconferencing.

The online format also allows you to collaborate with classmates and communicate with professors and other experts in your field through online chat, email, and other forms.

How long does it take to complete a PhD in computer science?

The time to complete a Ph.D. in computer science depends on several factors, most notably the background and interests of the student. For students with experience in computer science, the program could complete in as little as two years.

For students without a background in the field, it could take substantially longer. So, most programs are completed in 4 or 5 years, though students are often allowed to take longer if necessary.

What is a tuition fee waiver for Ph.D. students in the United States?

Tuition waivers are offered to Ph.D. students in the United States to help offset the cost of education. Universities grant these waivers, often to attract top students to their programs.

Consequently, some Ph.D. programs are offered tuition-free to qualified students, while others require paying tuition per semester. It can also apply to the tuition fees for graduate programs other than a Ph.D. (such as a Master’s degree or a certificate program).

What is the annual tuition fee in the United States for a Ph.D. in computer science?

In the United States, students attending PhD programs in computer science pay roughly $24,000 per year in tuition and living expenses. Thus, the annual tuition fee for a Doctor of Philosophy in computer science depends on location.

What jobs are available for PhDs in computer science?

With so many career opportunities in computer science, it’s no surprise that graduates with a doctorate find themselves in high-demand jobs. Many computer science PhDs choose to become faculty members in colleges and universities.

Other graduates become software engineers or research and development managers in the computer industry. Similarly, some people become computer programmers or software engineers.

The largest employers of computer scientists are Google, Facebook, Amazon, Microsoft, IBM, and Apple, in that order. So, PhDs in computer science have plenty of opportunities in the field.

There are essentially two types of computer science doctorate degrees: those that focus on a particular industry area and those that focus on teaching. The first is generally better for students who want to enter the industry; the second is better for students who wish to enter academia.

Whether it’s worth getting a Ph.D. in computer science depends on your career goals. A Ph.D. could help you achieve your career goals. It can also open many doors to higher-paying positions. However, if a master’s degree is sufficient for your needs, consider a career in software engineering or another field of computer science rather than pursuing a doctoral degree.

Google has a well-deserved reputation for seeking the best and the brightest for hiring software engineers. But does that mean they only hire people with PhDs? Not at all. While advanced degrees can be a great way to strengthen your resume, education is only one of the many factors we consider when evaluating candidates. Some of Google’s most successful employees have PhDs. A significant number of our engineers have advanced degrees. But we also hire for other kinds of backgrounds, including those without advanced degrees.

Salaries for computer science PhDs also vary by employer and position, but on average, computer science PhDs earn $108,000 per year. As a result, computer science PhDs have plenty of options for continuing their education and finding high-paying jobs. Computer science Doctor of Philosophy job prospects vary by field and employer, of course, but you can expect solid job prospects in academia, government, and industry.

We often think of programmers and software developers when we think of computer science. But a PhD in computer science has a lot more to offer. It is an excellent education for a variety of careers. The Ph.D. program allows you to specialize in a specific area of science and learn the most advanced techniques and theories in that area.

It helps expand your knowledge in a specific field and better understand complex topics. It also often comes with better pay and more job opportunities. Therefore, many people decide to pursue it instead of an MBA or other graduate degree.

The field of the internet and technology has been revolutionized. Therefore, this advanced degree is required for most academic positions and is a prerequisite for leadership positions such as department chairs and college presidents.

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Computer Science MS/PhD

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

Master of Science (MS)

The Master of Science (MS) emphasizes research preparation and experience and, for most students, is a chance 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, preparing 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

Department(s)

Electrical Engineering & Computer Sciences

Admit Term(s)

Application Deadline

December 9, 2024

Degree Type(s)

Masters / Professional, Doctoral / PhD

Degree Awarded

GRE Requirements

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PhD Program Admissions

Applying to our PhD Program

We’re thrilled that you are interested in our PhD program in computer science! This page provides an overview of the application process, some guidelines, and answers to specific questions. Please check our FAQ before emailing [email protected] with any questions not answered here.

Our program accepts a large number of applicants each year from a diverse range of backgrounds. Our applicants come directly from undergraduate or master’s programs, as well as industry positions, and from within United States and numerous countries around the world.

Visit the interactive PhD program statistics page   to view historical program data pertaining to admissions, enrollment, retention/attrition/completion, and time to degree conferral. (Select “ Computer Science” from the “Choose Program” drop-down menu.)

Ready to start your PhD application?

We’re ready for you. Click on the link below to start your application to become a PhD student at Johns Hopkins University

The Application: General Advice

The most important question we ask when reviewing applications is "Will this individual excel at research?" Every part of your application is helpful insofar as it answers this question. The three major components of an application are the statement, letters of recommendation, and grades.

This is one of the most important parts of your application; it lets us get to know you and creates a narrative of your academic career and future plans. Before you write your statement, start by thinking about what you want us to learn about you. Make a list of important achievements, perspectives, and goals. Build your statement around this list. We are looking for students who have made the most of the opportunities they have been presented with and who are smart, creative, and motivated. Keep in mind that we also have your CV and letters of recommendation, so we don’t necessarily need a list of all your accomplishments. However, your statement can fill in the narrative around what you did and, more specifically, why you did it. What motivates you? What are your research interests and why? These details aren’t found elsewhere in your application, so focus on them in your statement.

There are a few things we suggest not including in your statement. While it’s tempting to give a rationale for why you are applying to our program, don’t include it if it’s uninformed. Consider: “I want to apply to Johns Hopkins because it’s one of the premiere academic programs.” We know that already! If you do have specific reasons to be interested in our program (e.g. location, a specific project, a faculty member, etc.), be sure to mention them.

In terms of your motivation, be specific! Don’t write: “I’ve wanted to do a PhD in CS since I was six years old.” We don’t trust that six-year-olds make good career decisions. If you write “I have always found AREA X fascinating,” explain why.

Letters of Recommendation

The two most important factors of a recommendation letter are: 1) select someone who knows you well, and 2) select someone who knows how to write a letter.

First, it’s tempting to ask Professor X. to write a letter for you because they are a well-known person in the field. While we can better contextualize letters from people we know, it’s only helpful if the letter contains meaningful information. If Professor X. writes, “I’ve met the applicant a few times and they seem sharp,” that’s not useful information. It’s more important to select someone who knows you well and can discuss your achievements in detail.

Second, your letter writer should know how to write a letter. Academic research programs look for different things than a company. We often read letters from work supervisors that say nice things, but don’t speak to the qualities we find most important.

Of course, it’s a balance. You want someone who knows you well, but they still need to know how to write a good letter of recommendation.

We understand that three letters are a lot, especially for an undergraduate applying directly to a PhD program. We don’t expect each candidate to have three amazing letters. Your choices should be about balance: you want people who know you well, can write good academic letters, and know the research field. Use your choice of your three letter-writers to create this balance.

There isn’t much you can do about your grades—you have the grades you have. However, we do not use any grade cutoffs or thresholds in admissions. We want to see that you did well and excelled in whatever program you were in. Did you push yourself to take upper-level classes? Did you do well in the classes most directly related to your research area? If you have special circumstances that explain some of your grades, please include a description of them in your statement.

The Whiting School of Engineering does not require GRE General Test scores for applications to our PhD programs.

TOEFL or IELTS

Non-native English speakers must take the TOEFL or IELTS exams. Details on accepted exams, scores, and exceptions to this requirement can be found here .

Application Tips

There are many helpful guides for PhD applications. Here are a few we recommend:

• How to be a Successful PhD Student (co-authored by our own Mark Dredze )
• What Readers Look for in a Statement of Purpose
• Student Perspectives on Applying to NLP PhD Programs
• A Survival Guide to a PhD

Application Deadlines

Application Deadline:

The deadline for fall is December 15th. (No recruiting for spring admissions.)

The application will be available for submission on or around August 15.

Vivien Thomas Scholars

The Vivien Thomas Scholars Initiative (VTSI) is an endowed fellowship program at Johns Hopkins for PhD students in STEM fields. It provides full tuition, stipend, and benefits while also providing targeted mentoring, networking, community, and professional development opportunities. Students who have attended a historically black college and university or other minority serving institution for undergraduate study are eligible to apply. To be considered for the VTSI, all application and supplementary materials must be received by December 1, 2021.

CS PhD Course Guidelines

The following program guidelines (a.k.a model pogram) serve as a starting point for a discussion with the faculty about areas of interest.   This description of the Computer Science PhD course guidelines augments the school-wide  PhD course requirements .   Students should make themselves familiar with both.

Course Guidelines for Ph.D. Students in Computer Science

We expect students to obtain broad knowledge of computer science by taking graduate level courses in a variety of sub-areas in computer science, such as systems, networking, databases, algorithms, complexity, hardware, human-computer interaction, graphics, or programming languages.

Within our school, CS courses are roughly organized according to sub-area by their middle digit, so we expect students to take courses in a minimum of three distinct sub-areas, one of which should be theory (denoted by the middle digit of 2, or CS 231). Theory is specifically required as we expect all students to obtain some background in the mathematical foundations that underlie computer science. The intention is not only to give breadth to students, but to ensure cross-fertilization across different sub-disciplines in Computer Science.

Just as we expect all students obtaining a Ph.D. to have experience with the theoretical foundations of computer science, we expect all students to have some knowledge of how to build large software or hardware systems , on the order of thousands of lines of code, or the equivalent complexity in hardware. That experience may be evidenced by coursework or by a project submitted to the CHD for examination. In almost all cases a course numbered CS 26x or CS 24x will satisfy the requirement (exceptions will be noted in the course description on my.harvard). Students may also petition to use CS 161 for this requirement.   For projects in other courses, research projects, or projects done in internships the student is expected to write a note explaining the project, include a link to any relevant artifacts or outcomes, describe the student's individual contribution, and where appropriate obtain a note from their advisor, their class instructor, or their supervisors confirming their contributions.  The project must include learning about systems concepts, and not just writing many lines of code.   Students hoping to invoke the non-CS24x/26x/161 option must consult with  Prof. Mickens ,  Prof, Kung,  or  Prof. Idreos  well in advance of submitting their Program Plan to the CHD.  

Computer science is an applied science, with connections to many fields. Learning about and connecting computer science to other fields is a key part of an advanced education in computer science. These connections may introduce relevant background, or they may provide an outlet for developing new applications.

For example, mathematics courses may be appropriate for someone working in theory, linguistics courses may be appropriate for someone working in computational linguistics, economics courses may be appropriate for those working in algorithmic economics, electrical engineering courses may be appropriate for those working in circuit design, and design courses may be appropriate for someone working in user interfaces.

Requirements

The Graduate School of Arts & Sciences (GSAS) requires all Ph.D. students to complete 16 half-courses (“courses”, i.e., for 4 units of credit) to complete their degree. Of those 16 courses, a Ph.D. in Computer Science requires 10 letter-graded courses. (The remaining 6 courses are often 300-level research courses or other undergraduate or graduate coursework beyond the 10 required courses.)

The requirements for the 10 letter-graded courses are as follows:

• Of the 7 technical courses, at least 3 must be 200-level Computer Science courses, with 3 different middle digits (from the set 2,3,4,5,6,7,8), and with one of these three courses either having a middle digit of 2 or being CS 231 (i.e., a “theory” course).   Note that CS courses with a middle digit of 0 are valid technical courses, but do not contribute to the breadth requirement.
• At least 5 of the 8 disciplinary courses must be SEAS or SEAS-equivalent 200-level courses. A “SEAS equivalent” course is a course taught by a SEAS faculty member in another FAS department. 
• For any MIT course taken, the student must provide justification why the MIT course is necessary (i.e. SEAS does not offer the topic, the SEAS course has not been offered in recent years, etc.). MIT courses do not count as part of the 5 200-level SEAS/SEAS-equivalent courses. 
• 2 of the 10 courses must constitute an external minor (referred to as "breadth" courses in the SEAS “ Policies of the Committee on Higher Degrees [CHD] ”) in an area outside of computer science. These courses should be clearly related; generally, this will mean the two courses are in the same discipline, although this is not mandatory. These courses must be distinct from the 8 disciplinary courses referenced above.
• Students must demonstrate practical competence by building a large software or hardware system during the course of their graduate studies. This requirement will generally be met through a class project, but it can also be met through work done in the course of a summer internship, or in the course of research.
• In particular, for Computer Science graduate degrees, Applied Computation courses may be counted as 100-level courses, not 200-level courses.
• Up to 2 of the 10 courses can be 299r courses, but only 1 of the up to 2 allowed 299r courses can count toward the 8 disciplinary courses. 299r courses do not count toward the 5 200-level SEAS/SEAS-equivalent courses. If two 299r’s are taken, they can be with the same faculty but the topics must be sufficiently different.
• A maximum of 3 graduate-level transfer classes are allowed to count towards the 10 course requirement.
• All CS Ph.D. program plans must adhere to the SEAS-wide Ph.D. requirements, which are stated in the SEAS Policies of the Committee on Higher Degrees (CHD) . These SEAS-wide requirements are included in the items listed above, though students are encouraged to read the CHD document if there are questions, as the CHD document provides further explanation/detail on several of the items above.
• All program plans must be approved by the CHD. Exceptions to any of these requirements require a detailed written explanation of the reasoning for the exception from the student and the student’s research advisor. Exceptions can only be approved by the CHD, and generally exceptions will only be given for unusual circumstances specific to the student’s research program.

Requirement Notes

• Courses below the 100-level are not suitable for graduate credit.
• For students who were required to take it, CS 2091/2092 (formerly CS 290a/b or 290hfa/hfb may be included as one of the 10 courses but it does not count toward the 200-level CS or SEAS/SEAS-equivalent course requirements nor toward the SM en route to the PhD.

Your program plan  must always comply  with both our school's General Requirements, in addition to complying with the specific requirements for Computer Science. All program plans must be approved by the Committee on Higher Degrees [CHD]. Exceptions to the requirements can only be approved by the CHD, and generally will only be given for unusual circumstances specific to the student’s research program

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/images/cornell/logo35pt_cornell_white.svg" alt="computer science phd fields"> Cornell University --> Graduate School

Computer science, field description.

The Field of Computer Science is intended for students who are primarily interested in the general aspects of computational processes, both theoretical and practical. Areas of research in the field include architecture, artificial intelligence, computational biology, database systems, graphics, human interaction, machine learning, natural language processing, programming languages, robotics, scientific computing, security, systems and networking, theory of computation, and vision.

Contact Information

110C Gates Hall Cornell University Ithaca, NY  14853

Data and Statistics

• Research Master's Program Statistics
• Doctoral Program Statistics

Field Manual

Subject and degrees.

• Computer Science (M.Eng.) (Cornell Tech (NYC))
• Computer Science (M.Eng.) (Ithaca)
• Computer Science (M.S.) (Ithaca)
• Computer Science (Ph.D.) (Ithaca)
• Robotics (Ph.D.) (Ithaca)

Concentrations by Subject

• artificial intelligence
• computer science
• programming languages and logics
• scientific computing and applications
• theory of computation

Mohamed Abdelfattah

• Campus: Ithaca
• Concentrations: Computer Science: artificial intelligence
• Research Interests: Machine Learning

Jayadev Acharya

• Concentrations: Computer Science: artificial intelligence; theory of computation
• Research Interests: information theory, machine learning, algorithmic statistics

Rachit Agarwal

• Concentrations: Computer Science: systems; theory of computation
• Research Interests: distributed systems, systems for big data analytics, networking, design analysis of algorithms

David H. Albonesi

• Concentrations: Computer Science: computer science; systems
• Research Interests: computer science; operating systems; information organization and retrieval

Lorenzo Alvisi

• Research Interests: theory and practice of dependable distributed computing
• Campus: Cornell Tech (NYC)
• Concentrations: Computer Science: artificial intelligence; computer science
• Research Interests: I work in the intersection of natural language processing, machine learning, vision, and robotics. My current main research focus is algorithms for natural language understanding with specific interest in situated interactions.

Shiri Azenkot

• Concentrations: Computer Science: scientific computing and applications
• Research Interests: human-computer interactions

Kavita Bala

• Concentrations: Computer Science: artificial intelligence; computer science; programming languages and logics; scientific computing and applications
• Research Interests: interactive rendering; global illumination algorithms; image-based modeling and rendering

Siddhartha Banerjee

• Concentrations: Computer Science: theory of computation
• Research Interests: Stochastic Modeling, Design of Scalable Algorithms, Matching Markets and Social Computing, Control of Information-Flows, Learning and Recommendation

Christopher Batten

• Concentrations: Computer Science: systems
• Research Interests: high performance and energy efficient parallel; computer architecture; VSLI design

Tapomayukh Bhattacharjee

• Concentrations: Computer Science: artificial intelligence; scientific computing and applications; systems
• Research Interests: Artificial Intelligence, Human Interaction, Machine Learning, Robotics

David S. Bindel

• Concentrations: Computer Science: computer science; scientific computing and applications
• Research Interests: numerical analysis; numerical linear algebra; modeling microelectromechanical systems; numerical software engineering

Kenneth Paul Birman

• Research Interests: distributed computing; fault-tolerant network systems; distributed systems security; large-scale network applications
• Campus: Ithaca - (Minor Member)
• Research Interests: Architecture and VLSI

Florentina Bunea

• Research Interests: Statistical Machine Learning Theory, Statistical Foundations for Data Science, High Dimensional Statistics

Mark Campbell

• Research Interests: robotics, sensor fusion, machine learning and perception

Claire T Cardie

• Research Interests: natural language processing; machine learning; artificial intelligence

Eshan Chattopadhyay

• Research Interests: use of randomness in computation, computational complexity theory and cryptography

Tanzeem Choudhury

• Research Interests: systems that can learn how humans behave and interact with their environment and each other

Sanjiban Choudhury

• Concentrations: Computer Science: artificial intelligence; scientific computing and applications
• Research Interests: Artificial Intelligence, Machine Learning, Robotics

Michael Ryan Clarkson

• Concentrations: Computer Science: programming languages and logics
• Research Interests: Programming Languages, Security

Alex Conway

• Research Interests: algorithms
• Research Interests: Development of algorithms in applied and computational mathematics (e.g. numerical linear algebra, computational quantum chemistry, and spectral clustering)

Cristian Danescu-Niculescu-Mizil

• Research Interests: Understanding and modeling complex human social behavior using large scale textual data.
• Research Interests: computer graphics; computer vision; human-computer interaction; computational photography

Christopher Matthew De Sa

• Concentrations: Computer Science: artificial intelligence; computer science; systems
• Research Interests: Developing and understanding algorithmic, software, and hardware techniques for high-performance machine learning

Sarah A Dean

• Research Interests: Machine Learning, Robotics

Nicola Lee Dell

• Research Interests: human computer interaction(HCI) and information and communication technologies for development with a focus on designing and evaluating systems that improve the lives of underserved populations in low-income regions

Raaz Dwivedi

• Research Interests: Artificial Intelligence

Shimon Edelman

• Research Interests: vision; computational biology

Ahmed El Alaoui

• Research Interests: High-dimensional statistics and probability, algorithms, statistical physics

Kevin Michael Ellis

• Concentrations: Computer Science: artificial intelligence; programming languages and logics
• Research Interests: Artificial Intelligence, Machine Learning

Deborah Estrin

• Research Interests: mobile systems and applications, participatory sensing, health applications, privacy

Silvia Ferrari

• Research Interests: Distributed systems, computer vision, and robotics

John N. Foster

• Concentrations: Computer Science: computer science; programming languages and logics; systems
• Research Interests: intersection between programming languages, databases and formal methods

Sainyam Galhotra

• Concentrations: Computer Science: artificial intelligence; systems
• Research Interests: Databases, Responsible Data Science, and Causal Inference

Nikhil Garg

• Research Interests: Mechanism/Market design and data science, with a focus on design of democracy, markets, and other societal systems

Ziv Goldfeld

• Research Interests: optimal transport theory, statistical machine learning, information theory, high-dimensional statistic, applied probability and interacting particle systems

Carla P Gomes

• Research Interests: artificial intelligence; computer science

Donald P. Greenberg

• Research Interests: realistic image synthesis; modeling; scientific visualization; computer-aided design; image processing

Giulia Guidi

• Research Interests: Computational Biology, Scientific Computing, systems and Networking

Francois V. Guimbretiere

• Research Interests: systems; computer science
• Research Interests: Systems, Machine Learning

Joseph Yehuda Halpern

• Concentrations: Computer Science: artificial intelligence; computer science; programming languages and logics; theory of computation
• Research Interests: logic; artificial intelligence; distributed computing; reasoning about uncertainty

Bharath Hariharan

• Concentrations: Computer Science: artificial intelligence; computer science; scientific computing and applications
• Research Interests: Rich visual understanding (object recognition, object detection, segmentation and beyond), machine learning (deep learning, convolutional networks)
• Research Interests: machine learning;data mining; info retrieval, & AI, esp. targeting questions that integrally involve both people & computing

Guy Hoffman

• Research Interests: human-robot interaction, human-robot teamwork and collaboration in particular with respect to interaction fluency

Justin Alpine Hsu

• Concentrations: Computer Science: programming languages and logics; theory of computation
• Research Interests: Programming Languages, Theory of Computing

Thorsten Joachims

• Research Interests: machine learning; text-mining; statistical learning theory; information access

Wendy Guang-wen Ju

• Research Interests: Interaction with automation; interaction design research; human robot interaction; automotive interaction design
• Research Interests: human interaction, security
• Research Interests: robotics

Nathan Kallus

• Research Interests: casual inference, machine learning, personalization, optimization in statistics, data-driven optimization under uncertainty, online decision making, decision making and operations in health care

Michael PumShin Kim

• Research Interests: Theory of computation

Robert D. Kleinberg

• Concentrations: Computer Science: artificial intelligence; computer science; theory of computation
• Research Interests: theory of computation and computer science

Jon M Kleinberg

• Concentrations: Computer Science: computer science; theory of computation
• Research Interests: algorithms; combinatorial optimization; computational geometry; computational biology

Allison Koenecke

Dexter Campbell Kozen

• Campus: Ithaca - (Graduate School Professor)
• Concentrations: Computer Science: computer science; programming languages and logics; theory of computation
• Research Interests: theory of computation; proof-carrying code; computational complexity; analysis of algorithms; program logic and semantics

Hadas Kress-Gazit

• Research Interests: robotics; motion planning; task planning; language for robotics; human-robot interaction

Volodymyr Kuleshov

• Research Interests: artificial intelligence, machine learning, and computational genomics

Lillian Jane Lee

• Research Interests: natural language processing

Daniel Dongyuel Lee

• Research Interests: machine learning, robotics, computational neuroscience

Owolabi Legunsen

• Concentrations: Computer Science: programming languages and logics; systems
• Research Interests: software engineering, applied formal methods

Emaad Manzoor

• Research Interests: machine learning

Stephen Robert Marschner

• Research Interests: appearance models for natural materials; 3D scanning; processing scanned geometric data; image-based appearance measurements for 3D objects

Jose Martinez

• Research Interests: multithreaded and multiprocessor architectures for high performance and programmability; microarchitecture; hardware-software interaction

David Matteson

• Research Interests: Mathematical Foundations of Data Science, Data Science in Science, Machine Learning

David Mimno

• Research Interests: Machine learning, text mining, digital humanities

Kristina Monakhova

• Research Interests: Artificial Intelligence, Computational Biology, Machine Learning, Vision

John Gregory Morrisett

• Research Interests: programming languages; compilers; distributed systems; runtime systems

Andrew C Myers

• Research Interests: programming languages
• Research Interests: Social media, data mining, human-computer interaction, computational social science, interactive systems

Rajalakshmi Nandakumar

• Research Interests: wireless networking, Mobile Systems and Mobile Health.

Anil Nerode

• Concentrations: Computer Science: artificial intelligence; computer science; programming languages and logics; systems; theory of computation
• Research Interests: logic; applied mathematics

Tapan Suryakant Parikh

• Research Interests: human-computer interaction; information and communication technologies for development; computer science education

Francesca Parise

• Research Interests: Theory of Computing, Artificial Intelligence

Rafael N. Pass

• Research Interests: theory; computer science

Kirstin Hagelskjaer Petersen

• Research Interests: design and coordination of bio-inspired robot collectives and their natural counterparts

Emma Pierson

Thomas Ristenpart

• Concentrations: Computer Science: computer science; systems; theory of computation
• Research Interests: Software Security, applied and theoretical cryptography

Thijs Jan Roumen

• Research Interests: Digital Fabrication, Human-Computer Interaction

Alexander Matthew Rush

• Research Interests: natural language processing, machine learning

Mert Sabuncu

• Research Interests: computer vision, data mining, machine learning

Adrian Lewis Dequine Sampson

• Research Interests: hardware-software abstractions,including computer architecture, compilers, software engineering

Katya Scheinberg

• Concentrations: Computer Science: scientific computing and applications; theory of computation
• Research Interests: continuous optimization, stochastic optimization, optimization in machine learning, complexity analysis

Fred Barry Schneider

• Research Interests: distributed systems security and fault-tolerance; mobile code; concurrent programming; operating systems
• Research Interests: Theory of Computation

Bart Selman

• Research Interests: artificial intelligence and experimental computer science

Phoebe J Sengers

• Research Interests: culturally embedded computing; human-computer interaction; everyday computing; affective computing; interactive art; autonomous agents

Vitaly Shmatikov

• Research Interests: computer security and privacy

David B Shmoys

• Concentrations: Computer Science: computer science; scientific computing and applications; theory of computation
• Research Interests: scheduling; computational complexity

Alexandra Silva

Rachee Singh

• Research Interests: Security, Systems and networking

Keith N. Snavely

• Research Interests: computer graphics; computer vision

Karthik Sridharan

• Research Interests: Machine Learning, Statistical Learning Theory, Online Learning and Decision Making, Optimization, Empirical Process Theory, Concentration Inequalities, Game Theory

Noah Stephens-Davidowitz

• Research Interests: lattices, cryptography, and theoretical computer science more broadly
• Research Interests: machine learning, reinforcement learning, decision making under uncertainty
• Research Interests: computer networks; large-scale complex networks; stochastic networks and processes; optimization theory; control theory and applications; game theory
• Research Interests: combinatorics; complexity theory; communication networks; QoS and data flow

Angelique Marie Taylor

• Research Interests: Robotics, Artificial Intelligence

Alex John Townsend

• Research Interests: Scientific Computing

Immanuel Trummer

• Research Interests: databases; data science; optimization

Madeleine Richards Udell

• Concentrations: Computer Science: artificial intelligence; scientific computing and applications; theory of computation
• Research Interests: optimization and machine learning for large scale data analysis and control

Robbert Van Renesse

• Research Interests: distributed computing; fault-tolerance; distributed multimedia systems

Marten van Schijndel

• Research Interests: natural language processing, machine learning, cognitive modeling

Anke van Zuylen

• Research Interests: design and analysis of algorithms, combinatorial optimization, polyhedral combinatorics

Aditya Vashistha

• Research Interests: Artificial Intelligence, Human-computer interaction

Aaron B. Wagner

• Research Interests: information theory especially compression, feedback communication, security and quantum information
• Research Interests: data mining, machine learning, health data science

Hakim Weatherspoon

• Research Interests: information systems; distributed systems; network systems; peer-to-peer systems

Kilian Quirin Weinberger

• Research Interests: Machine Learning: high dimensional data analysis, machine learned web-search ranking, sentiment analysis, metric learning, multitask- and transfer-learning settings and bio-medical applications

Stephen B Wicker

• Research Interests: artificial intelligence, concurrency and distributed computing

Mark McMahon Wilde

• Research Interests: Quantum computation

David Paul Williamson

• Research Interests: algorithms; combinatorial optimization; computer science
• Research Interests: Artificial Intelligence and Scientific Computing and Applications
• Research Interests: theory of computation

Christina Lee Yu

• Research Interests: theory of computing, artificial intelligence (machine learning), scientific computing

Ramin Zabih

• Research Interests: computer vision

Cheng Zhang

• Research Interests: ubiquitous computing, wearable computing, human computer interaction

Zhiru Zhang

• Research Interests: computer-aided design methodologies, optimization algorithms, compilers & computer architectures of gigascale integrated systems, esp. systems-on-chips

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Master's in Computer Science for PhD Students in Other Fields

You are here.

A Master's degree in Computer Science (CS) is available to students enrolled in a PhD or MS/PhD program in the following fields:

• Mathematics
• Applied Mathematics
• Information Science

(A Master's degree in Mathematics is also available to students enrolled in the PhD program in Computer Science; consult the Field of Mathematics for information.)

• 4 residence units
• A Computer Science field member on the special committee
• Passing an A-exam in the student's major field of study
• Knowledge of CS 2110, CS 3110 and CS 4410/4411 (e.g., by having taken these courses at Cornell, or equivalent courses at other institutions)
• Two of the following courses:  CS 6410, CS 6110, CS 6320, CS 6820
• In addition to 4 and 5, any two CS courses numbered 5000 and above (lecture/practicum pairs such as CS 5120/5121, CS 5320/5321, and CS 5620/5621 count as one course).

All courses taken in fulfillment of these requirements must be taken for grade credit, and grades of B– or better in all coursework are required.

Minor amendments to these requirements for a particular student, such as the substitution of one course for another, may be made on a case-by-case basis with the unanimous approval of the special committee and the DGS of CS.  Please note all course requirements must be completed within two semesters of taking the A exam.

Administration

A PhD candidate wishing to receive a master's degree from CS must apply formally. The student must obtain approval from all members of the special committee and apply to the Graduate School for this degree. There is an application form available for this purpose (link below). You must apply to the Graduate School for this degree. To apply, fill out the Application Requirements, have the appropriate parties sign the form and submit it along with your A Exam Schedule form to [email protected] or deliver to 143 Caldwell Hall. Once all requirements have been completed, have the appropriate parties sign the Approval part of this form and submit it to [email protected] or deliver to 143 Caldwell Hall.  

• Latest form can be found under Exams and Research -> Special Master’s Degree in Minor Field Application and Approval Forms (PDF)

Formal registration in CS is not required.

The member of the student's special committee representing CS is primarily responsible for supervising the content of the program of study as it pertains to the master's degree. That member must be present at the A-exam. It is expected that substantial progress toward fulfilling the requirements for the master's degree will have been made by the time of the A-exam, and a suitable demonstration of understanding at the A-exam will be expected. It is not necessary for all requirements to have been met at the time of the A-exam, but the degree will not be awarded until all requirements are satisfied.

When all requirements have been satisfied, a copy of the application form should be signed by all members of the special committee and DGS of CS and submitted to the CS Graduate Office in Upson Hall for communication to the Graduate School.

Special Circumstances

If the student should leave the PhD program or transfer to a different major field that is not one of the approved major fields, the student may still receive the master's degree in CS if all other requirements have been met.

Financial Support

Financial support will still be the responsibility of the major field.

• [email protected]
• +91-97 91 62 64 69

Latest PhD Topics in Computer Science

Computer science is denoted as the study based on computer technology about both the software and hardware. In addition, computer science includes various fields with the fundamental skills that are appropriate and that are functional over the recent technologies and the interconnected world. We guide research scholars to design latest phd topics in computer science.

Introduction to Computer Science

In general, the computer science field is categorized into a range of sub-disciplines and developed disciplines . The computer science field has the extension of some notable areas such as.

• Scientific computing
• Software system
• Hardware system
• Computer Theory

We have an updated technical team to provide novel research ideas with the appropriate theorems, proofs, source code, and data about tools. So, the research scholars can communicate with our research experts in computer science for your requirements. Now, let us discuss the significant research areas that are used to select the latest PhD topics in computer science in the following.

Research Area in Computer Science

• Internet-based mobile ad hoc network (iMANET)
• Smartphone ad hoc network (SPANET)
• Mobile cloud computing
• Soft computing
• Context-aware computing
• Systems and cybernetics
• Learning technologies
• Internet computing
• Information forensics and security
• Dependable and secure computing
• Brain-computer interface
• Audio and language processing
• Wireless sensor networks
• Wireless body area network
• Visual cryptography
• Video streaming
• Vehicular network
• Ad hoc network
• Text mining
• Telecommunication engineering
• Software-defined networking
• Software reengineering
• Service computing (web service)
• Social sensor networks
• Network security and routing
• Cloud computing
• Computer vision and image processing
• Bioinformatics and biotechnology
• Big data and databases
• Cyber security
• Natural language processing
• Embedded systems
• Human-computer interaction
• Networks and security

Frequently, all the research areas in computer science are quite innovative. In addition, we focus on innovative computer science projects and examine all the sections of research works through the models, techniques, algorithms, mechanisms , etc. Now, it’s time to pay equal attention to the consequence of research protocols. So, let us take a glance over the notable protocols that are used in computer science-based projects along with their specifications.

Protocols in Computer Science

• Ad hoc on-demand distance vector is abbreviated as AODV and it is based on the loop-free routing protocol for the ad hoc networks. It is created for the self-starting environment with the mobile nodes along with various network features that include packet loss, link failure, and node mobility
• It is denoted as the reactive and proactive routing protocol in which the routes are revealed as per the necessity
• Dynamic source routing abbreviated as DSR is one of the routing protocols that is used for the functions of wireless mesh networks and it is parallel to the AODV in transmitting the node requests

The above-mentioned are the substantial research protocols along with their descriptions . Thus, you can just contact us to get the finest and latest PhD topics in computer science. Our research experts can help you in all aspects of your research. Now, you can refer to the following to know about the research trends in computer science.

Current Trends in Computer Science

• It is deployed in the process of detecting and segregating the zombie attack based on cloud computing
• Stenography technique is applied in the cloud computing process to develop the security in cloud data
• In the network process, the reduction of fault occurs through the enhancement of green cloud computing
• In cloud computing, the issues are based on load balancing through the usage of a weight-based scheme
• Homomorphic encryption is developed for key sharing and management
• It is deployed in the cloud computing to segregate the virtual side-channel attack
• It is used to develop the cloud data security and watermarking technique in the cloud computing

The following is about the guidelines for research scholars to prepare the finest research work provided by our experienced research professionals.

How to do Good Research in Computer Science?

• Initially, select the research area that you are interested in computer science
• After selecting an area, the researcher has to find an innovative research topic in computer science
• Select good ideas to enhance the state of art
• The real-time implementations are applied
• Possessions based on the selected approach have to be proved and that should be the enhancement of the existing process
• Software tools have to be developed to support the system
• Have to describe the systematic comparison with the other approaches which has the same issue and discuss the advantages and disadvantages of the research notion
• Results based on some research papers have to be accessible

Applications in Computer Science

Manet is deployed to identify some applications in the research areas that are highlighted in the following.

• Detecting the selective forwarding attack in the mobile as hoc networks
• Avoidance of congestion in the mobile ad hoc networks
• It is used in the trust and security-based mechanism of wormhole attack isolation based on Manet
• Scheme is evaluated with the recovery of mobile as hoc network
• Road safety
• Vehicular ad hoc communication
• Environment sensors

The following is the list of research applications in the field of image processing .

• Video processing
• Pattern recognition
• Color processing
• Robot vision
• Encoding and transmission
• Medical field
• Gamma-rayay imaging

In addition, we have highlighted some applications that are related to the bioinformatics research field.

• Modeling and simulation based on proteins, RNA, and DNA are created through tools based on bioinformatics
• It is used to compare the genetic data along with the assistance of bioinformatics tools
• It is deployed in the study of various aspects including protein regulation and expression
• Organization of biological data and text mining has a significant phase in the process
• It is used in the field of genetics for the mutation observation

More than above, the utmost research applications are available in real-time. In overall, it increases the inclusive efficiency in all aspects of the research features. In addition, our research experts have listed down the prominent research topics based on computer science.

• Network and security
• Distributed system
• High-performance computing
• Visualization and graphics
• Geographical information system
• Databases and data mining
• Architectures and compiler optimization

List of Few Latest and Trending Research Topics in Big Data

• The parallel multi-classification algorithm for big data using the extreme learning machine
• Disease prediction through machine learning through big data from the healthcare communities
• Nearest neighbor classification for high-speed big data streams using spark
• Privacy preserving big data publishing: A scalable k-anonymization approach using MapReduce
• Efficient and rapid machine learning algorithms for big data and dynamic varying systems

Software Engineering-Based Topics in Computer Science

• It is used to support team awareness and collaboration, distributed software development, open source communities, and software as the service
• Software modeling and reasoning
• The reasoning and modeling based on software along with the reasoning specifications in security and safety, analysis of model-driven software development, analysis of requirements modifications, and product timeline
• Dependencies of stakeholders
• Enterprise contexts
• Modeling and analysis of software requirements

Latest Computer Networking Topics for Research

• Data security in the local network through the distributed firewalls
• Efficient peer-to-peer keyword searching
• Tolerant routing on mobile ad hoc network
• Hybrid global-local indexing for efficient peer-to-peer information retrieval
• Application of genetic algorithms in network routing
• Bluetooth-based smart sensor networks
• ISO layering model
• Distributed processing and networks
• Delay tolerant network
• Wireless intelligent networking
• Network security and cryptography

The abovementioned are the contemporary and topical research topics based on the computer science research field. In addition, the research experts have highlighted the latest phd topics in computer science domain detailed in the following.

Area-Based Topics Process

• Human-robot interaction
• Digital fabrication
• Critical computing
• UI technologies
• Information visualization
• Information and communication technology and development (ICTD)
• Computer-supported cooperative work
• Computer-supported cooperative learning
• Augmented and virtual reality
• Shape modeling
• Geometry processing
• Computational imaging
• Computing fabrication
• Translating computational tools
• NLP and speech for healthcare and medicine
• Satisfiability in reasoning
• Sequential decision making
• Multi-agentnt system
• Cognitive robotics
• Knowledge representation
• Human motion analysis
• Computational photography
• Object recognition
• Physics-based modeling of shape and appearance
• Cognitive modeling of language acquisition and processing
• Applications of NLP in healthcare and medicine
• Formal perspectives on language
• Applications of NLP in social sciences and humanities
• Machine translation
• Speech processing

Now, let’s have a glance over the list of research tools that are used in the implementation of research in computer science.

Simulation Tools in Computer Science

For your information, our technical professionals from computer science backgrounds have given you some foremost research questions with answers, to what the researchers are looking for.

Research Questions Computer Science

How to implement ad hoc routing protocols using omnet++.

Oment++ environment is implemented through the adaptations and it is enabling for the contrast simulation results with the designs of the Manet application. The routing protocols such as DSR and AODV are used in the process and as the open source code.

How is Hadoop used in big data?

In general, Hadoop is considered as the java and open source framework that is deployed in the process of big data storing. Mapreduce programming model is deployed in Hadoop for the speed process of data storage.

What are the trending technologies in computer science?

• Artificial intelligence (AI)
• Everything as a service
• Human augmentation
• Big data analytics
• Intelligent process automation (IPA)
• Internet of behaviors (IoB)
• 5G technology

What are the major areas in the field of computer science?

• Theory of computing
• Bioinformatics
• Software engineering
• Programming languages
• Numerical analysis
• Vision and Graphics
• Human-computerer interaction
• Database systems
• Computer systems and network security

How to implement artificial intelligence in python?

Generally, this process includes four significant steps and they are highlighted in the following.

• Organizational and AI capabilities that are essential for digital transformation are apprehended
• Business ecosystem role, the potential for BMI, and current BM are comprehended
• Capabilities are enhanced and cultivated for the AI execution
• Internal is developed and organizational acceptance is reached
• Tensor flow

Taking everything into account, the research scholars can grasp any innovative and latest PhD topics in computer science from our research experts. Consequently, we guide research scholars in all stages. In the same way, we make discussions with you at all stages of the research work. So, scholars can closely track the research work from everywhere in the world. Additionally, our well-experienced research professionals will provide significant assistance throughout your research process.

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Is a PhD in Computer Science Worth It in 2024?

As applicants dutifully craft their graduate school statements of purpose and tailor their applications in hopes of acceptance, a fundamental question lingers – is pursing a PhD in Computer Science truly worth the immense investment of time, money and effort in 2024 and beyond? While a PhD opens doors to prestigious academic research positions and confers the highest academic credential, the opportunity costs and uncertainty surrounding the job market, particularly for new PhDs, are substantial. In this post, we’ll explore both sides of the “Is a PhD in computer science worth it?: question through rigorous analysis of admissions trends, employment outcomes, earning potential and quality of life considerations. Our goal is not to make the decision for you, but to present our perspective on the ROI of a PhD in computer science in today’s rapidly changing technological and economic landscape to help inform your choice.

Navigating the Digital Frontier

In the rapidly evolving digital era, asking the questions: “Is a PhD in Computer Science worth it in 2024?” is a highly relevant question. The decision isn’t trivial, as it involves considering both the significant time and financial investments required for doctoral studies.

One must consider the technological advancements and the increasing demand for advanced skills in artificial intelligence, machine learning, data science, cybersecurity, and other specialized areas of computer science. For example, breakthroughs in AI and machine learning have opened up new avenues of research and practical applications, from self-driving cars to predictive analytics in healthcare, necessitating a deeper understanding that often a PhD education can provide.

At the same time, cybersecurity threats have become more sophisticated, requiring advanced expertise to design robust security systems. Furthermore, the rise in big data has created an escalating demand for data scientists capable of analyzing and interpreting complex sets of information.

Weighing the Pros and Cons

However, if you are asking yourself “Is a PhD in computer science worth it?” there are compelling arguments against pursuing a PhD in Computer Science in 2024. The time commitment is significant, often requiring 4-5 years of dedicated study beyond a master’s degree. This time could otherwise be used to gain practical experience in the industry and climb the career ladder. As rapidly as technology evolves, there is a risk that the specific area of research chosen at the start of a PhD might become obsolete by the time the degree is completed. Moreover, the financial return on investment isn’t always guaranteed.

According to Payscale, the average salary for a PhD in Computer Science is approximately $139,000 per year, while a Software Engineer with a bachelor’s degree and five-plus years of experience can potentially earn around the same salary or even more . Also, the increasing availability and quality of online learning platforms and bootcamps provide cheaper and faster alternatives to acquire advanced skills in AI, machine learning, data science, and cybersecurity, challenging the exclusivity of PhD programs for high-level education in these fields.

While these arguments against pursuing a PhD in Computer Science may hold weight, there are still valid reasons for considering this advanced degree.

A PhD in Computer Science does not merely represent the pinnacle of formal education in the field but serves as a conduit to an expansive realm of opportunities and intellectual challenges that go beyond the conventional. It is a journey that encourages the exploration of uncharted territories in technology, the creation of new knowledge, the refinement of existing theories, and the chance to contribute significantly to the evolution of computer science.

Today, the motivations for undertaking this rigorous academic expedition are as diverse as the subject itself. For some, a PhD serves as a stepping stone into academia, a platform from which they can educate future generations of tech enthusiasts and drive forward the boundaries of research. Others are drawn to the comprehensive understanding and mastery of a specific topic that a PhD provides, skills that are highly sought after in a competitive job market.

In an era where technology is rapidly evolving and its impact on society is profound, a PhD also offers the privilege to influence this trajectory and address significant societal challenges. The question then arises, do these motivations still hold true in 2024?

The answer seems to be a resounding yes. As the pace of technological advancement accelerates, the demand for experts capable of navigating and shaping this landscape grows. A PhD in Computer Science continues to open doors to exciting careers in academia, research, and the tech industry while offering the personal satisfaction of an in-depth understanding of the field.

ROI of a PhD

The return on investment (ROI) of a PhD in Computer Science versus a career as a software engineer depends heavily on several factors, including career goals, job market dynamics, and personal interests. On a strictly financial basis, the ROI for a software engineer can be more immediately rewarding in the short and medium-term. Nonetheless, a PhD can offer non-monetary rewards such as personal fulfillment, academic influence, and a deeper understanding of specific areas in the field. Therefore, when evaluating the ROI of a PhD, it is crucial to consider both financial and non-financial returns, as well as individual career aspirations.

Job opportunities

For many, the important question: Is a PhD in computer science worth it? is embedded in career considerations. When it comes to job opportunities, the career paths available to master’s-level graduates and PhD holders diverge significantly, shaped by the educational focus of each degree. Master’s graduates, particularly in computer science, often find themselves in high-demand industry roles like software developers, data scientists, or systems analysts, where they can apply their technical skills and theoretical knowledge directly. Industry demand for such roles is burgeoning with the increasing digitisation of our world and the pivotal role technology plays across sectors.

On the other hand, PhD graduates, equipped with a deeper proficiency and specialisation in their field of study, often find opportunities in academia or research-intensive roles. They contribute to cutting-edge advancements in their field, often as research scientists or professors. The demand in academia, however, does not mirror the explosive growth seen in the industry. The number of academic positions is often limited, creating a highly competitive environment.

However, industry opportunities for PhD holders are on the rise, particularly in sectors like technology, pharmaceuticals, and finance, where research and development are critical. These roles often offer the chance to work on challenging problems, pushing the boundaries of what’s possible in their field.

Consequently, the choice between a master’s or a PhD should hinge on individual career aspirations, whether they lean towards immediate application of skills in industry roles or contributing to the knowledge pool with intensive research.

Lifestyle considerations

When contemplating lifestyle considerations between pursuing a PhD or opting for a career in software engineering, it is crucial to recognise the distinct challenges and rewards each path presents. The PhD journey can be a long and arduous one, often requiring a significant investment of time and intellectual effort over several years. It involves long hours of intensive research, extensive reading, and rigorous academic writing, often with little financial remuneration. Stress levels can be high due to the pressure to publish and uncertain job prospects. The work-life balance can be skewed, with late-night study sessions and minimal time for leisure or family.

However, a PhD can also be immensely rewarding. It offers the chance to delve deeply into a topic of interest, contribute to the body of knowledge in a field, and might lead to a career in academia or high-level research, impacting the future of science, technology, or society.

On the other hand, a software engineering career, while also demanding, provides immediate financial benefits and stability. Work hours in software engineering can be long, especially during project sprints, but they are often compensated by competitive salary packages, comprehensive benefits, and a more predictable work schedule. Stress in this career can stem from tight deadlines, demanding clients, or technical challenges, but the satisfaction of solving problems, seeing tangible results from one’s work, and potential for rapid career advancement can offset these pressures. The work-life balance might be more manageable, with more time for personal pursuits and family. However, it’s worth noting that software engineering does not offer the same depth of specialization or potential for academic contribution as a PhD. Consequently, the choice between these paths should be guided by one’s personal and professional goals, financial needs, and lifestyle preferences.

Alternatives to a PhD

While a PhD offers extensive knowledge and research opportunities, it’s not the only path to gain expertise in fields like AI and ML. A master’s degree in computer science or data science can also provide a solid foundation in these areas and provide a satisfying answer to the “Is a PhD in computer science worth it?” question for many prospective students.

Master’s programs are typically shorter in duration and more application-focused, making them a practical choice for those aiming to enter the industry swiftly. They often allow students to specialize in a sub-discipline, such as AI or ML, and provide hands-on experience through projects and internships.

Further, many prestigious universities and organizations offer online courses on platforms like Coursera, edX, and Udacity. These courses, often taught by industry experts, offer flexible learning schedules and are a cost-effective way to gain knowledge in specific areas. They also provide certificates upon completion, which can enhance your professional profile. For those contemplating a career switch, these options could be valuable.

Bootcamps, intensive training programs that teach coding and data skills, are another alternative. They provide focused, practical training in a short time frame, enabling career transitions into tech roles. However, it’s important to note that while these alternatives can provide the necessary technical skills, the depth of knowledge and research experience that a PhD offers might still be required for certain roles, particularly those in research and academia.

Individual factors

Individual factors play a critical role in determining whether pursuing a PhD is the right choice. First and foremost, personal interest and intellectual curiosity cannot be discounted. A PhD is a rigorous and intensive endeavor, often requiring several years of study, and success in such a program often hinges on having a genuine passion for the subject matter. Those who derive satisfaction from conducting original research and contributing new knowledge to their field may find a PhD to be immensely rewarding.

Additionally, a person’s career aspirations greatly influence the value of a PhD. Those aiming for high-level research roles, faculty positions, or leadership roles in certain technical industries might find a PhD essential, as it provides a level of depth and specialization that’s often sought in these positions.

Conversely, for those interested in roles that prioritize practical skills and immediate application, alternatives like master’s programs or bootcamps might be more beneficial. Furthermore, individual skill sets and learning styles can also impact this decision. A PhD program requires a high level of independence, critical thinking, and resilience in the face of challenges and setbacks. Those who thrive in this type of environment may find a PhD program to be a good fit.

What to look for when choosing a PhD in computer science

When choosing a PhD in computer science, several important factors should be taken into account. The reputation and academic rigor of the institution and program are paramount; they not only affect the quality of education you’ll receive but also how your degree is perceived in the academic and professional community. Look at the university’s research output, faculty expertise, and alumni success to gauge this. Faculty members play a critical role in your doctoral journey; their research interests should align with yours, and they should be open to mentoring students.

The department’s resources, including research facilities and funding opportunities, can greatly impact your PhD experience. Having access to advanced technology and software, research funding, and opportunities to attend conferences or workshops, can enhance your learning and research.

Program curriculum is another important consideration; it should provide a balance of breadth and depth, covering fundamental courses and allowing specialization in your area of interest.

The program’s time length is another factor to consider; a PhD can take anywhere from 3 to 6 years, depending on the program and your pace of study. Evaluating these considerations can help you identify a program that aligns with your career goals and provide a strong foundation for your future in computer science.

How difficult is it to get admitted?

Securing admission into a PhD in Computer Science program in 2024 is anticipated to be quite challenging, given the increasing competition and stringent selection criteria. The surge in the demand for computer scientists, fueled by rapid technological advancements, has led to a rise in applications for such programs.

Universities are looking for candidates with a solid foundation in computer science fundamentals, combined with research experience and potential. Most require a master’s degree in computer science or a related field, although some do admit exceptional candidates with only a bachelor’s degree.

Apart from academic qualifications, universities consider the candidate’s GRE scores, letters of recommendation, research statement, and personal statement. Strong letters of recommendation that attest to the candidate’s research prowess can significantly enhance the application. A well-articulated research statement that aligns with the research interests of the faculty can also make a compelling case for admission. The personal statement should reflect the candidate’s passion for computer science and demonstrate their commitment to contributing to the field.

An applicant’s publications and participation in research projects or internships can further strengthen the application.

In essence, a combination of academic excellence, research experience, and a compelling personal narrative are crucial in securing admission into a computer science PhD program in 2024.

Making the decision: Don’t do it alone

Navigating the complex decision-making process around pursuing a PhD in computer science in 2024 can be significantly eased by seeking the guidance of mentors and experienced individuals in the field. These individuals, be they professors, seniors in academia, industry professionals, or career counselors, can provide a wealth of insights about the current trends, future prospects, and implicit challenges of a career in computer science research. They can assist in evaluating the alignment between individual career aspirations, academic interests, and the commitment required for a doctorate.

Furthermore, mentors can play a pivotal role in strengthening your application process. They can provide feedback on your research statement, help articulate your personal narrative, and might offer invaluable letters of recommendation. The benefits of seeking such mentorship are multifold – from making an informed decision about your future to gaining a competitive edge in the admission process. In a field as dynamic and rapidly evolving as computer science, such mentorship could make the crucial difference between merely surviving and truly thriving in your academic journey.

In conclusion, pursuing a PhD in computer science is not a decision to be taken lightly. While it can open doors to prestigious academic positions and comes with the title of highest academic credential, there are significant opportunity costs and job market uncertainties to consider. However, after delving into admissions trends, employment outcomes, earning potential, and quality of life considerations, it’s clear that a doctoral degree in computer science is still a valuable investment in 2024 and beyond. Ultimately, the choice rests on whether one is truly passionate about research and the advancements of technology, as well as their individual career aspirations.

We hope this post has provided insight into both sides of the argument if you have have been wondeing “Is a PhD in computer science worth it?” and helped guide your decision-making process. Remember, our goal is not to make the decision for you but provide valuable information to empower you in making an informed choice. If you decide that a PhD in computer science is the right path for you, be sure to check out our PhD application services for personalized guidance and support on your journey towards a fulfilling and rewarding career in academia or industry. As always, keep exploring, stay curious, and never stop learning!

With a Master’s from McGill University and a Ph.D. from New York University, Dr. Philippe Barr is the founder of The Admit Lab . As a tenure-track professor, Dr. Barr spent a decade teaching and serving on several graduate admission committees at UNC-Chapel Hill before turning to full-time consulting. With more than seven years of experience as a graduate school admissions consultant, Dr. Barr has stewarded the candidate journey across multiple master’s and Ph.D. programs and helped hundreds of students get admitted to top-tier graduate programs all over the world .

Subscribe to my YouTube Channel for weekly tutorials on navigating the PhD application process and live Q&A sessions!

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I’m so glad you pointed out how after you’ve whittled down your list of suitable programs, it’s critical to conduct in-depth research on each one’s curriculum and faculty. I can see how it’s important to verify the faculty members’ field-specific knowledge and experience, as well as how closely their research relates to your interests. This will be shared with my mom who is thinking of finally getting her doctorate degree after ten years. It’s always been her dream so I hope she finds the perfect university.

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What is Electrical and Computer Engineering?

Electrical and computer engineering is shaping our digital world.

It combines principles of electrical engineering and computer science to develop cutting-edge systems and devices that enhance connectivity, efficiency, and quality of life. If you’re…

• Passionate about using technology to solve complex problems and drive innovation 
• Have a background in STEM
• Want to make a tangible impact with your work

…exploring electrical and computer engineering is the perfect pathway to a rewarding career. 

But first, you’ll need to understand the field and which skills you need for success.

Unpacking the Definition of Electrical and Computer Engineering

Electrical and computer engineering encompasses the study, design, development, and application of electrical, electronic, computer systems and related technologies. It involves designing and improving equipment and systems that use electricity, electronics, and electromagnetism — from power generation systems and electric motors to radar and navigation systems.

What is Electrical Engineering?

Electrical engineering focuses on the study and application of electricity, electromagnetism, and electronics. Professionals in the field design, develop and test electrical and electronic components, circuits, systems, and equipment. Core concepts include:

• Circuit theory
• Signal processing
• Electromagnetics
• Power systems
• Control systems
• Telecommunications.

What do electrical engineers do?

Electrical engineers work across diverse real-world applications: power generation and distribution, telecommunications networks, industrial machinery control systems, electrical components for vehicles and aircraft, medical diagnostic and therapeutic equipment, and more. 

Their innovations range from power distribution grids to telecommunications networks and industrial control systems; whether the average person knows it or not, much of our modern life is shaped by their work.

What is Computer Engineering?

It’s a simple equation:

Electrical engineering + computer science = computer engineering.

To put it more broadly, computer engineering combines electrical engineering and computer science principles to design, develop, and integrate computer hardware and software systems. While closely related to computer science, it has a stronger focus on computing systems' physical aspects.

Computer Engineering vs. Computer Science

Though related, distinct differences exist between computer engineering and computer science , and if you want to go into computer and electrical engineering, you’ll need to understand them:

• Computer engineering is hardware-oriented , focusing on designing and developing physical computing devices and systems. 
• Computer science is software-oriented , emphasizing theoretical foundations, algorithms, programming languages, and optimizing software applications.
• Key areas in computer engineering include digital logic design, computer architecture, microprocessors, embedded systems, computer networks, and hardware/software integration. 
• Key areas in computer science include programming languages, data structures, algorithms, software engineering, artificial intelligence, and database systems.

The Intersection of Computer and Electrical Engineering

Computer and electrical engineering are deeply intertwined disciplines, with principles from electrical engineering applied to computer hardware and system design.

For example, developing embedded systems may involve electrical engineers designing power and control circuits while computer engineers focus on microprocessors and software. Their synergy results in systems performing complex tasks like controlling industrial machinery or executing real-time vehicle computations.

Collaborative Innovations of Computer and Electrical Engineering

Self-driving cars: Computer scientists create algorithms enabling vehicles to perceive their environment, make decisions, and navigate autonomously. Computer engineers design and integrate hardware like sensors, processors, and control systems to allow that software to function.

Smart home devices: Computer scientists develop AI algorithms for interpreting voice commands and automating tasks. Computer engineers create the hardware — microcontrollers, wireless modules and user interfaces — enabling devices to interact with the physical world.

What Do Electrical and Computer Engineering Careers Look Like?

According to the Bureau of Labor Statistics , demand for computer and electrical engineers is highly favorable. Between 2022 and 2032, employment is projected to grow 5%, with the 2023 median electrical and computer engineering salary being $109,010 per year for computer scientists and $138,080 per year for computer engineers.

Potential Electrical and Computer Engineer Jobs

Career satisfaction is generally high for electrical and computer engineers because they get to work on innovative, societally impactful projects. Potential jobs for those with the right skills include:

• Hardware design engineer
• Embedded systems engineer
• Robotics engineer
• Power systems engineer
• Telecommunications engineer
• Semiconductor engineer 

Professionals in these roles have given much to our modern world, including:

Internet and World Wide Web: Their contributions to networking protocols, data transmission technologies, and the creation of the World Wide Web have revolutionized communication, commerce, and information dissemination worldwide.

Mobile communication and smartphones: The invention and continuous improvement of mobile communication technologies by electrical and computer engineers has revolutionized many aspects of modern life, including communication, navigation, entertainment, and commerce.

Renewable energy technologies: Electrical and computer engineers have made significant contributions to the development and implementation of renewable energy technologies, such as solar power, wind power, and energy storage systems.

If you’d like to enter into a field with high earning potential, projected job growth, career satisfaction and positive impact, knowing what skills and qualifications you’ll need for ECE engineering is the next step.

Impact Innovation as an Electrical and Computer Engineer

To become an electrical or computer engineer, earning a degree in the field is crucial. Many pursue master's or Ph.D. in engineering to open additional opportunities, further their training in the field, and increase their earning potential.

That’s because electrical and computer engineers are highly skilled, and also in very high demand; If you want to be successful in the field, you’ll need top-notch skills like strong problem-solving, proficiency in programming and computer-aided design (CAD), knowledge of math and physics principles, and excellent communication and collaboration abilities.

Our advice? Look towards graduate programs to deepen your expertise.

Here are some options:

• The M.S. in Computer Engineering at SMU Moody focuses on computer system design and analysis, covering computer architecture, digital systems design, and software engineering.
• The M.S. in Electrical Engineering at SMU moody offers specializations like telecommunications, signal processing, and microelectronics for various industry roles.
• The Ph.D. in Computer Engineering allows cutting-edge research advancing the field's knowledge.
• The Ph.D. in Electrical Engineering provides advanced training in communications, signal processing, microelectronics, systems, and control for research and academic careers.

Electrical and computer engineers are more in-demand than ever before — and there is space for you.

Help shape the future in a field where extraordinary advances are announced every day and download our resource, A Complete Guide to Earning Your Ph.D. in Electrical and Computer Engineering.

Request more information about SMU's electrical and computer engineering graduate programs, or apply now to begin your journey in this impactful field.

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Computer Science

Welcome to the Department of Computer Science

Join us for one of six undergraduate major programs and two graduate programs and jump start your successful career in the tech industry, research or academia.

Learn object-oriented programming and algorithms, computer science fundamentals, web and app development, data science, or blend your love for art, dance, music, theater, digital media or game development with computing. 

When you complete your studies in computer science, you become highly valued by industry, governments, entrepreneurs and graduate schools. Jobs are plentiful with starting salaries ranging between $70K and $100K for the southeast region, not including signing bonuses and stock options.

World-Class Degree Programs

Established in 1980, the Department of Computer Science offers six undergraduate major programs, four undergraduate minors, two graduate programs, two graduate certificates and an accelerated Bachelor's to Master's (4+1) program in a high-tech facility at the Charleston Harbor.

Experiential Learning

Participate in hands-on learning and research or study abroad

Undergraduate Research

Engage in undergraduate research in our eight high-tech research labs specializing in areas such as:

• Cybersecurity
• Machine Learning and Data Science
• Data Mining and IoT Connectivity
• Artificial Intelligence
• Health Informatics
• Cloud Computing
• Computer Music and Interaction
• Virtual Reality
• Game Design and Development
• Computing Education Research

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Discover our Student Innovation Center and student workrooms – a high-tech playground for our students

Be part of a winning tradition

Our students have clinched national competitions, including:

• The nationwide 2020 RISE Competition : winners Stefanie Elling & Joshua Gilley
• The 2020 Adobe X Airbnb Creative Jam : winner Paul Dierksheide
• The winning team of the 2019 ICPC Competition (Division II)
• And the 2021 Southeast Collegiate Cyber Defense Competition champions

What Starts Here

   , ph.d. program, master's programs, portfolio program in robotics, admissions & incoming students, current students, online programs & degrees, master's degrees, student experience, master's program.

The Master of Science in Computer Science (MSCS) program is designed for students who have completed a bachelor's degree in computer science and want to further their studies. Admission to the program is highly selective with a limited number of openings and many strong applicants each year. Only applicants who possess a bachelor's degree in computer science or equivalent are likely to be competitive for admission.

On-Campus Research Track Degree Options

The department offers two on-campus Master's degree options for students interested in pursuing a research career: the MSCS with thesis and the MSCS no thesis/no report. Both degrees require 30 hours of coursework. The no thesis/no report option is defined by more organized coursework than the thesis option, which requires 2 thesis classes. Students admitted to the Master's program do not need to declare which degree they intend to pursue. Students apply to receive the degree in the semester they complete the requirements for the option they chose.

Online Professional Degree Programs

For students seeking a professional master's degree, the department offers three master’s degrees in an online format. Taught by UT faculty, many of whom are award-winning leaders in the CS research community, these programs provide a flexible learning environment while fostering personal engagement between professors and students. By the end of any of these programs, students will have gained the skills needed to accelerate their careers in their related field.

Students seeking a professional degree are strongly urged to apply to one of our online programs, which have a much higher capacity than on-campus programs.

Online Master’s in Computer Science

The online Master of Science in Computer Science program is primarily designed for working professionals and those planning a career in industry who have a bachelor’s degree in computer science or a related technical field and want to broaden and deepen their knowledge.

Online Master’s in Artificial Intelligence

The Master of Science in Artificial Intelligence (MSAI) students will receive advanced training in natural language processing, reinforcement learning, computer vision, deep learning and related topics. The degree will provide a critical framework for understanding the ethical implications of AI technologies and equip students for an array of potential career opportunities – from engineering to research and development, and product management to consulting.

Online Master’s in Data Science

UT Computer Science has partnered with the Department of Statistics and Data Science to offer an online Master of Science in Computer Science program (MSDS). The MSDS program supplies rigorous instruction to expand your expertise in areas such as advanced systems design, machine learning and artificial intelligence. Interested in applying?

Ph.D. Students Pursuing a Master's

Ph.D. students may also pursue the research-track on-campus MSCS degree as long as it does not interfere with their pursuit of the doctorate.

• MyU : For Students, Faculty, and Staff

Alex Wege wins best student poster at IEEE WAMICON 2024

Alumnus Alex Wege is the recipient of a best student poster award at the IEEE WAMICON 2024 held in April in Clearwater, Florida. Wege placed third in the competition for his poster titled, “Characterization of Zynq® FPGA for Microwave Ferromagnetic Resonance Identification (FMR-ID).”

Wege’s poster presents how a Xilinx radio frequency system on chip (RFSoC) and RF frontend/daughter card can be used for chipless, microwave radio frequency identification (RFID) measurements in place of a vector network analyzer (VNA). VNAs typically are too large and expensive to be commercially used, which confines them to laboratory environments. Wege’s characterization of this system that is based on field-programmable gate arrays (FPGA) can support the easy commercialization of chipless RFID and contribute to its wider use. 

Wege earned his undergraduate degree in electrical engineering with a minor in computer science (spring 2020) during which time he focused on embedded systems. As a graduate student he focused on wireless/microwave systems and joined McKnight Presidential Endowed Professor Rhonda Franklin’s laboratory as a member of her research team. He worked on developing a chipless RFID reader driven by his interest in software defined radio. Wege is a ham radio operator and his hobby has inspired some of his academic and research interests. 

Wege graduated with his master’s degree in electrical and computer engineering in spring 2024, and is currently working with Tomorrow.io on the development of their software defined radar system for spaced based weather radar.

Learn about Professor Rhonda Franklin's research

About IEEE WAMICON

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