phd computer science subjects

• Research & Faculty
• Offices & Services
• Information for:
• Faculty & Staff
• News & Events
• Contact & Visit
• About the Department
• Message from the Chair
• Computer Science Major (BS/BA)
• Computer Science Minor
• Data Science and Engineering Minor
• Combined BS (or BA)/MS Degree Program
• Intro Courses
• Special Programs & Opportunities
• Student Groups & Organizations
• Undergraduate Programs
• Undergraduate Research
• Senior Thesis
• Peer Mentors
• Curriculum & Requirements
• MS in Computer Science
• PhD in Computer Science
• Admissions FAQ
• Financial Aid
• Graduate Programs
• Courses Collapse Courses Submenu
• Research Overview
• Research Areas
• Systems and Networking
• Security and Privacy
• Programming Languages
• Artificial Intelligence
• Human-Computer Interaction
• Vision and Graphics
• Groups & Labs
• Affiliated Centers & Institutes
• Industry Partnerships
• Adobe Research Partnership
• Center for Advancing Safety of Machine Intelligence
• Submit a Tech Report
• Tech Reports
• Tenure-Track Faculty
• Faculty of Instruction
• Affiliated Faculty
• Adjunct Faculty
• Postdoctoral Fellows
• PhD Students
• Outgoing PhDs and Postdocs
• Visiting Scholars
• News Archive
• Weekly Bulletin
• Monthly Student Newsletter
• All Public Events
• Seminars, Workshops, & Talks
• Distinguished Lecture Series
• CS Colloquium Series
• CS + X Events
• Tech Talk Series
• Honors & Awards
• External Faculty Awards
• University Awards
• Department Awards
• Student Resources
• Undergraduate Student Resources
• MS Student Resources
• PhD Student Resources
• Student Organization Resources
• Faculty Resources
• Postdoc Resources
• Staff Resources
• Purchasing, Procurement and Vendor Payment
• Expense Reimbursements
• Department Operations and Facilities
• Initiatives
• Student Groups
• CS Faculty Diversity Committee
• Broadening Participation in Computing (BPC) Plan
• Northwestern Engineering

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.

CS PhD Manual Apply now

Request More Information

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

Request info about the PhD degree

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

More in this section

• Engineering Home
• CS Department

Related Links

• The Graduate School
• Graduate Funding
• International Office
• Graduate Housing
• Meet Our Faculty

Contact Info

Admissions Questions

Help for Current PhD Students

Director of Graduate Studies for PhD Program

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

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

In Computer Science

• First-Year Exploration
• Concentration Information
• Secondary Field
• Senior Thesis
• AB/SM Information
• Student Organizations
• How to Apply
• PhD Timeline
• PhD Course Requirements
• Qualifying Exam
• Committee Meetings (Review Days)
• Committee on Higher Degrees
• Research Interest Comparison
• Collaborations
• Cross-Harvard Engagement
• Lecture Series
• Clubs & Organizations
• Centers & Initiatives
• Alumni Stories

Carnegie Mellon University School of Computer Science

Doctoral programs.

In any of the Ph.D. programs across our seven departments, you'll be matched with an advisor based primarily on mutual research interests and begin a research project on day one. All our Ph.D. students receive full financial support while in good academic standing, which helps ensure freedom to explore regardless of funding hurdles. We also believe that it's vital for advisors and students to work as peers, and the inherent flexibility of our programs means students often work with more than one faculty member and many other students during their time in SCS.

Together, our research environment and interdisciplinary mindset produce graduates who emerge into the world ready to tackle its biggest problems.

• Doctoral Programs Home

Interested in Applying?

• Graduate Admissions Overview
• Frequently Asked Questions

Program Contact

Robert Frederking Associate Dean for Doctoral Programs

Explore Our Ph.D. Programs

Ray and stephanie lane computational biology department, computer science department, human-computer interaction institute.

Ph.D. in Human-Computer Interaction

Language Technologies Institute

Ph.D. in Language and Information Technologies

Machine Learning Department

Robotics institute.

Ph.D. in Robotics

Software and Societal Systems Department

Ph.D. in Societal Computing (SC) Ph.D. in Software Engineering (SE)

Dual Degree Ph.D. Programs

The carnegie mellon portugal program (cmu portugal), ph.d. in computer science/dual degree portugal, ph.d. in human-computer interaction/dual degree portugal, ph.d. in language and information technologies/dual degree portugal, ph.d. in robotics/dual degree portugal, ph.d. in software engineering/dual degree portugal.

Departments

• Applied Physics
• Biomedical Engineering
• Center for Urban Science and Progress
• Chemical and Biomolecular Engineering
• Civil and Urban Engineering
• Computer Science and Engineering
• Electrical and Computer Engineering
• Finance and Risk Engineering
• Mathematics
• Mechanical and Aerospace Engineering
• Technology, Culture and Society
• Technology Management and Innovation

Degrees & Programs

• Bachelor of Science
• Master of Science
• Doctor of Philosophy
• Digital Learning
• Certificate Programs
• NYU Tandon Bridge
• Undergraduate
• Records & Registration
• Digital Learning Services
• Teaching Innovation
• Explore NYU Tandon
• Year in Review
• Strategic Plan
• Diversity & Inclusion

News & Events

• Social Media

Looking for News or Events ?

Computer Science, Ph.D.

• Request Information

We have a thriving Ph.D. program with approximately 80 full-time Ph.D. students hailing from all corners of the world. Most full-time Ph.D. students have scholarships that cover tuition and provide a monthly stipend. Admission is highly competitive. We seek creative, articulate students with undergraduate and master's degrees from top universities worldwide. Our  current research strengths  include data management and analysis, cybersecurity, computer games, visualization, web search, graphics, vision and image processing, and theoretical computer science.

This degree program offers interested students opportunities to do their research abroad, under the supervision of faculty at NYU Shanghai or  NYU Abu Dhabi .

• View the Computer Science Ph.D. program flyer
• Admissions requirements for the Ph.D. Program.
• Find out more about general  Admission Requirements .

To receive a Ph.D. in Computer Science at the NYU Tandon School of Engineering, a student must:

• satisfy a breadth course requirement, intended to ensure broad knowledge of computer science,
• satisfy a depth requirement, consisting of an oral qualifying exam presentation with a written report, to ensure the student's ability to do research,
• submit a written thesis proposal and make an oral presentation about the proposal,
• write a Ph.D. thesis that must be approved by a dissertation guidance committee and present an oral thesis defense, and
• satisfy all School of Engineering requirements for the Ph.D. degree, as described in the NYU Tandon School of Engineering bulletin, including graduate study duration, credit points, GPA, and time-to-degree requirements.

Upon entering the program, each student will be assigned an advisor who will guide them in formulating an individual study plan directing their course choice for the first two years. The department will hold an annual Ph.D. Student Assessment Meeting, in which all Ph.D. students will be formally reviewed.

Note: for pre-fall 2015 Ph.D. students, please see the pre-fall 2015 Ph.D. Curriculum.

Program Requirements

Details about Breadth and Depth Requirements, Thesis Proposal and Presentation, and Thesis Defense can be found in the NYU Bulletin.

Program Details

Each incoming Ph.D. student will be assigned to a research advisor, or to an interim advisor, who will provide academic advising until the student has a research advisor. The advisor will meet with the student when the student enters the program to guide the student in formulating an Individual Study Plan. The purpose of the plan is to guide the student’s course choice for the first two years in the program and to ensure that the student meets the breadth requirements. The plan may also specify additional courses to be taken by the student in order to acquire necessary background and expertise. Subsequent changes to the plan must be approved by the advisor.

Sample Plan of Study

In order to obtain a Ph.D. degree, a student must complete a minimum of 75 credits of graduate work beyond the BS degree, including at least 21 credits of dissertation. A Master of Science in Computer Science may be transferred as 30 credits without taking individual courses into consideration. Other graduate coursework in Computer Science may be transferred on a course-by-course basis. Graduate coursework in areas other than Computer Science can be transferred on a course-by-course basis with approval of the Ph.D. Committee (PHDC). The School of Engineering places some limits on the number and types of transfer credits that are available. Applications for transfer credits must be submitted for consideration before the end of the first semester of matriculation. 

All Ph.D. students will be formally reviewed each year in a Ph.D. Student Assessment Meeting. The review is conducted by the entire CSE faculty and includes at least the following items (in no particular order):

• All courses taken, grades received, and GPAs.
• Research productivity: publications, talks, software, systems, etc.
• Faculty input, especially from advisors and committee members.
• Student’s own input.
• Cumulative history of the student's progress.

As a result of the review, each student will be placed in one of the following two categories, by vote of the faculty:

• In Good Standing: The student has performed well in the previous semester and may continue in the Ph.D. program for one more year, assuming satisfactory academic progress is maintained.
• Not in Good Standing: The student has not performed sufficiently well in the previous year. The consequences of not being in good standing will vary, and may include being placed on probation, losing RA/GA/TA funding, or not being allowed to continue in the Ph.D. program.

Following the review, students will receive formal letters which will inform them of their standing. The letters may also make specific recommendations to the student as to what will be expected of them in the following year. A copy of each student’s letter will be placed in the student’s file.

Quick Links

• Graduate Admissions
• Frequently Asked Questions
• Pre-Fall 2015 Ph.D. Curriculum

Program Admissions Chair

Justin Cappos

Program director.

Rachel Greenstadt

Periwinkle Doerfler

Nektarios Tsoutsos

Doctoral Degree in Computer Science

Carnegie Mellon's Ph.D. in Computer Science is, above all, a research degree. When the faculty award a Ph.D., they certify that the student has a broad foundation and awareness of core concepts in computer science, has advanced the field by performing significant original research and has reported that work in a scholarly fashion.

When you begin our Ph.D. program, you’ll take the Introductory Course for Doctoral Students — an intense two week program that orients you to the department, introduces you to research and education topics our faculty are interested in, helps you find a faculty advisor and familiarizes you with Carnegie Mellon’s resources. Next, you’ll gain a broad understanding of fundamental research issues in major areas of computer science through coursework and original research. Finally, you’ll write and orally defend a thesis that guarantees you understand the area well enough to advance the state of knowledge in the field.

During the first two years of the program, you’ll gain the foundation of knowledge that will allow you to become an expert researcher in computer science, primarily by

Mastering a body of graduate material, achieved by passing 96 university units worth of graduate courses (equivalent to eight full-time courses).

Learning how to organize and begin to carry out original research, achieved by participating in directed research.

You will also serve as a teaching assistant, hone your writing and speaking skills and maintain your programming prowess. You’ll also receive periodic evaluation of your progress, and must make satisfactory progress to continue in the program.

Time Commitment:

As a Ph.D. student in computer science at CMU, you'll spend roughly five years acquiring a body of technical knowledge that includes a familiarity with the breadth of the field, as well as a deep understanding of your research area. From your second month in the program, you'll work closely with your faculty advisor, who is charged with guiding your education and monitoring your progress through the program. You'll take courses, teach and perform directed research — all to ensure that you leave Carnegie Mellon as an expert in your field. For a complete breakdown of our program requirements, read our Ph.D. Handbook .

Financial Information:

The Computer Science Department offers all Ph.D. students full financial support while they are in good academic standing in their respective programs. To learn more about Ph.D. funding, visit the SCS  Doctoral Programs  webpage.

Graduate Tuition: https://www.cmu.edu/sfs/tuition/graduate/scs.html

Student Fees: https://www.cmu.edu/sfs/tuition/fees/index.html

Carnegie Mellon Graduate Student Financial Aid: https://www.cmu.edu/sfs/financial-aid/graduate/index.html

• Bachelor's Programs
• Master's Programs
• Ph.D. in Computer Science
• Ph.D. in ACO
• Doctoral - How to Apply
• Degrees Conferred
• Other SCS Doctoral Programs
• Student Resources

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 .

• Values of Inclusion
• 2020 Antiracism Task Force
• 2022 DEI Report
• Research News

Department Life

• Listed by Recipient
• Listed by Category
• Oral History of Cornell CS
• CS 40th Anniversary Booklet
• ABC Book for Computer Science at Cornell by David Gries
• Books by Author
• Books Chronologically
• The 60's
• The 70's
• The 80's
• The 90's
• The 00's
• The 2010's
• Faculty Positions: Ithaca
• Faculty Positions: New York City
• Lecturer Position: Ithaca
• Post-doc Position: Ithaca
• Staff/Technical Positions
• Ugrad Course Staff
• Ithaca Info
• Internal info
• Graduation Information
• Cornell Learning Machines Seminar
• Student Colloquium
• Spring 2024 Colloquium
• Conway-Walker Lecture Series
• Salton 2023 Lecture Series
• Spring 2024 Artificial Intelligence Seminar
• Spring 2024 Robotics Seminar
• Spring 2024 Theory Seminar
• Big Red Hacks
• Cornell University - High School Programming Contests 2024
• Game Design Initiative
• CSMore: The Rising Sophomore Summer Program in Computer Science
• Explore CS Research
• ACSU Research Night
• Cornell Junior Theorists' Workshop 2023
• Researchers
• Ph.D. Students
• M.Eng. Students
• M.S. Students
• Ph.D. Alumni
• List of Courses
• Course and Room Roster
• CS Advanced Standing Exam
• Architecture
• Artificial Intelligence
• Computational Biology
• Database Systems
• Human Interaction
• Machine Learning
• Natural Language Processing
• Programming Languages
• Scientific Computing
• Software Engineering
• Systems and Networking
• Theory of Computing
• Contact Academic Advisor
• Your First CS Course
• Technical Electives
• CS with Other Majors/Areas
• Transfer Credits
• CS Honors Program
• CPT for International CS Undergrads
• Graduation Requirements
• Useful Forms
• Becoming a CS Major
• Requirements
• Game Design Minor
• Co-op Program
• Cornell Bowers CIS Undergraduate Research Experience (BURE)
• Independent Research (CS 4999)
• Student Groups
• UGrad Events
• Undergraduate Learning Center
• UGrad Course Staff Info
• The Review Process
• Early M.Eng Credit Approval
• Financial Aid
• Prerequisites
• The Application Process
• The Project
• Pre-approved Electives
• Degree Requirements
• The Course Enrollment Process
• Advising Tips
• Entrepreneurship
• Cornell Tech Programs
• Professional Development
• Contact MEng Office
• Career Success
• Applicant FAQ
• Computer Science Graduate Office Hours
• Exam Scheduling Guidelines
• Graduate TA Handbook
• MS Degree Checklist
• MS Student Financial Support
• Special Committee Selection
• Diversity and Inclusion
• Contact MS Office
• Ph.D. Applicant FAQ
• Graduate Housing
• Non-Degree Application Guidelines
• Ph. D. Visit Day
• Business Card Policy
• Cornell Tech
• Curricular Practical Training
• Fellowship Opportunities
• Field of Computer Science Ph.D. Student Handbook
• Field A Exam Summary Form
• Graduate School Forms
• Instructor / TA Application
• Ph.D. Requirements
• Ph.D. Student Financial Support
• Travel Funding Opportunities
• Travel Reimbursement Guide
• The Outside Minor Requirement
• CS Graduate Minor
• Outreach Opportunities
• Parental Accommodation Policy
• Special Masters
• Student Spotlights
• Contact PhD Office

Search form

Computer Science Ph.D. Program

You are here.

The Cornell Ph.D. program in computer science is consistently ranked among the top six departments in the country, with world-class research covering all of computer science. Our computer science program is distinguished by the excellence of the faculty, by a long tradition of pioneering research, and by the breadth of its Ph.D. program. Faculty and Ph.D. students are located both in Ithaca and in New York City at the Cornell Tech campus . The Field of Computer Science also includes faculty members from other departments (Electrical Engineering, Information Science, Applied Math, Mathematics, Operations Research and Industrial Engineering, Mechanical and Aerospace Engineering, Computational Biology, and Architecture) who can supervise a student's Ph.D. thesis research in computer science.

Over the past years we've increased our strength in areas such as artificial intelligence, computer graphics, systems, security, machine learning, and digital libraries, while maintaining our depth in traditional areas such as theory, programming languages and scientific computing.  You can find out more about our research here . 

The department provides an exceptionally open and friendly atmosphere that encourages the sharing of ideas across all areas. 

Cornell is located in the heart of the Finger Lakes region. This beautiful area provides many opportunities for recreational activities such as sailing, windsurfing, canoeing, kayaking, both downhill and cross-country skiing, ice skating, rock climbing, hiking, camping, and brewery/cider/wine-tasting. In fact, Cornell offers courses in all of these activities.

The Cornell Tech campus in New York City is located on Roosevelt Island.  Cornell Tech  is a graduate school conceived and implemented expressly to integrate the study of technology with business, law, and design. There are now over a half-dozen masters programs on offer as well as doctoral studies.

FAQ with more information about the two campuses .

Ph.D. Program Structure

Each year, about 30-40 new Ph.D. students join the department. During the first two semesters, students become familiar with the faculty members and their areas of research by taking graduate courses, attending research seminars, and participating in research projects. By the end of the first year, each student selects a specific area and forms a committee based on the student's research interests. This “Special Committee” of three or more faculty members will guide the student through to a Ph.D. dissertation. Ph.D. students that decide to work with a faculty member based at Cornell Tech typically move to New York City after a year in Ithaca.

The Field believes that certain areas are so fundamental to Computer Science that all students should be competent in them. Ph.D. candidates are expected to demonstrate competency in four areas of computer science at the high undergraduate level: theory, programming languages, systems, and artificial intelligence.

Each student then focuses on a specific topic of research and begins a preliminary investigation of that topic. The initial results are presented during a comprehensive oral evaluation, which is administered by the members of the student's Special Committee. The objective of this examination, usually taken in the third year, is to evaluate a student's ability to undertake original research at the Ph.D. level.

The final oral examination, a public defense of the dissertation, is taken before the Special Committee.

To encourage students to explore areas other than Computer Science, the department requires that students complete an outside minor. Cornell offers almost 90 fields from which a minor can be chosen. Some students elect to minor in related fields such as Applied Mathematics, Information Science, Electrical Engineering, or Operations Research. Others use this opportunity to pursue interests as diverse as Music, Theater, Psychology, Women's Studies, Philosophy, and Finance.

The computer science Ph.D. program complies with the requirements of the Cornell Graduate School , which include requirements on residency, minimum grades, examinations, and dissertation.

The Department also administers a very small 2-year Master of Science program (with thesis). Students in this program serve as teaching assistants and receive full tuition plus a stipend for their services.

Secondary Menu

Phd program, find your passion for research.

Duke Computer Science gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators and friends.

Write a research proposal the first year and finish the research the second under the supervision of the chosen advisor and committee; present the research results to the committee and peers. Many students turn their RIP work into a conference paper and travel to present it.

Course work requirements are written to support the department's research philosophy. Pass up to four of the required six courses in the first two years to give time and space for immersing oneself in the chosen area.

Years three through five continue as the students go deeper and deeper into a research area and their intellectual community broadens to include collaborators from around the world. Starting in year three, the advisor funds the student's work, usually through research grants. The Preliminary exam that year is the opportunity for the student to present their research to date, to share work done by others on the topic, and to get feedback and direction for the Ph.D. from the committee, other faculty, and peers.

Most Ph.D students defend in years five and six. While Duke and the department guarantee funding through the fifth year, advisors and the department work with students to continue support for work that takes longer.

Teaching is a vital part of the Ph.D. experience. Students are required to TA for two semesters, although faculty are ready to work with students who want more involvement. The Graduate School's Certificate in College Teaching offers coursework, peer review, and evaluation of a teaching portfolio for those who want to teach. In addition, the Department awards a Certificates of Distinction in Teaching for graduating PhD students who have demonstrated excellence in and commitment to teaching and mentoring.

• CS 50th Anniversary
• Computing Resources
• Event Archive
• Location & Directions
• AI for Social Good
• Computational Social Choice
• Computer Vision
• Machine Learning
• Natural Language Processing (NLP)
• Reinforcement Learning
• Search and Optimization
• Computational Biochemistry and Drug Design
• Computational Genomics
• Computational Imaging
• DNA and Molecular Computing
• Algorithmic Game Theory
• Social Choice
• Computational Journalism
• Broadening Participation in Computing
• CS1/CS2 Learning, Pedagogy, and Curricula
• Education Technology
• Practical and Ethical Approaches to Software and Computing
• Interdisciplinary Research in Data Science
• Security & Privacy
• Architecture
• Computer Networks
• Distributed Systems
• High Performance Computing
• Operating Systems
• Quantum Computing
• Approximation and Online Algorithms
• Coding and Information Theory
• Computational Complexity
• Geometric Computing
• Graph Algorithms
• Numerical Analysis
• Programming Languages
• Why Duke Computer Science?
• BS Concentration in Software Systems
• BS Concentration in Data Science
• BS Concentration in AI and Machine Learning
• BA Requirements
• Minors in Computer Science
• 4+1 Program for Duke Undergraduates
• IDM in Math + CS on Data Science
• IDM in Linguistics + CS
• IDM in Statistics + CS on Data Science
• IDM in Visual & Media Studies (VMS) + CS
• Graduation with Distinction
• Independent Study
• Identity in Computing Research
• CS+ Summer Program
• CS Related Student Organizations
• Undergraduate Teaching Assistant (UTA) Information
• Your Background
• Schedule a Visit
• All Prospective CS Undergrads
• Admitted or Declared 1st Majors
• First Course in CS
• Duties and Commitment
• Compensation
• Trinity Ambassadors
• Mentoring for CS Graduate Students
• MSEC Requirements
• Master's Options
• Financial Support
• MS Requirements
• Concurrent Master's for Non-CS PhDs
• Admission & Enrollment Statistics
• PhD Course Requirements
• Conference Travel
• Frequently Asked Questions
• Additional Graduate Student Resources
• Graduate Awards
• Undergraduate Courses
• Graduate Courses
• Spring 2024 Classes
• Fall 2023 Classes
• Spring 2023 Classes
• Course Substitutions for Majors & Minors
• Course Bulletin
• Course Registration Logistics
• Assisting Duke Students
• For Current Students
• Alumni Lectures - Spring 2024
• News - Alumni
• Primary Faculty
• Secondary Faculty
• Adjunct and Visiting Faculty
• Emeriti - In Memoriam
• Postdoctoral Fellows
• Ph.D. Program
• Masters in Computer Science
• Masters in Economics and Computation
• Affiliated Graduate Students

Find Info For

• Become a Student
• Current Students
• Research and Partnerships

Quick Links

• Graduate Program

Doctoral Program

• Graduate Admissions
• Application Steps and Process
• Requirements
• Useful Links
• Orientation
• GoBoiler Internship
• Master's Program
• Professional Master’s in Information and Cybersecurity
• Computational Science & Engineering
• Statistics-Computer Science Joint Masters
• Computational Life Sciences
• Financial Support
• Oral English Proficiency
• GTA Information
• RA Expectations
• Requesting Time Off
• Payroll Information
• Contact Information
• GTA Award Winners

The CS Policies/Procedures Manual is online and is incorporated in the CS Grad website. The website contains all current information on the CS policies/procedures, in addition to other helpful information and links. 

The Purdue Graduate School manual contains the minimum requirements, but CS policies may exceed the Grad School requirements and are considered the primary policy to follow in those situations.

The doctoral program is designed to prepare students for a career in computer science research. The program includes coursework to provide core computer science knowledge, coursework to provide knowledge in the intended area of research, and extensive research training and experience.

Invitation to participate:

Information Session on the CS Doctoral Requirements with the Graduate Study Chair

Thursday, September 14th, at 5:30 pm in LWSN 3102

The doctoral program requirements are:

• One research orientation course
• Ethics Training
• Two initial research courses
• Core course requirement
• Advisory Committee
• Area-specific requirements
• Research credits
• Preliminary Examination
• Annual Review

Graduation Candidacy Information

Changes in Requirements

Policies and Procedures Manual

Sample Ph.D. Timeline

Back to top

1. Research Orientation

The research orientation requirement consists of three parts: (a) the research orientation course, (b) the ethics training, and (c) the initial research courses.

a. Research Orientation Course

Students must, in their first year, take “ CS 59100 Research Seminar for First-Year Graduate Students ”.  This course introduces students to the research of CS faculty and includes lectures on how to conduct, present, and review research.

b. Ethics Training

Students must complete this multiple part training in the first year.

• Students must be present for the ethics lecture that is part of " CS 59100 Research Seminar for First-Year Graduate Students ".
• Go to  CITI Program: Responsible Conduct of Research .
• Register with “Purdue University” as your Organization Affiliation;
• Complete the  Course Responsible Conduct of Research Training – Faculty, Postdoctoral, and Graduate Course
• Forward the certificate of completion to the Graduate Office by email at  [email protected]
• participation  in discussions with colleagues on RCR topics related to their specific research programs (e.g., through group meetings, coursework, orientations, professional development activities, or other organized events.) OR
• participation/viewing panel discussions around topics identified as most relevant by the College of Science researchers. There will be a one hour event each spring semester to fulfill this. These will be announced by the Grad Office whenever available.
• Each student researcher is responsible for self-reporting their activities here: https://webapps.ecn.purdue.edu/VPR/RT/login

Further information on Responsible Conduct of Research

c. Initial Research Courses

Students must take two initial research courses by the end of their third semester.  Students take an initial research course by registering for at least 3 credits of “CS 69900, Research PhD Thesis”. To register for research, use the Scheduling Assistant in myPurdue. Only one initial research course can be taken per semester or per summer.  Each student must identify a faculty supervisor and work with that faculty supervisor to define and conduct a research project. At the end of each course, the student must write a report that is formally evaluated by the faculty supervisor. The two initial courses may be supervised by the same or by different faculty members .

Beginning PhD students (in first two years) doing research with a faculty member other than their initial advisor may discuss whether to formally change the advisor of record. If both the initial advisor and the proposed new initial advisor agree, an email to the grad office can be sent to request an update. Email confirmation from both advisors is needed before myCS can be updated. Students in their third year and beyond should have a plan of study approved identifying their permanent advisor. See Plan of study below for additional details. 

2. Core Course Requirement

Students must satisfy this requirement by the end of their fourth semester by passing one theory core course and one systems core course with an average grade of at least 3.5.

The theory core course must be chosen from the following set: {“CS 58000 Algorithm Design, Analysis, And Implementation”, “CS 58400 Theory of Computation and Computational Complexity”, "CS 58800 Randomized Algorithms"}.

The systems core course must be chosen from the following set: {“CS 50300 Operating Systems”, “CS 50500 Distributed Systems”, “CS 53600 Data Communication and Computer Networks”}.

For the purpose of this requirement, a grade of A+, A, A-, B+, B, and B- counts as 4.3, 4.0, 3.7, 3.3, 3.0, and 2.7, respectively, must be earned.

3. Plan of Study

Students must submit a draft plan of study by the end of the fifth week of their fifth semester (not including summer semesters), and are expected to revise it and to submit as final, as directed by the CS Graduate Office, by the end of classes that semester. The plan of study lists (a) the student’s advisory committee, and (b) the courses the student plans to use to fulfill the degree requirement. The draft of the plan of study is submitted electronically and must be approved by the student's advisory committee and by the CS Graduate Committee, see Instructions for Filing a Plan of Study .

a. Advisory Committee

The student must identify a Ph.D. research supervisor and then consult with the research supervisor to define an advisory committee. The advisory committee consists of

• the student’s research supervisor (a.k.a. “major professor”, or “advisor”), who serves as chair.
• two or more additional faculty members.
• a research supervisor who is not a CS faculty member may be approved as a co-chair along with a co-chair from CS.
• a majority of committee members must be CS faculty . Faculty from other Purdue West Lafayette departments may be approved to serve on the committee.
• committee members from outside Purdue West Lafayette may be approved, but they must be in addition to the required three committee members from Purdue West Lafayette.

The plan of study must include at least six graduate level CS courses and only CS graduate courses, with a grade point average (GPA) of at least 3.5. The six courses must be taught by a faculty member whose primary appointment is in the CS department. The courses must include the two courses used to satisfy the core course requirement. The remaining courses must be three-credit, level 50000 or 60000, non-individual CS courses. CS 50100, 50010, 50011 and certain CS 59000/69000/59200/59300 courses may not be used.

Students admitted to the doctoral program Fall 2017 or later may list at most one approved variable title CS 59000/69000/59200/59300 lecture course. Please check the Variable Title Courses page to determine if a course has been approved for inclusion on a PhD plan of study.

All courses included in the plan of study must have a student evaluation component, and they must be graded in the usual manner so they can be used to compute the GPA. In particular, courses graded on a pass/no pass or satisfactory/unsatisfactory basis cannot be included in the plan of study. A student receiving a grade lower than C- in a course on the plan of study will have to repeat or replace the course. If a course is repeated, only the last grade, even if lower, is used to compute all GPAs involving that course.

Courses taken as a graduate student from other institutions may be accepted with the approval of the student's advisory committee , the Graduate Committee, and the Graduate School.  The minimum acceptable grade for such courses is B- or the equivalent. Please refer to these  Instructions for Transfer of Courses (PDF).  Requests must be submitted to the CS grad office within the first six weeks of the fall or spring semester.

The courses on the plan of study cannot have been used to satisfy requirements for an undergraduate degree, nor can they cause the student's doctoral plan of study to include courses used for more than one master's degree.

4. Area-Specific Requirements

Students must satisfy any additional requirements specific to their area of research . Students must consult with their major professor to ascertain area-specific requirements.  Students are responsible for knowing and completing area-specific requirements by the assigned deadlines.

5. Research

Ph.D. research experience is planned, supervised, accumulated, and demonstrated by forming an advisory committee , by taking graduate level computer science courses , by conducting thesis research, by passing a preliminary examination, and by writing and defending a thesis.

a. Research Credits

The credits used to satisfy the Ph.D. degree credit requirement consist of (1) all credits for the courses that appear on the plan of study, and (2) all “CS 69900 Research Ph.D. Thesis” credit hours with a grade of S. At least 90 total credit hours are required. For example, if a plan of study lists 18 credits, an additional 72 research credits of CS 69900 with a grade of S are required.

At least one-third (i.e. 30) of the total credit hours used to satisfy the Ph.D. degree credit hour requirement must be earned while registered for doctoral study at Purdue West Lafayette.

b. Preliminary Examination

Students must pass a preliminary examination that tests competence in the student’s research area and readiness for research on a specific problem. The content of the examination is at the discretion of the examining committee. The examination may include a presentation by the student of papers relevant to a chosen research topic, an oral examination over advanced material on the student’s research topic, a presentation by the student of the student’s preliminary research results, or a proposal of thesis research.

The examining committee consists of the student's advisory committee , and of an additional member, who is not on the advisory committee, who is approved by the Graduate Committee.

The preliminary examination can be taken as soon as the plan of study is approved, and as late as two semesters before the semester in which the thesis defense is held. The student should consult with their advisory committee to decide when to take the preliminary examination (e.g. if a final exam is taken Fall 2021, the prelim exam would have needed to have happened Fall 2020). 

Please see the Procedure for Arranging a Preliminary Examination.

The thesis must present new results worthy of publication. At least two academic sessions of registration devoted to research and writing must elapse between the preliminary and final doctoral examinations. The student must defend the thesis publicly and to the satisfaction of the examining committee, which consists of the student's  advisory committee  and of one additional faculty member who represents an area outside that of the thesis, and who is approved by the graduate committee.

The thesis should be defended at the latest by the end of the fourth semester following the one in which the student passes the  preliminary examination .

Defense Procedure Instructions

Thesis Format

In preparing a PhD dissertation, please read the graduate school templates information at:  http://www.purdue.edu/gradschool/research/thesis/templates.html  and choose the LaTeX Template. For the review of the format, schedule a Formatting Consultation prior to your defense at  https://www.purdue.edu/gradschool/research/thesis/appointment.html  .

Thesis Deposit Process

6. Annual Review

Each doctoral students’ academic and research progress is evaluated annually by their major professor and the Graduate Committee.  Students receive written feedback and guidance to support progress.

The Ph.D. requirements described above apply to all students entering or re-entering the Department of Computer Science at West Lafayette ("the Department") as degree-seeking graduate students in the summer session of 2016 or later. Here is an archive of the 2013 , 2010 ,  2009 ,  2006 ,  2002  and  2001  Doctoral Program Requirements.

Students are governed by the degree requirements in effect when they enter the Department as degree-seeking students.  For students re-entering, the date of the most recent re-entry determines the degree requirements.  Students who wish to take advantage of subsequent changes may apply to the Graduate Committee to be governed by all degree requirements in effect at a specified subsequent time.  Choosing features from different sets of requirements is not permitted.

For information about the commencement ceremony, please visit www.purdue.edu/commencement .

In order to graduate, you must declare candidacy for the semester in which you intend to graduate by the designated deadline. You declare candidacy by using the Scheduling Assistant within myPurdue and registering for either CAND 99100, 99200, or 99300 (Form 23 is no longer used). 

If you are declaring candidacy for multiple degrees (both PhD and MS) within the same semester, please register for candidacy for one degree, and then contact [email protected],  to let them know information on the second degree. Candidacy will only show on your schedule for one degree, but we will work with the Registrar's Office and Grad School to make sure expectation for both degrees is recorded in their systems.

CAND 99100: This the candidacy to register for if you are currently taking any courses and/or research. Doctoral students must register for research in proportion to their efforts during each session, and must be registered for at least one credit of research in this semester. Research registration should be commensurate with actual research and writing efforts. (International students registering for candidacy and less than full-time, need to request approval for a Reduced Course Load from ISS; at least one credit if not funded or at least three credits if funded.)

Special candidate registration may be allowed for those students needing to only deposit (CAND 992) or defend/deposit (CAND 993). If allowed, please note:

• Early deadlines apply (you can find the deadline calendar on the Grad School website,   https://www.purdue.edu/gradschool , and select Academic Calendar).
• Students cannot be registered for any credits in this semester (research or coursework).
• Students MUST be registered in research the semester prior to enrolling in one of these candidate types (including summer if research (which includes writing/formatting thesis) was performed).
• Students may still hold an RA appointment (and TA appointment, if remaining for the full semester despite defending and/or depositing early).
• Candidates who register for this special registration and who do not meet the early deadline, will be switched by the Grad School to CAND 991 and required to register for credits.  If you’re funded or on Research in Absentia, you need to make sure you are funded for a minimum of 3 credits, so check your schedule if you miss the early deadline and notify  [email protected]  immediately to assist you with modifying the number of registered credits.

CAND 993  (Exam-Only Candidacy): Candidacy for those that ONLY need to defend AND deposit their thesis.  Please note that there is a fee to register in CAND 993.

PLEASE NOTE: Being registered as a candidate does not automatically register you for the commencement ceremony itself. If you plan to participate in commencement, you must respond by using the Commencement tab on myPurdue. It will be added to your myPurdue account after a specified date in the semester you have registered as a candidate.

Graduation Deadline Calendar: https://www.purdue.edu/gradschool/about/calendar/deadlines.html

Back to top  

Sample Ph.D timeline

Department of Computer Science, 305 N. University Street, West Lafayette, IN 47907

Phone: (765) 494-6010 • Fax: (765) 494-0739

Copyright © 2024 Purdue University | An equal access/equal opportunity university | Copyright Complaints

Trouble with this page? Disability-related accessibility issue ? Please contact the College of Science .

Email forwarding for @cs.stanford.edu is changing. Updates and details here . CS Commencement Ceremony June 16, 2024.  Learn More .

PhD Admissions

Main navigation.

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.

PhD Program

In many ways, the PhD program is the cornerstone of Computer Science at Boston University.  Our PhD students serve some of the most central roles of our department, from pursuing sponsored research together with supervising faculty members as Research Assistants, to serving as Teaching Fellows in support of our undergraduate and graduate curriculum.

Pursuing the PhD degree enables you to become an expert in a technical subfield of Computer Science and advance the state of the art by contributing original research in that discipline. Most PhD students also gain practical experience in the classroom, as well as, becoming a visible member of the research community by publishing research and delivering oral presentations at conferences and research seminars.

Upon completing your PhD degree, you will be able to set your own research direction, teach and advise students, and work at the forefront of cutting-edge research in academia or at an industrial laboratory.

Learning Outcomes

• Produce and defend original research in the field of Computer Science.
• Master broad knowledge of Computer Science across algorithms, software, systems, theory of computation, and in one of the areas of artificial intelligence, computer graphics, cryptography & security, and data science .
• Demonstrate in-depth knowledge of a particular subject area within Computer Science.
• Actively participate in the Computer Science research community, for example by attending academic conferences and submitting research results for publication in professional conferences and journals.
• Be able to effectively communicate the results of research.

We invite you to learn more about our program through the links below.

PhD Program Information

• Program Milestones
• Breadth Requirements
• Subject Exams
• Specimen Curriculum

Fellowships & Awards

• Computer Science Fellowship Opportunities
• Research Excellence Award
• Teaching Excellence Award
• Teaching Fellow Expectations

More Information

• PhD in Computer Science – Graduate School of Arts & Sciences (GRS) Bulletin
• Graduate School of the College of Arts and Sciences (GRS) PhD Requirements
• Graduation Calendar
• PhD Profile for Computer Science

Apply Today

To apply to the Ph.D. program, please fill out an online application .

Deadline: December 15 for Fall admission.

With questions about admissions, please contact us at [email protected] .

• Request Info
• Check Status

Computer Science PhD Program

Doctoral Program

This PhD program provides cutting-edge research experience and expertise in advanced computer science subjects, aiming at educating future leaders in academia and industry.

Start Your Bold Future

By submitting this form, I agree that UTSA may contact me by email, voice, pre-recorded message and/or text message using automated technology.

Please enable javascript in your browser

Why Pursue a Computer Science PhD

At UTSA, the Department of Computer Science offers comprehensive programs at both undergraduate and graduate levels, with approximately 1,761 undergraduates, 135+ master’s students and 65+ doctoral students. The PhD in Computer Science offers opportunities for students to do advanced research in many fields. The research activities and experimental facilities have been well-supported by various federal research and infrastructure grants.

Research Opportunities

The research in the Department of Computer Science is supported by more than $10M in active research grants and contracts from various external funding agencies. PhD students conduct innovative research in cooperation with and under the supervision of department faculty. Activities of research assistants include implementing prototype systems, developing and proving new theories, conducting experiments, attending international conferences, and publishing their results in scientific journals.

The department’s 21 faculty members conduct research in a variety of areas including algorithms, bioinformatics, computer and information security, computer architecture, computer networks, databases, high performance computing, parallel and distributed systems, programming languages and compilers, and software engineering among others.

• Admission Requirements

Application Deadlines

Funding opportunities, career options, admission & application requirements.

Applications are submitted through the UTSA Graduate Application . Please upload all required documents (listed below) on your UTSA Graduate Application. It is the applicant’s responsibility to ensure completion and submission of the application, a nonrefundable application fee, and all required supporting documents are on file with UTSA by the appropriate application deadline.

Applicants are encouraged to have their admission file completed as early as possible. All applications, required documents and letters of recommendation, if applicable, must be submitted by 5:00 PM U.S. Central Time on the day of the deadline. Deadlines are subject to change.

All full-time, admitted PhD students are welcomed with an appointment as either a Graduate Teaching Assistant (GTA) or Graduate Research Assistant (GRA). This is comprehensive support that comes with a tuition waiver, stipend, and health insurance. The stipend starts from $24,000 per year, and the total value of the financial support package is more than $42,000 yearly. The Computer Science PhD program is designed so that students who are admitted as GTAs find a doctoral advisor during their first year of study.

UTSA prepares you for future careers that are in demand. The possible careers below is data pulled by a third-party tool called Emsi, which pulls information from sources like the U.S. Bureau of Labor Statistics, U.S. Census Bureau, online job postings, other government databases and more to give you regional and national career outlook related to this academic program.

Earning a Master's Degree

While in a doctoral program, a student may earn a master’s degree provided the following conditions are satisfied:

• A student must be admitted to candidacy.
• A student is eligible to receive a master’s degree upon completion of University-wide requirements and any additional degree requirements specific to the program.
• The Doctoral Studies Committee, Department Chair, and the Graduate Associate Dean of the College must recommend students for the degree.
• The student must apply for graduation by the published deadline the semester prior to awarding the doctoral degree.
• All required coursework in the doctoral program at the time of admission to candidacy must have been taken within the previous six years.
• If the master’s degree requires a thesis, the degree cannot be awarded on the basis of the doctoral qualifying examination.
• Students will not be approved for an additional master’s degree in the same field in which an individual has previously received a master’s degree.

Course Offerings & Schedule

Faculty offices and research labs for Computer Science are located both on the Main Campus and Downtown Campus (San Pedro I building). Graduate courses are held in San Pedro I. Courses are scheduled in late afternoons and evenings, accommodating part-time student schedules very well.

Graduate Advisor of Record

Dakai Zhu, PhD

210-458-7453

• Skip to Content
• Skip to Main Navigation
• Skip to Search

IUPUI IUPUI IUPUI

• Undergraduate Majors
• Apply to the Accelerated Program
• Master's Degrees
• Doctoral Degrees & Minors
• Minors & Certificates
• General Education
• Artificial Intelligence
• Bioinformatics
• Computer Science
• Data Science
• Health Informatics
• Health Information Management
• Library & Information Science
• Informatics
• Media Arts and Science
• Study Abroad in Greece
• Study Abroad in Finland
• Micro-Credentials
• Freshman Applicants
• Returning Students
• Master's Degree
• Doctoral Program
• Graduate Certificates
• Change or Declare your Major
• Admitted Students
• Student Ambassadors
• Virtual Tour
• Undergraduate Webinars & Information Sessions
• Graduate Student Information Sessions
• Summer Camp
• Earn College Credit
• Biomedical Informatics Challenge
• Computer Science Challenge
• Incoming Undergraduate Scholarships
• Undergraduate Scholarships
• Graduate Scholarships
• Accelerated Program Cost & Aid
• Travel Funding
• Tuition Reduction
• Peer Advisors
• Forms & Policies
• Become a Student Leader
• Student Organizations
• Honors Program
• Laptop Requirements
• Equipment Checkout
• Luddy Knowledge Base
• Student Facility Access
• Biomedical Informatics B.S.
• Health Information Management B.S.
• Informatics B.S.
• Media Arts and Science B.S.
• Bioinformatics M.S.
• Health Informatics M.S.
• Applied Data Science M.S.
• Human-Computer Interaction M.S.
• Master of Library and Information Science
• Media Arts and Science M.S.
• Find a Job or Internship
• F-1 Students & Internships
• Library & Information Science Internships
• Internship Checklist
• Forage: Virtual Job Simulations
• Forage: Earn Credit
• Network with LinkedIn
• Big Interview
• Elevator Pitch
• Cover Letter
• Informational Interview
• Interviewing
• Technical Interviewing
• The Offer Process
• The Negotiation Process
• Freelance Work
• Grant Proposal Writing
• Schedule an Appointment
• Request a Career Services Presentation
• Featured Employer Days
• Resume Reviews
• Portfolio Reviews
• Presentations and Workshops
• Employer Career Fair Registration
• Research Centers & Labs
• Undergraduate Research
• Research Events
• Luddy Strategic Plan
• Meet Fred Luddy
• Faculty Openings
• Faculty Directory
• Staff Directory
• Media Requests
• Contact Admissions
• Request Undergraduate Information
• Request Graduate Information
• Get involved
• Advisory Boards
• Advisory Board
• Department Blog
• Strategic Plan
• Multimedia Stories
• Luddy Leads Blog
• Student Showcases
• LIS Industry Speaker Series

Luddy School of Informatics, Computing, and Engineering

• Alumni & Giving
• Departments
• News & Blog

Computer science is a rapidly evolving field. Position yourself at its cutting edge.

When you embark on a Ph.D. in Computer Science from IU’s Luddy School of Informatics, Computing, and Engineering in Indianapolis, you’ll be conducting research alongside expert faculty mentors. Develop new algorithms, design innovative systems, explore novel applications in your area of interest, and see your work presented at top-tier conferences and published in leading academic journals.

• Degrees & Courses

Computer Science Ph.D.

Challenging and rewarding.

The Ph.D. in Computer Science is ideal for those with solid academic backgrounds who are passionate about computer science and seek to make significant contributions to the field through research and innovation, including:

• Recent graduates with a bachelor’s degree in computer science who wish to pursue a career in research or academia.
• Professionals with a master’s degree in computer science or a related field seeking to advance their research skills and expertise to pursue a career in academia, industry, or government research.
• Researchers eager to develop projects in artificial intelligence, machine learning, computer security, or robotics.
• Those interested in pursuing academic careers who aspire to become professors or researchers in computer science.
• Industry or government researchers who want to gain expertise in specialized areas and enhance their skills to take on more advanced research roles.
• Future entrepreneurs seeking to start high-tech ventures requiring advanced knowledge and expertise in computer science research.

Well-positioned for success

Luddy Indianapolis offers the only Computer Science Ph.D. program based in Central Indiana. Anthem, Dow AgroSciences, Eli Lilly, Interactive Intelligence, and Salesforce contribute to the local employer demand for researchers with a Ph.D. in computer science. The job market for graduates with doctoral degrees in Computer Science is expected to grow. According to the U.S. Bureau of Labor Statistics, the employment of Computer and Information Research Scientists is projected to increase by 21% from 2021 to 2031, with a median pay of $131,490 per year in 2021.

Meet our faculty

Mohammad Al Hasan

Professor, Computer Science

Spyridon Bakas

Adjunct Associate Professor of Computer Science

Arjan Durresi

Shiaofen Fang

Assistant Professor, Computer Science

Professor, Computer Science, Data Science

Teaching Professor, Computer Science

Associate Professor and Program Director, Computer Science

Get your questions answered

Request information.

Contact our graduate admissions team and get your questions answered.

Meet our student ambassadors

Get to know our student ambassadors and find out what life at Luddy is like.

Information Sessions

Register for a virtual information session.

Ready to get started?

• Register for an info session
• Learn how to apply

Luddy School of Informatics, Computing, and Engineering resources and social media channels

• Schedule a Visit

Additional links and resources

• Degrees & Majors
• Scholarships

Happening at Luddy

• Pre-college Programs

Information For

• Current Students
• Faculty & Staff Intranet

Luddy Indianapolis

• Advisory Council
• Faculty Awards
• Working at UD CIS
• Diversity & Inclusion
• MS in Computer Science

PhD in Computer Science

• Bioinformatics MS/PhD
• 4+1 Bachelors/CS Masters Program
• Graduate Certificates
• Graduate Admissions
• Professional Development Awards
• Graduate Resources
• BA Computer Science
• BS Computer Science
• BS Information Systems
• Independent Study
• Undergraduate Research
• Undergraduate FAQs
• Undergraduate Resources
• Student Organizations
• Faculty Directory
• Additional Faculty
• Graduate Students
• Faculty and Staff Resources
• Artificial Intelligence and Machine Learning
• Bioinformatics and Health Informatics
• Computer Systems, Networks, and Security
• Computer Vision, Robotics, and Autonomous Systems
• Computing Foundations
• Human-Computer Interaction and Education
• Research Matrix
• Internships & Opportunities
• 2023 CIS Honors Day Awards
• 2022 CIS Honors Day Awards
• 2021 CIS Honors Day Awards
• 2020 CIS Honors Day Awards
• Grace Hopper Celebration
• Newsletters
• Undergraduate Newsletter
• Make a Gift
• Alumni Spotlight
• Alumni Connection

The PhD is designed to prepare students for academic careers and careers in government and industry research labs. Computer science is a vigorous and exciting field of research and study that continues to grow in importance.

Departmental research strengths include:

• Artificial Intelligence (machine learning, multiagent systems, planning and problem solving),
• Bioinformatics,
• Computational Theory (computational learning theory, design and analysis of algorithms, computability theory),
• Compiler Optimization and Compilation for Parallel Machines,
• Natural-Language Processing, (discourse and dialogue, generation, information extraction, summarization),
• Systems (parallel and distributed computing, grid and volunteer computing, algorithm and architecture design for massive parallelism),
• Networks (distributed computing, transport layer protocols, mobile and wireless networks, algorithm and architecture design for massive parallelism, networks management, security performance modeling, simulation),
• Graphics and Computer Vision,
• Rehabilitation Engineering (augmentative communication, speech recognition and enhancement),
• Software Engineering (program analysis and testing),
• Symbolic Mathematical Computation (algebraic algorithms, parallelization), and

The CIS graduate program provides a solid foundation in the fundamental areas of computer science and provides numerous advanced courses and seminars to acquaint the student with current computer science research.

Degree Requirements

In addition to satisfying the general requirements of the University, candidates for the Doctor of Philosophy degree must satisfy several departmental requirements. One objective of these requirements is to provide flexibility in designing an appropriate plan of study. The PhD is an individualistic degree. As soon as possible in the program, each candidate should find a faculty member to act as adviser and be in charge of the candidate’s research.

The candidate and advisor design a plan of study that satisfies the University and Department requirements. The Department requirements as listed below specify a minimum amount of necessary work. It is expected that additional course work will normally be required by the adviser. A minimum set of requirements provides a large degree of flexibility for each individual candidate.

A. Departmental General Requirements

• A minimum grade average of 3.0 is required in the graduate courses used to satisfy the degree requirements. The University also requires a minimum GPA of 3.0 in all graduate courses taken including any not used towards the degree requirements. Students are encouraged to explore graduate courses (600 level or higher) in other areas such as electrical engineering, mathematics, linguistics, statistics, and business and economics. Graduate courses outside of Computer and Information Sciences to be used towards meeting degree requirements require written approval of the Graduate Committee.
• Each semester all graduate students must explicitly register for CISC 890 – Colloquium and sign up and satisfactorily participate in one of the Department’s special research interest groups. One faculty member for each group will be responsible for overseeing satisfactory participation for each student on an individual basis (e.g., simply attending, giving a presentation) and will assign a pass/fail grade accordingly.

The Department requires the following:

• Each candidate must complete all requirements of a University of Delaware MS degree in Computer and Information Sciences. A candidate with a master’s degree in a related field (e.g., EE, Math) must put together a program that meets the CIS Graduate Committee’s approval. Using courses taken for the related graduate degree plus courses taken at Delaware, the candidate must satisfy the Computer Science course requirements for the MS degree, and show the equivalent of the 30 credit MS degree offered by the CIS Department.
• Each candidate is required to complete a minimum of 6 additional credits beyond the master’s degree. At least 3 of the 6 additional credits must be in 800-level CISC courses. The 6 additional credits do not include the following courses: CISC 666, CISC 866, CISC 868, CISC 969. Normally, in meeting the University’s requirement for a major area, a candidate will be required by the adviser to complete more than 6 credits. (Note that the University requires a candidate to complete 9 credits of CISC969 after admission to candidacy.)
• Research Ability . PhD candidates are strongly encouraged to get involved in research as early as possible in their program. As part of the process of finding an adviser, and as early as possible, candidates must demonstrate the potential to perform research. Demonstration may be in the form of independent study ( CISC 666 , CISC 866 ), research ( CISC 868 ), working as a research assistant, or writing an MS thesis.
• Preliminary Requirements . These requirements ensure that each Ph.D. candidate (1) has significant breadth of knowledge in core areas of computer science, and (2) has demonstrated the ability to perform research in a specific computer science area. The breadth requirement is met by taking 5 breadth courses, which may include the 4 breadth courses from the breadth requirement of the MS degree, and obtaining a minimum 3.5 GPA on these breadth courses. See Prelim Course Selection Process for detail. The research requirement is met by working with a committee of 2 CIS faculty members on a research project, culminating in a written report and presentation/oral exam. A pass or fail decision for the preliminary exam will be made by the faculty in a faculty meeting that will take place after the end of each semester. Candidates must fulfill the Preliminary Requirements within 2 years, counted from the date the student enters the graduate program. Candidates may request an extension in exceptional circumstances (such as serious illness or injury) subject to approval by the Faculty. The student will be dismissed from the Ph.D. program if the Preliminary Requirements are not satisfied within the allowed time period. ( further information )
• Advisory Committee . Each candidate, with the advice of the PhD advisor, needs to establish an advisory committee (usually following the successful completion of the preliminary exam). In accordance with the University requirements, the committee consists of 4-6 members nominated and approved by the CIS Department faculty. The committee chair is the candidate’s PhD advisor in charge of the candidate’s research and dissertation and must be a member of the CIS faculty. The candidate may have a co- advisor who must be a UD faculty, possibly from another department. A co-advisor is a member of the advisory committee. At least two members represent the area of proposed research. The committee must also include at least one member of the CIS faculty working outside the main area of the proposed research. At least one member must be from outside the CIS Department. The proposed advisory committee must be submitted to the Graduate Committee for approval. It must then be approved by the CIS faculty. In the above, CIS faculty means tenure-track faculty whose primary appointment is in the CIS Department or who have a joint appointment in CIS, but not including continuing track faculty, research faculty, affiliated faculty, visiting faculty, secondary faculty, or adjunct faculty.
• Qualifying Examination . Each candidate must pass a qualifying exam. The advisory committee prepares an examination (oral and/or written) testing a candidate’s knowledge in the area of proposed research. Part of the examination includes an oral presentation of a candidate’s proposed dissertation research. A student passes the qualifying exam as long as there is no more than one negative vote. Prior to taking the qualifying exam, candidates must submit a dissertation proposal and a written plan describing their background and research interests. The proposal and plan are submitted to the advisory committee and are considered as input to the qualifying examination. Copies of “Discussion on PhD Thesis Proposals in Computing Science” are available in the CIS Department Office. The qualifying exam is normally taken one year after passing the preliminary exam. During this year a student should actively investigate research possibilities and select a dissertation topic.
• Dissertation . Each candidate must complete a dissertation demonstrating results of original and significant research written in a scholarly and competent manner worthy of publication. Upon completion of the dissertation, a final oral public examination must be passed, consisting of a defense of the dissertation and a test of the mastery of a candidate’s research area. The final oral examination is directed and evaluated by the student’s advisory committee.
• Facility of Expression in English . As part of satisfying the University’s requirement that PhD graduates demonstrate an ability to orally express themselves clearly and forcefully, each candidate must present his or her research results in a departmental colloquium, or one of the Department’s special research interest groups within six months of the defense.
• Foreign Language . There is no foreign language requirement.

Graduate Recruitment Contacts

Li Liao Email: cis [email protected] Phone : 302-831-2783

Chiamesha Carey Graduate Academic Advisor II Email: [email protected] Phone : 302-831-4467

UD Graduate Admissions Email : [email protected] Phone : 302-831-2129

• JEE Main 2024
• MHT CET 2024
• JEE Advanced 2024
• BITSAT 2024
• View All Engineering Exams
• Colleges Accepting B.Tech Applications
• Top Engineering Colleges in India
• Engineering Colleges in India
• Engineering Colleges in Tamil Nadu
• Engineering Colleges Accepting JEE Main
• Top IITs in India
• Top NITs in India
• Top IIITs in India
• JEE Main College Predictor
• JEE Main Rank Predictor
• MHT CET College Predictor
• AP EAMCET College Predictor
• GATE College Predictor
• KCET College Predictor
• JEE Advanced College Predictor
• View All College Predictors
• JEE Main Question Paper
• JEE Main Cutoff
• JEE Main Answer Key
• SRMJEEE Result
• Download E-Books and Sample Papers
• Compare Colleges
• B.Tech College Applications
• JEE Advanced Registration
• MAH MBA CET Exam
• View All Management Exams

Colleges & Courses

• MBA College Admissions
• MBA Colleges in India
• Top IIMs Colleges in India
• Top Online MBA Colleges in India
• MBA Colleges Accepting XAT Score
• BBA Colleges in India
• XAT College Predictor 2024
• SNAP College Predictor
• NMAT College Predictor
• MAT College Predictor 2024
• CMAT College Predictor 2024
• CAT Percentile Predictor 2023
• CAT 2023 College Predictor
• CMAT 2024 Registration
• TS ICET 2024 Registration
• CMAT Exam Date 2024
• MAH MBA CET Cutoff 2024
• Download Helpful Ebooks
• List of Popular Branches
• QnA - Get answers to your doubts
• IIM Fees Structure
• AIIMS Nursing
• Top Medical Colleges in India
• Top Medical Colleges in India accepting NEET Score
• Medical Colleges accepting NEET
• List of Medical Colleges in India
• List of AIIMS Colleges In India
• Medical Colleges in Maharashtra
• Medical Colleges in India Accepting NEET PG
• NEET College Predictor
• NEET PG College Predictor
• NEET MDS College Predictor
• DNB CET College Predictor
• DNB PDCET College Predictor
• NEET Admit Card 2024
• NEET PG Application Form 2024
• NEET Cut off
• NEET Online Preparation
• Download Helpful E-books
• LSAT India 2024
• Colleges Accepting Admissions
• Top Law Colleges in India
• Law College Accepting CLAT Score
• List of Law Colleges in India
• Top Law Colleges in Delhi
• Top Law Collages in Indore
• Top Law Colleges in Chandigarh
• Top Law Collages in Lucknow

Predictors & E-Books

• CLAT College Predictor
• MHCET Law ( 5 Year L.L.B) College Predictor
• AILET College Predictor
• Sample Papers
• Compare Law Collages
• Careers360 Youtube Channel
• CLAT Syllabus 2025
• CLAT Previous Year Question Paper
• AIBE 18 Result 2023
• NID DAT Exam
• Pearl Academy Exam

Predictors & Articles

• NIFT College Predictor
• UCEED College Predictor
• NID DAT College Predictor
• NID DAT Syllabus 2025
• NID DAT 2025
• Design Colleges in India
• Fashion Design Colleges in India
• Top Interior Design Colleges in India
• Top Graphic Designing Colleges in India
• Fashion Design Colleges in Delhi
• Fashion Design Colleges in Mumbai
• Fashion Design Colleges in Bangalore
• Top Interior Design Colleges in Bangalore
• NIFT Result 2024
• NIFT Fees Structure
• NIFT Syllabus 2025
• Free Sample Papers
• Free Design E-books
• List of Branches
• Careers360 Youtube channel
• IPU CET BJMC
• JMI Mass Communication Entrance Exam
• IIMC Entrance Exam
• Media & Journalism colleges in Delhi
• Media & Journalism colleges in Bangalore
• Media & Journalism colleges in Mumbai
• List of Media & Journalism Colleges in India
• Free Ebooks
• CA Intermediate
• CA Foundation
• CS Executive
• CS Professional
• Difference between CA and CS
• Difference between CA and CMA
• CA Full form
• CMA Full form
• CS Full form
• CA Salary In India

Top Courses & Careers

• Bachelor of Commerce (B.Com)
• Master of Commerce (M.Com)
• Company Secretary
• Cost Accountant
• Charted Accountant
• Credit Manager
• Financial Advisor
• Top Commerce Colleges in India
• Top Government Commerce Colleges in India
• Top Private Commerce Colleges in India
• Top M.Com Colleges in Mumbai
• Top B.Com Colleges in India
• IT Colleges in Tamil Nadu
• IT Colleges in Uttar Pradesh
• MCA Colleges in India
• BCA Colleges in India

Quick Links

• Information Technology Courses
• Programming Courses
• Web Development Courses
• Data Analytics Courses
• Big Data Analytics Courses
• RUHS Pharmacy Admission Test
• Top Pharmacy Colleges in India
• Pharmacy Colleges in Pune
• Pharmacy Colleges in Mumbai
• Colleges Accepting GPAT Score
• Pharmacy Colleges in Lucknow
• List of Pharmacy Colleges in Nagpur
• GPAT Result
• GPAT 2024 Admit Card
• GPAT Question Papers
• NCHMCT JEE 2024
• Mah BHMCT CET
• Top Hotel Management Colleges in Delhi
• Top Hotel Management Colleges in Hyderabad
• Top Hotel Management Colleges in Mumbai
• Top Hotel Management Colleges in Tamil Nadu
• Top Hotel Management Colleges in Maharashtra
• B.Sc Hotel Management
• Hotel Management
• Diploma in Hotel Management and Catering Technology

Diploma Colleges

• Top Diploma Colleges in Maharashtra
• UPSC IAS 2024
• SSC CGL 2024
• IBPS RRB 2024
• Previous Year Sample Papers
• Free Competition E-books
• Sarkari Result
• QnA- Get your doubts answered
• UPSC Previous Year Sample Papers
• CTET Previous Year Sample Papers
• SBI Clerk Previous Year Sample Papers
• NDA Previous Year Sample Papers

Upcoming Events

• NDA Application Form 2024
• UPSC IAS Application Form 2024
• CDS Application Form 2024
• CTET Admit card 2024
• HP TET Result 2023
• SSC GD Constable Admit Card 2024
• UPTET Notification 2024
• SBI Clerk Result 2024

Other Exams

• SSC CHSL 2024
• UP PCS 2024
• UGC NET 2024
• RRB NTPC 2024
• IBPS PO 2024
• IBPS Clerk 2024
• IBPS SO 2024
• CBSE Class 10th
• CBSE Class 12th
• UP Board 10th
• UP Board 12th
• Bihar Board 10th
• Bihar Board 12th
• Top Schools in India
• Top Schools in Delhi
• Top Schools in Mumbai
• Top Schools in Chennai
• Top Schools in Hyderabad
• Top Schools in Kolkata
• Top Schools in Pune
• Top Schools in Bangalore

Products & Resources

• JEE Main Knockout April
• NCERT Notes
• NCERT Syllabus
• NCERT Books
• RD Sharma Solutions
• Navodaya Vidyalaya Admission 2024-25
• NCERT Solutions
• NCERT Solutions for Class 12
• NCERT Solutions for Class 11
• NCERT solutions for Class 10
• NCERT solutions for Class 9
• NCERT solutions for Class 8
• NCERT Solutions for Class 7
• Top University in USA
• Top University in Canada
• Top University in Ireland
• Top Universities in UK
• Top Universities in Australia
• Best MBA Colleges in Abroad
• Business Management Studies Colleges

Top Countries

• Study in USA
• Study in UK
• Study in Canada
• Study in Australia
• Study in Ireland
• Study in Germany
• Study in China
• Study in Europe

Student Visas

• Student Visa Canada
• Student Visa UK
• Student Visa USA
• Student Visa Australia
• Student Visa Germany
• Student Visa New Zealand
• Student Visa Ireland
• CUET PG 2024
• IGNOU B.Ed Admission 2024
• DU Admission 2024
• UP B.Ed JEE 2024
• LPU NEST 2024
• IIT JAM 2024
• IGNOU Online Admission 2024
• Universities in India
• Top Universities in India 2024
• Top Colleges in India
• Top Universities in Uttar Pradesh 2024
• Top Universities in Bihar
• Top Universities in Madhya Pradesh 2024
• Top Universities in Tamil Nadu 2024
• Central Universities in India
• CUET Exam City Intimation Slip 2024
• IGNOU Date Sheet
• CUET Mock Test 2024
• CUET Admit card 2024
• CUET PG Syllabus 2024
• CUET Participating Universities 2024
• CUET Previous Year Question Paper
• CUET Syllabus 2024 for Science Students
• E-Books and Sample Papers
• CUET Exam Pattern 2024
• CUET Exam Date 2024
• CUET Syllabus 2024
• IGNOU Exam Form 2024
• IGNOU Result
• CUET Courses List 2024

Engineering Preparation

• Knockout JEE Main 2024
• Test Series JEE Main 2024
• JEE Main 2024 Rank Booster

Medical Preparation

• Knockout NEET 2024
• Test Series NEET 2024
• Rank Booster NEET 2024

Online Courses

• JEE Main One Month Course
• NEET One Month Course
• IBSAT Free Mock Tests
• IIT JEE Foundation Course
• Knockout BITSAT 2024
• Career Guidance Tool

Top Streams

• IT & Software Certification Courses
• Engineering and Architecture Certification Courses
• Programming And Development Certification Courses
• Business and Management Certification Courses
• Marketing Certification Courses
• Health and Fitness Certification Courses
• Design Certification Courses

Specializations

• Digital Marketing Certification Courses
• Cyber Security Certification Courses
• Artificial Intelligence Certification Courses
• Business Analytics Certification Courses
• Data Science Certification Courses
• Cloud Computing Certification Courses
• Machine Learning Certification Courses
• View All Certification Courses
• UG Degree Courses
• PG Degree Courses
• Short Term Courses
• Free Courses
• Online Degrees and Diplomas
• Compare Courses

Top Providers

• Coursera Courses
• Udemy Courses
• Edx Courses
• Swayam Courses
• upGrad Courses
• Simplilearn Courses
• Great Learning Courses

PhD Computer Science: Course, Admission 2024, Eligibility, Syllabus, Fees, Career

PhD Computer Science is a three to a five-year-long full-time research degree programme that instructs students in computer science and associated fields. Algorithms, machine learning, bioinformatics, network administration protection, database management systems, data mining, distributed algorithms, and computer science topics are taught in the PhD Computer Science programme.

Highlights: Ph.D Computer Science

Eligibility criteria for ph.d computer science, admission process for ph.d computer science, top ph.d computer science entrance exams, cutoff for top colleges, skills required for ph.d computer science, syllabus for ph,d computer science, fee structure of ph.d computer science, scope of ph.d in computer science, career options after ph.d computer science, benefits of studying ph.d in computer science, salary after ph.d computer science, list of top 10 ph.d in computer science colleges in india with fees, top private ph.d computer science colleges in india with fees, top government ph.d computer science colleges in india with fees.

The Doctor of Philosophy, or PhD , is the highest level of an academic programme that can be achieved. The study of computers and computation, encompassing their conceptual and mathematical principles, hardware and software, and applications for processing data, is known as computer science.

Applicants wishing to pursue a Ph.D Computer Science course must satisfy some eligibility requirements. These eligibility requirements play a very important role in providing the admission to the students in Ph.D Computer Science course at their desired institute. The average salary of a Computer Science Engineer in India Rs. 17 LPA, the average salary varies depending on the company.

Several private as well as government institutes offer Ph.D Computer Science courses, candidates can check the admission details by either visiting the college physically or on the official website of the same. Students after completing the course can pursue various job roles such as Software Developer, Computer System Engineer, Software Engineer, and Web Developer.

Students wishing to appear for the Ph.D Computer Science course must meet certain eligibility requirements of the course before applying for the admissions. Eligibility criteria for the Ph.D Computer Science course may vary depending on the institute but the general eligibility requirements remain the same for most of the engineering institutes.

• Applicants must have a Master’s degree in the field of Computer Science or related discipline with valid aggregate marks as specified by the institute.
• Students must pass the entrance examinations. Some of the top entrance exams in the Ph.D Computer Science field are UGC NET and CSIR UGC NET exams.
• Students must meet the cut-off specified by the institute to be eligible for admissions to the Ph.D Computer Science course.

The admission criteria for admission to a Ph,D Computer Science course vary depending on the institute. The admission is based on the recent qualifying marks and master’s degree results, students get admitted to their desired institute. Many institutes prefer students who have already completed research projects and received recommendation letters.

• Candidates have to apply for PhD Computer Science admission at the higher education institution directly by visiting there, filling out the request form, and providing the required documents.
• Candidates can also apply by going to the college's website, filling out the online PhD Computer Science degree application, and mailing in the relevant documents.
• They can also apply directly to the UGC NET exam, and based on their rank they can choose the most suitable institute for them.

UGC NET: University Grants Commission National Eligibility Test popularly known as UGC NET is a national-level entrance examination for many courses related to lectureship and doctorate. The examination is conducted by the National Testing Agency (NTA) twice a year. The exam is conducted in various languages and is conducted in online mode.

• UGC NET Admit Card
• UGC NET Syllabus
• UGC NET Exam Pattern
• UGC NET Result
• UGC NET Cutoff
• UGC NET Preparation Tips
• UGC NET Eligibility
• UGC NET Application

CSIR NET : CSIR UGC Test for JRF and Eligibility for Lectureship or CSIR UGC NET is a national-level entrance examination administered by National Testing Agency (NTA) for admission to various courses in the Lectureship such as Life Sciences, Chemical Sciences, and Mathematical Sciences, the exam is conducted in Online mode in English and Hindi languages.

• CSIR UGC NET Cutoff
• CSIR UGC NET Eligibility
• CSIR UGC NET Application
• CSIR UGC NET Exam Pattern
• CSIR UGC NET Admit Card
• CSIR UGC NET Answer Key
• CSIR UGC NET Result
• CSIR UGC NET Date
• CSIR UGC NET Syllabus

A PhD Computer Science cut-off is a group of criteria for judging an applicant's merit or entrance exam score. Admission to the college is granted to those who score below the cut-off percentage, rank, or grades. The number of candidates and difficulty level of the entrance exam decide the PhD Computer Science cutoff for each college.

Those interested in applying for a PhD in Computer Science degree must have a diverse skill set in order to comprehend ideas and improve their academic experience. These skills are also necessary for future work and a successful career. Some of the skills required in the field of Ph.D Computer Science course are listed below:

• Interest and Aptitude for Computer and Technology
• Researching Skills and abilities
• Decision-Making skills
• Problem-Solving Skills
• Critical Thinking

The syllabus for a Ph.D Computer Science course depends on the institute, some of the important subjects included in the course curriculum of Ph.D in Computer Science are Research Methodology, Review of Literature and Computer Applications. In the table below, we have mentioned the syllabus of Ravenshaw University for their Ph.D Computer Science course.

• Paper- 1 Research Methodology and Computer Applications
• Paper- 2 Elective Courses (from Research Topics)
• Paper- 3 Research and Publication Ethics
• Paper- 4 Review of Literature

The fee structure of Ph.D in Computer Science depends on the institute, fee varies depending on the institute, type of the institute, and its location. Students can check the fee details on the website of the college or they can visit their preferred institute and know in detail about the course including the fee details of Ph.D Computer Science. The fee generally ranges from Rs. 83,690 to Rs. 2.11 Lakhs.

Computer science is one of the fastest-growing careers in the world today, because of advancements in technologies and the increasing use of automation. Individuals who can manage complicated networks and handle problems on the go are needed by a huge number of firms as they want to place their products and services into the digital arena.

After earning a Ph.D., you will have a plethora of job options. It varies on whether they want to pursue academics, perform in a corporation, or simply continue with existing research initiatives. Some of the career options in the field of Ph.D Computer Science are Teacher, Professor, Software Developer, and Web Developer.

Several Indian firms are looking for Ph.D Computer Science students; the graduates' career and work options are determined by their educational achievements, talents, and the job profile chosen by the institute. The following are some of the job opportunities for PhD Computer Science graduates.

Software Developer : A software developer is a person who is in charge of creating and maintaining software that is used for various purposes. During the development of software, a software developer handles requirements analysis, prototype development, product development, testing, and maintenance.

Computer Systems Engineer : A computer systems engineer is a person who is in charge of determining how technology is being used to meet the personal and professional needs of users by applying their knowledge of programming, computer science, and mathematical principles. The software and hardware programmes in a computer system are then modified, updated, installed, and evaluated by a computer system engineer.

Computer Hardware Engineer : A computer hardware engineer is a professional who inspects and reviews technical specifications, accuracy, and design conformity. A computer hardware engineer is in charge of conducting technical investigations into media material as well as designing and building equipment like servers, network circuits, electrical components, and microprocessors.

Web Developer : A web developer is a person who is in charge of creating a website from the ground up. A web developer creates a website using several programming languages and platforms for a variety of purposes, including instructional websites, e-commerce, online interaction platforms, social networking, and more. Django, HTML, CSS, and JAVA are some of the most popular web development languages.

Top Recruiters:

A Ph.D in Computer Science degree provides the students with the required knowledge and expertise to make their careers in the field of Computer Science or Academics. Students can work in companies and understand the practical applications of Computer Science which will be very useful in their careers as a professor or a lecturer.

Students who have received the doctorate degree in the field of Computer Science must know the salaries associated with the in-demand career options. The salary can vary depending on the location of the company, job profile applied by the candidate, and the skills and expertise of the graduates. Mentioned in the following table are the popular career options along with their salaries.

Source: AmbitionBox

Several educational institutions throughout India offer PhD programmes in computer science and in various different specialisations, entrance to these institutes is based on prior academic achievements and admission exam score. The following is a list of India's top private and government institutes offering Ph.D in Computer Science courses.

PhD Computer Science programmes are available at a number of private academic universities and colleges across India. The admission to these institutes is based on prior academic achievements as well as the marks scored in the entrance examination. In the following table we have listed some of the top private Ph.D Computer Science colleges.

Many government institutes in India offer Ph.D Computer Science courses, government institutes are slightly less expensive than a private institute and often charge affordable fees while providing quality education. In the table below we have mentioned the top government institutes offering Ph.D. in Computer Science degree programmes in India:

Ph.D in Computer Science is an excellent course for those who want to learn Computer Science at an advanced level. There are many career options available after graduating with a Ph.D in Computer Science course such as Web Developer, Software Developer, Computer Software Engineer, and Computer Hardware Engineer.

Frequently Asked Question (FAQs)

For most students, pursuing the field of Computer Science is a challenging and time-consuming task. Most students, however, can acquire the discipline and pursue good careers in Computer Science fields if they are prepared to invest the time and learn significant time management skills.

Studying Computer Programming or Computer Science necessitates a significant amount of arithmetic, which is not required in the domain.

Depending on the type of institution, a PhD Computer Science programme costs between Rs. 83,690 to Rs. 2.11 Lakhs.

For a PhD in Computer Science, students must get a master's degree in Computer Science from a recognised university.

For PhD Computer Science, students must complete a 10+2 exam from a recognised board.

The PhD Computer Science is a three to five-year-long programme.

To gain knowledge and improve their learning experience, students need a wide range of abilities. Computer and Technology Knowledge, Communication Skills, Data Analysis, Creativity, Software Development, Technical Skills are only a few examples.

Several colleges offer Ph.D Computer Science courses such as GITAM Institute of Technology, VIT Pune, Christ University Bangalore, NIMS University, IIT Bombay, IIT Delhi, IISc Bangalore, and NIT Goa.

There are various career options after a PhD Computer Science degree. Some of the careers are Teacher, Professor, Software Developer, Computer Systems Engineer, Computer Hardware Engineer, Web Developer.

Some of the top organizations that hire PhD Computer Science graduates include Google, Amazon, Flipkart, Netflix, Facebook, TCS, Infosys, Accenture, Cognizant, Microsoft, IBM, Oracle, Cisco, Tech Mahindra, Snapdeal, Delhi University, Jamia Millia, Islamia University, Jadavpur University, Kolkata, Banaras Hindu University, Varanasi, Jawaharlal Nehru University.

• Latest Articles
• Popular Articles

Upcoming Exams

Chandigarh university common entrance test.

Application Date : 20 October,2023 - 14 May,2024

NMIMS Programs After Twelfth

Application Date : 06 December,2023 - 20 May,2024

Banasthali Vidyapith Aptitude Test

Application Date : 06 February,2024 - 15 May,2024

Telangana Integrated Common Entrance Test

Application Date : 07 March,2024 - 07 May,2024

NMIMS Common Entrance Test

Application Date : 11 March,2024 - 20 May,2024

Popular Courses and Specializations

Popular degrees, popular branches.

• Computer Science Engineering

Popular Courses

• MBA in Banking & Finance Management Course: Eligibility, Fees, Admission, Syllabus, Subjects, Scope
• MBA in International Business Course, Admissions, Eligibility, Syllabus, Fees, Career
• MBA in Logistics and Supply Chain Management Course, Admission, Colleges, Fees, Syllabus
• MBA in Digital Marketing: Course, Admission 2024, Fees, Syllabus, Entrance Exams, Colleges, Scope
• MBA IT-Systems Management Course, Subjects, Exams, Admission, Colleges, Fees, Career

Download Careers360 App's

Regular exam updates, QnA, Predictors, College Applications & E-books now on your Mobile

Certifications

We Appeared in

Best Computer Science Schools

Ranked in 2024, part of Best Science Schools

Earning a graduate degree in computer science can lead

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

• Clear Filters

Graduate Courses

(NOTE:- Fall 2023 schedule is tentative and subject to change)

Graduate Information Systems Courses

Ms project courses and basic research experience, proficiency courses, advanced courses, special topics courses, ph.d teaching and research experience.

National Center for Science and Engineering Statistics

• All previous cycle years

The NSCG is a biennial survey that provides data on the characteristics of the nation's college graduates, with a focus on those in the science and engineering workforce.

Survey Info

• tag for use when URL is provided --> Methodology
• tag for use when URL is provided --> Data
• tag for use when URL is provided --> Analysis

The NSCG is a unique source for examining the relationship of degree field and occupation in addition to other characteristics of college-educated individuals, including work activities, salary, and demographic information.

Areas of Interest

• Science and Engineering Workforce
• STEM Education

Survey Administration

This survey was conducted by the Census Bureau in partnership with the National Center for Science and Engineering Statistics within the National Science Foundation.

Survey Details

• Survey Description (PDF 123 KB)
• Data Tables (PDF 2.1 MB)

Featured Survey Analysis

Effects of the COVID-19 Pandemic on Employment, Earnings, and Professional Engagement: New Insights from the 2021 National Survey of College Graduates

NSCG Overview

Data highlights, the share of u.s. college graduates employed full time trended downward between 2015 and 2021..

Unemployment increased across all levels of education between 2019 and 2021.

Methodology

Survey description, survey overview (2021 survey cycle).

The National Survey of College Graduates (NSCG)—sponsored by the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF)—provides data on the characteristics of the nation’s college graduates, with a focus on those in the science and engineering workforce. It samples individuals who are living in the United States during the survey reference week, have at least a bachelor’s degree, and are younger than 76. By surveying college graduates in all academic disciplines, the NSCG provides data useful in understanding the relationship between college education and career opportunities, as well as the relationship between degree field and occupation.

Data collection authority

The information collected in the NSCG is solicited under the authority of the NSF Act of 1950, as amended, and the America COMPETES Reauthorization Act of 2010. The Census Bureau collects the NSCG data under the authority of Title 13, Section 8 of the United States Code. The Office of Management and Budget control number is 3145-0141.

Major changes to recent survey cycle

The 2021 NSCG data collection instrument included new questions to gauge the effects of the coronavirus pandemic on employment, specifically on labor force status, number of hours worked per week, salary, benefits, telecommuting options, and total earned income.

Key Survey Information

Initial survey year, reference period.

The week of 1 February 2021.

Response unit

Individuals with at least a bachelor’s degree.

Sample or census

Population size.

Approximately 68.6 million individuals.

Sample size

Approximately 164,000 individuals.

Key variables

Key variables of interest are listed below.

• Demographics (e.g., age, race, sex, ethnicity, and citizenship)
• Educational history
• Employment status
• Field of degree

Survey Design

Target population.

The NSCG target population includes individuals who meet the following criteria:

• Earned a bachelor’s degree or higher prior to 1 January 2020,
• Are not institutionalized and reside in the United States or Puerto Rico as of 1 February 2021, and
• Are younger than 76 years as of 1 February 2021.

Sampling frame

The 2021 NSCG retains the four-panel rotating panel design that began with the 2010 NSCG. As part of this design, every new panel receives a baseline survey interview and three biennial follow-up interviews before rotating out of the survey.

The 2021 NSCG includes approximately 164,000 sample cases drawn from the following:

• Returning sample from the 2019 NSCG who were originally selected from the 2013 American Community Survey (ACS)
• Returning sample from the 2019 NSCG who were originally selected from the 2015 ACS
• Returning sample from the 2019 NSCG who were originally selected from the 2017 ACS
• New sample selected from the 2019 ACS

Approximately 90,000 cases were selected from the returning sample members for one of the three biennial follow-up interviews that are part of the rotating panel design. For the baseline survey interview, about 74,000 new sample cases were selected from the 2019 ACS.

Sample design

The NSCG uses a stratified sampling design to select its sample from the eligible sampling frame. Within the sampling strata, the NSCG uses probability proportional to size or systematic random sampling techniques to select the NSCG sample. The sampling strata were defined by the cross-classification of the following four variables:

• Young graduate oversample group eligibility indicator (2 levels)
• Demographic group (9 levels)
• Highest degree type (3 levels)
• Detailed occupation group (25 levels)

As has been the case since the 2013 NSCG, the 2021 NSCG includes an oversample of young graduates to improve the precision of estimates for this important population.

Data Collection and Processing

Data collection.

The NSCG uses a trimodal data collection approach: Web survey, mail survey, and computer-assisted telephone interview (CATI). The 2021 NSCG data collection effort lasted approximately 7 months.

Data processing

The data collected in the NSCG are subject to both editing and imputation procedures. The NSCG uses both logical imputation and statistical (hot deck) imputation as part of the data processing effort.

Estimation techniques

Because the NSCG is based on a complex sampling design and subject to nonresponse bias, sampling weights were created for each respondent to support unbiased population estimates. The final analysis weights account for several factors, including the following:

• Adjustments to account for undercoverage of recent immigrants and undercoverage of recent degree-earners
• Adjustment for incorrect names or incomplete address information on the sampling frame
• Differential sampling rates
• Adjustments to account for non-locatability and unit nonresponse
• Adjustments to align the sample distribution with population controls
• Trimming of extreme weights
• Overlap procedures to convert weights that reflect the population of each individual frame (2013 ACS, 2015 ACS, 2017 ACS, and 2019 ACS) into a final sample weight that reflects the 2021 NSCG target population.

The final sample weights enable data users to derive survey-based estimates of the NSCG target population.

Survey Quality Measures

Sampling error.

Estimates of sampling errors associated with this survey were calculated using the successive difference replication method. Please contact the NSCG Survey Manager to obtain the replicate weights.

Coverage error

Any missed housing units or missed individuals within sample households in the ACS would create undercoverage in the NSCG. Additional undercoverage errors may exist because of self-reporting errors in the NSCG sampling frame that led to incorrect classification of individuals as not having a bachelor’s degree or higher when in fact they held such a degree.

Nonresponse error

The weighted response rate for the 2021 NSCG was 65%. Analyses of NSCG nonresponse trends were used to develop nonresponse weighting adjustments to minimize the potential for nonresponse bias in the NSCG estimates. A hot deck imputation method was used to compensate for item nonresponse.

Measurement error

The NSCG is subject to reporting errors from differences in interpretation of questions and by modality (Web, mail, or CATI). To reduce measurement errors, the NSCG questionnaire items were pretested in focus groups and cognitive interviews.

Data Availability and Comparability

Data availability.

Data from 1993 to the present are available at the NSCG Web page .

Data comparability

Year-to-year comparisons can be made among the 1993 to 2021 NSCG survey cycles because many of the core questions remained the same. Small but notable differences exist across some survey years, such as the collection of occupation and education data based on more recent taxonomies. Also, because of the use of different reference months in some survey cycles, seasonal differences may occur when making comparisons across years.

There is overlap in the cases included in the 2010 NSCG through the 2017 NSCG, in the 2013 NSCG through the 2019 NSCG, and in the 2015 NSCG through the 2021 NSCG. This sample overlap consists of cases that originated in the 2013 ACS, 2015 ACS, 2017 ACS, or 2019 ACS. The overlap among cases allows for the ability to conduct longitudinal analysis of this subset of the NSCG sample. To reduce the risk of disclosure, longitudinal analyses can be conducted only within a restricted environment. See the NCSES Restricted-Use Data Licensing and Procedures page to learn more.

Data Products

Publications.

Data from the NSCG are published in NCSES InfoBriefs and data tables, available at https://www.nsf.gov/statistics/srvygrads/ .

Information from this survey is also included in Science and Engineering Indicators and Women, Minorities, and Persons with Disabilities in Science and Engineering .

Electronic access

The NSCG public use data through 2021 are available in the SESTAT data tool and in downloadable files through the NCSES data page . Data from 1993 to 2019 (2021 forthcoming) are also available in the new NCSES interactive data tool . The NSCG restricted use data are available through the Census Bureau’s Federal Statistical Research Data Centers .

Technical Notes

Survey overview.

Purpose. The National Survey of College Graduates (NSCG) provides data on the characteristics of the nation’s college graduates, with a focus on those in the science and engineering (S&E) workforce. It samples individuals who are living in the United States during the survey reference week, have earned at least a bachelor’s degree, and are younger than 76. By surveying college graduates in all academic disciplines, the NSCG provides data useful in understanding the relationship between college education and career opportunities, as well as the relationship between degree field and occupation.

The NSCG is designed to provide demographic, education, and career history information about college graduates and to complement another survey conducted by the National Center for Science and Engineering Statistics (NCSES): the Survey of Doctorate Recipients (SDR, https://www.nsf.gov/statistics/srvydoctoratework/ ). These two surveys share a common reference date, and they use similar questionnaires and data processing guidelines.

These technical notes provide an overview of the 2021 NSCG. Complete details are provided in the 2021 NSCG Methodology Report, available upon request from the NSCG Survey Manager.

Data collection authority. The information collected in the NSCG is solicited under the authority of the National Science Foundation Act of 1950, as amended, and the America COMPETES Reauthorization Act of 2010. The Census Bureau collects the NSCG data, on behalf of NCSES, under the authority of Title 13, Section 8 of the United States Code. The Office of Management and Budget control number is 3145-0141.

Survey contractor. Census Bureau.

Survey sponsor. NCSES.

Frequency. Biennial.

Initial survey year. 1993.

Reference period. The week of 1 February 2021.

Response unit. Individual.

Sample or census. Sample.

Population size. Approximately 68.6 million individuals.

Sample size. Approximately 164,000 individuals.

Target population. The NSCG target population includes individuals who meet the following criteria:

• Earned a bachelor’s degree ​ Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. Bachelor’s degrees include equivalent undergraduate academic degrees awarded by colleges and universities in countries that may name their degrees differently. or higher prior to 1 January 2020
• Are not institutionalized and reside in the United States or Puerto Rico as of 1 February 2021
• Are younger than 76 years as of 1 February 2021

Sampling frame . Using a rotating panel design, the 2021 NSCG includes new sample cases from the 2019 American Community Survey (ACS) and returning sample cases from the 2019 NSCG.

The NSCG sampling frame for new sample cases included the following eligibility requirements:

• Were residing in the United States or Puerto Rico as of the ACS interview date
• Were noninstitutionalized as of the ACS interview date
• Had earned at least a bachelor’s degree as of the ACS interview date
• Would be under the age of 76 as of 1 February 2021
• Did not have an inaccurate name or incomplete address on the ACS data file

Returning sample cases from the 2019 NSCG originated from three different frames (the 2013 ACS, 2015 ACS, and 2017 ACS) and had the following eligibility requirements:

• Were a complete interview or temporarily ineligible during their initial NSCG survey cycle
• During the 2019 NSCG survey cycle, did not refuse to participate and request to be excluded from future NSCG cycles

Sample design . The NSCG sample design is cross-sectional with a rotating panel element. As a cross-sectional study, the NSCG provides estimates of the size and characteristics of the college graduate population for a point in time. As part of the rotating panel design, every new panel receives a baseline survey interview and three biennial follow-up interviews before rotating out of the survey.

The NSCG uses a stratified sampling design to select its sample from the eligible sampling frame. In the new sample, cases were selected using systematic probability proportional to size (PPS) sampling. ​ With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. With PPS sampling, the probability of selection was proportional to the ACS final person-level weight, adjusted to account for imputed educational attainment, incomplete addresses, or invalid names. Among the returning sample, all eligible cases were selected. The sampling strata were defined by the cross-classification of the following four variables:

As has been the case since the 2013 NSCG, the 2021 NSCG includes an oversample of young graduates to improve the precision of estimates for this important population. The 2021 NSCG includes approximately 164,000 sample cases drawn from the following:

• Returning sample from the 2019 NSCG who were originally selected from the 2013 ACS

Data Collection and Processing Methods

Data collection . The data collection period lasted approximately 7 months (8 April 2021 to 1 November 2021). The NSCG used a trimodal data collection approach: self-administered online survey (Web), self-administered paper questionnaire (via mail), and computer-assisted telephone interview (CATI). Individuals in the sample generally were started in the Web mode, depending on their available contact information and past preference. After an initial survey invitation, the data collection protocol included sequential contacts by postal mail, e-mail, and telephone that ran throughout the data collection period. At any time during data collection, sample members could choose to complete the survey using any of the three modes. Nonrespondents to the initial survey invitation received follow-up contacts via alternate modes.

Quality assurance procedures were in place at each data collection step (e.g., address updating, printing, package assembly and mailing, questionnaire receipt, data entry, CATI, coding, and post-data collection processing).

Mode . About 89% of the participants completed the survey by Web, 7% by mail, and 4% by CATI.

Response r ates . Response rates were calculated on complete responses, that is, from instruments with responses to all critical items. Critical items are those containing information needed to report labor force participation (including employment status, job title, and job description), college education (including degree type, degree date, and field of study), and location of residency on the reference date. The overall unweighted response rate was 67%; the weighted response rate was 65%. Of the roughly 164,000 persons in the 2021 NSCG sample, 106,279 completed the survey.

Data e diting. Response data had initial editing rules applied relative to the specific mode of capture to check internal consistency and valid range of response. The Web survey captured most of the survey responses and had internal editing controls where appropriate. A computer-assisted data entry (CADE) system was used to process the mailed paper forms. Responses from the three separate modes were merged for subsequent coding, editing, and cleaning necessary to create an analytical database.

Following established NCSES guidelines for coding NSCG survey data, including verbatim responses, staff were trained in conducting a standardized review and coding of occupation and education information, certifications, “other/specify” verbatim responses, state and country geographical information, and postsecondary institution information. For standardized coding of occupation (including auto-coding), the respondent's reported job title, duties and responsibilities, and other work-related information from the questionnaire were reviewed by specially trained coders who corrected respondents’ self-reporting errors to obtain the best occupation codes. For standardized coding of field of study associated with any reported degree (including auto-coding), the respondent’s reported department, degree level, and field of study information from the questionnaire were reviewed by specially trained coders who corrected respondents’ self-reporting errors to obtain the best field of study codes.

Imputation. Logical imputation was primarily accomplished as part of editing. In the editing phase, the answer to a question with missing data was sometimes determined by the answer to another question. In some circumstances, editing procedures found inconsistent data that were blanked out and therefore subject to statistical imputation.

The item nonresponse rates reflect data missing after logical imputation or editing but before statistical imputation. For key employment items—such as employment status, sector of employment, and primary work activity—the item nonresponse rates ranged from 0.0% to 1.1%. Nonresponse to questions deemed sensitive was higher: nonresponse to salary and earned income was 5.4% and 7.8%, respectively, for the new sample members and 4.7% and 6.8%, respectively, for the returning members. Personal demographic data of the new sample members had variable item nonresponse rates, with sex at 0.00%, birth year at 0.04%, marital status at 0.6%, citizenship at 0.4%, ethnicity at 1.4%, and race at 3.1%. The nonresponse rates for returning sample members were 0.8% for marital status and 0.7% for citizenship.

Item nonresponse was typically addressed using statistical imputation methods. Most NSCG variables were subjected to hot-deck imputation, with each variable having its own class and sort variables chosen by regression modeling to identify nearest neighbors for imputed information. For some variables, there was no set of class and sort variables that was reliably related to or suitable for predicting the missing value, such as day of birth. In these instances, random imputation was used, so that the distribution of imputed values was similar to the distribution of reported values without using class or sort variables.

Imputation was not performed on critical items or on verbatim-based variables. In addition, for some missing demographic information, the NSCG imported the corresponding data from the ACS, which had performed its own imputation.

Weighting. Because the NSCG is based on a complex sampling design and subject to nonresponse bias, sampling weights were created for each respondent to support unbiased population estimates. The final analysis weights account for several factors, including the following:

• Overlap procedures to convert weights that reflect the population of each individual frame (2013 ACS, 2015 ACS, 2017 ACS, and 2019 ACS) into a final sample weight that reflects the 2021 NSCG target population

The final sample weights enable data users to derive survey-based estimates of the NSCG target population. The variable name on the NSCG public use data files for the NSCG final sample weight is WTSURVY.

Variance estimation. The successive difference replication method (SDRM) was used to develop replicate weights for variance estimation. The theoretical basis for the SDRM is described in Wolter (1984) and in Fay and Train (1995). As with any replication method, successive difference replication involves constructing numerous subsamples (replicates) from the full sample and computing the statistic of interest for each replicate. The mean square error of the replicate estimates around their corresponding full sample estimate provides an estimate of the sampling variance of the statistic of interest. The 2021 NSCG produced 320 sets of replicate weights.

Disclosure protection. To protect against the disclosure of confidential information provided by NSCG respondents, the estimates presented in NSCG data tables are rounded to the nearest 1,000.

Data table cell values based on counts of respondents that fall below a predetermined threshold are deemed to be sensitive to potential disclosure, and the letter “D” indicates this type of suppression in a table cell.

Sampling error. NSCG estimates are subject to sampling errors. Estimates of sampling errors associated with this survey were calculated using replicate weights. Data table estimates with coefficients of variation (that is, the estimate divided by the standard error) that exceed a predetermined threshold are deemed unreliable and are suppressed. The letter “S” indicates this type of suppression in a table cell.

Coverage error. Coverage error occurs in sample estimates when the sampling frame does not accurately represent the target population and is a type of nonsampling error. Any missed housing units or missed individuals within sample households in the ACS would create undercoverage in the NSCG. Additional undercoverage errors may exist because of self-reporting errors in the NSCG sampling frame that led to incorrect classification of individuals as not having a bachelor's degree or higher when in fact they held such a degree.

Nonresponse error. The weighted response rate for the 2021 NSCG was 65%; the unweighted response rate was 67%. Analyses of NSCG nonresponse trends were used to develop nonresponse weighting adjustments to minimize the potential for nonresponse bias in the NSCG estimates. A hot deck imputation method was used to compensate for item nonresponse.

Measurement error. The NSCG is subject to reporting errors from differences in interpretation of questions and by modality (Web, mail, CATI). To reduce measurement errors, the NSCG questionnaire items were pretested in focus groups and cognitive interviews.

Data Comparability and Changes

Data comparability. Year-to-year comparisons of the nation’s college-educated population can be made among the 1993, 2003, 2010, 2013, 2015, 2017, 2019, and 2021 survey cycles because many of the core questions remained the same. Since the 1995, 1997, 1999, 2006, and 2008 surveys do not provide full coverage of the nation’s college-educated population, any comparison between these cycles and other cycles should be limited to those individuals educated or employed in S&E fields.

Small but notable differences exist across some survey cycles, however, such as the collection of occupation and education data based on more recent taxonomies. Also, because of the use of different reference months in some survey cycles, seasonal differences may occur when making comparisons across years. Thus, use caution when interpreting cross-cycle comparisons.

There is overlap in the cases included in the 2010 NSCG through the 2017 NSCG, in the 2013 NSCG through the 2019 NSCG, and in the 2015 NSCG through the 2021 NSCG (see figure 1 ). The overlap among cases allows for longitudinal analysis of a subset of the NSCG sample using restricted use data files within NCSES’ Secure Data Access Facility (SDAF). Cases can be linked across survey years using a unique identification variable and single-frame weights are available for each survey year, allowing for the evaluation of estimates from each frame independently. If you are interested in applying for a license to access restricted use NSCG data via the SDAF, please visit NCSES Restricted-Use Data Procedures Guide . Moreover, the Census Bureau offers NSCG restricted use data files that include a few additional data elements. These files can be accessed via the Federal Statistical Research Data Centers .

• Share on X/Twitter
• Share on Facebook
• Share on LinkedIn
• Send as Email

Rotating panel design and sample sizes for the National Survey of College Graduates: 2010–21

ACS = American Community Survey; NSCG = National Survey of College Graduates; NSRCG = National Survey of Recent College Graduates.

During a panel’s second survey cycle (in which it is part of the returning sample for the first time), its members include individuals who responded or who were temporarily ineligible during the first cycle. During a panel’s third and fourth cycles, its members include all respondents, nonrespondents, and temporarily ineligible cases from the preceding cycle. Beginning in 2013, the NSCG transitioned to a design that includes an oversample of young graduates to improve the precision of estimates for this important population.

National Center for Science and Engineering Statistics, National Science Foundation, National Survey of College Graduates.

Changes in survey coverage and population . None.

Changes in q uestio n naire

• 2021. To gauge the effects of the coronavirus pandemic on employment, the content of the NSCG questionnaire was modified for 2021 in two ways:
• The response options of long-standing items were revised to identify pandemic-related consequences: for example, reasons for not working, reasons for working part time, reasons for changing employment, and available job benefits.
• New items were added to understand the effects of the pandemic on salaries and earnings and to measure the prevalence of telework.
• 2019. The content of the 2019 NSCG questionnaire remained unchanged from the 2017 NSCG version.
• 2017. The 2017 NSCG questionnaire added two new questions about U.S. military veteran status that are asked on the ACS.
• 2015. The 2015 NSCG questionnaire added a section on professional certifications and licenses.
• 2013. The 2013 NSCG questionnaire added questions about attendance at community colleges, amounts borrowed to finance undergraduate and graduate degrees, and sources of financial support for undergraduate and graduate degrees. The 2013 questionnaire also differed from the 2010 questionnaire by splitting the first response category for the indicator of sample member location on the survey reference date into two categories. “United States, Puerto Rico, or another U.S. territory” became “United States or Puerto Rico” and “Another U.S. territory.”
• 2010. The 2010 NSCG questionnaire added items on components of job satisfaction, importance of job benefits, year of retirement, whether employer is a new business, and degree of difficulty concentrating, remembering, or making decisions.

Changes in reporting procedures or classification

• In past years, NSCG data were combined with data from the SDR and the NSRCG to form the Scientists and Engineers Statistical Data System (SESTAT). The last series of tables produced from SESTAT used 2013 NSCG data. Since then, NSCG data have been used in numerous tables for NCSES’s two congressionally mandated reports ( Science and Engineering Indicators and Women, Minorities, and Persons with Disabilities in Science and Engineering ).

Definitions

Field of degree. NSCG respondents are asked to report each degree they have earned at the bachelor’s level or higher, along with the major field of study for each degree. The 2021 NSCG used a taxonomy of 142 “detailed” fields of study from which respondents could select the field that best represented their major. These 142 “detailed” fields of study were aggregated into 31 “minor” fields, 7 “major” fields, and 3 “broad” fields (S&E, S&E-related, and non-S&E). (See technical table A-1 for a list and classification of fields of study reported in the NSCG.)

Full-time and part-time employment. Full-time (working 35 hours or more per week) and part-time (working less than 35 hours per week) employment status is for the principal job only and not for all jobs held in the labor force. For example, an individual who works part time in his or her principal job but full time in the labor force would be tabulated as part time.

Highest degree level. NSCG respondents report the degrees they have earned at the bachelor’s level (e.g., BS, BA, AB), master’s level (e.g., MS, MA, MBA), and doctorate level (e.g., PhD, DSc, EdD), as well as other professional degrees (e.g., JD, LLB, MD, DDS, DVM). Because the NSCG is focused on the S&E workforce, the sampling strategy does not include a special effort to collect professional degrees. As such, there is not always sufficient data for the professional degrees to be displayed separately in the tables.

Occupation data. The occupational classification of the respondent was based on his or her principal job (including job title) held during the reference week—or on his or her last job held, if not employed in the reference week (survey questions A5 and A6 as well as A16 and A17). Also used in the occupational classification was a respondent-selected job code (survey questions A7 and A18). (See technical table A-2 for a list and classification of occupations reported in the NSCG.)

Race and ethnicity. Ethnicity is defined as Hispanic or Latino or not Hispanic or Latino. Values for those selecting a single race include American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, and White. Those persons who report more than one race and who are not of Hispanic or Latino ethnicity also have a separate value.

Salary. Median annual salaries are reported for the principal job, rounded to the nearest $1,000, and computed for individuals employed full time. For individuals employed by educational institutions, no accommodation was made to convert academic year salaries to calendar year salaries.

Sector of employment. Employment sector is a derived variable based on responses to questionnaire items A13, A14, and A15. In the data tables, the category 4-year educational institution includes 4-year colleges or universities, medical schools (including university-affiliated hospitals or medical centers), and university-affiliated research institutes. Two-year and pre-college institutions include community colleges, technical institutes, and other educational institutions (which respondents reported verbatim in the survey questionnaire). For-profit business or industry includes respondents who were self-employed in an incorporated business. Self-employed includes respondents who were self-employed or were a business owner in a non-incorporated business.

Fay RE, Train GF. 1995. Aspects of Survey and Model-Based Postcensal Estimation of Income and Poverty Characteristics for States and Counties. American Statistical Association Pro cee dings of the S ec tion on Go ve rnm e nt Statisti c s , 154–59.

Wolter K. 1984. An Investigation of Some Estimators of Variance for Systematic Sampling. J ournal of the Am e ri c an Statisti c al Asso c iation 79(388):781–90.

Technical Tables

Questionnaires, view archived questionnaires, key data tables.

Recommended data tables

Fields of study of college graduates

Occupations of college graduates, college graduates over time, data tables, work activities and job satisfaction of employed college graduates, median salaries of full-time employed college graduates, demographic characteristics of college graduates, general notes.

The National Survey of College Graduates, conducted by the National Center for Science and Engineering Statistics within the National Science Foundation, is a repeated cross-sectional biennial survey that collects information on the nation’s college-educated workforce. This survey is a unique source for examining the relationship between degree field and occupation, as well as for examining other characteristics of college-educated individuals, including work activities, salary, and demographic information.

Acknowledgments and Suggested Citation

Acknowledgments.

Lynn Milan of the National Center for Science and Engineering Statistics (NCSES) developed and coordinated this report under the leadership of Emilda B. Rivers, NCSES Director; Vipin Arora, NCSES Deputy Director; and John Finamore, NCSES Chief Statistician. Jock Black (NCSES) reviewed the report.

The Census Bureau, under National Science Foundation interagency agreement number NCSE-2040211, collected and tabulated the data for the NSCG. The statistical data tables were compiled by Greg Orlofsky (Census) and verified by Nguyen Tu Tran (DMI). Data and publication processing support was provided by Devi Mishra, Christine Hamel, Tanya Gore, Joe Newman, and Rajinder Raut (NCSES).

NCSES thanks the college graduates who participated in the NSCG for their time and effort in generously contributing to the information included in this report.

Suggested Citation

National Center for Science and Engineering Statistics (NCSES). 2022. National Survey of College Graduates: 20 21 . NSF 23-306. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf23306/ .

Featured Analysis

Ncses data explorer, related content, related collections, survey contact.

For additional information about this survey or the methodology, contact

Get e-mail updates from NCSES

NCSES is an official statistical agency. Subscribe below to receive our latest news and announcements.

10 colleges and universities shaping the future of cybersecurity education

Institutions featured on this list often provide undergraduate and graduate degrees, courses, as well as certificate programs tailored to meet the growing demand for cybersecurity professionals in various industries.

Some notable colleges and universities renowned for their cybersecurity programs and courses include:

Carnegie Mellon University (USA)

Information Networking Institute (INI)

The Information Networking Institute (INI) at Carnegie Mellon University (CMU) educates and develops engineers through technical, interdisciplinary master’s degree programs in information networking, security and mobile and IoT engineering that incorporate business and policy perspectives.

Program : Master of Science in Information Security (MSIS)

Georgia Institute of Technology (USA)

Institute for Information Security & Privacy (IISP)

The Georgia Institute of Technology’s Institute for Information Security & Privacy (IISP) is a research institution dedicated to advancing cybersecurity and privacy technologies. Established within Georgia Tech, the IISP serves as a focal point for interdisciplinary research, education, and collaboration in the field of information security and privacy.

Program : Master of Science in Cybersecurity

Massachusetts Institute of Technology (USA)

MIT Department of Electrical Engineering and Computer Science

A joint venture between the Schwarzman College of Computing and the School of Engineering, EECS is grounded in three overlapping sub-units: electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D).

• Computer Science and Engineering
• Artificial Intelligence and Decision Making

Stanford University (USA)

Cyber Policy Center and Computer Science Department

The Cyber Policy Center brings together researchers across the Stanford campus to solve the biggest issues in cybersecurity, governance and the future of work.

• Global Digital Policy Incubator
• The Program on Platform Regulation
• Geopolitics, Technology, and Governance

SANS Technology Institute (USA)

An independent subsidiary of SANS, the SANS Technology Institute offers graduate programs (master’s degree and graduate certificates) that develop technically-adept leaders and undergraduate programs (bachelor’s degree and undergraduate certificate) for people who want to enter the cybersecurity field.

Program : Cybersecurity Master’s Degree

University of California, Berkeley (USA)

School of Information

The School of Information is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy. This requires the insights of scholars from diverse fields — information and computer science, design, social sciences, management, law, and policy.

Program : Master of Information and Cybersecurity (MICS)

University of Cambridge (UK)

Department of Computer Science and Technology

The Department of Computer Science and Technology (formerly known as the Computer Laboratory) is the academic department within the University of Cambridge that encompasses computer science, along with many aspects of technology, engineering and mathematics.

• Cybersecurity
• Software and Security Engineering

University of Oxford (UK)

Global Cyber Security Capacity Centre (GCSCC)

The Global Cyber Security Capacity Centre (GCSCC) is an international centre for research on efficient and effective cybersecurity capacity-building, promoting an increase in the scale, pace, quality and impact of cybersecurity capacity-building initiatives across the world.

Course : MSc in Software and Systems Security

Technische Universität Darmstadt (Germany)

Department of Computer Science

The scientists of the Department of Computer Science combine their diverse research activities in three main research areas:

• Artificial Intelligence
• Complex Networked Systems
• Cybersecurity & Privacy

Program : Master’s degree program IT Security

Tel Aviv University (Israel)

Research is a cornerstone of Tel Aviv University’s mission, with its scholars making discoveries in fields ranging from biotechnology and cybersecurity to archaeology and social sciences.

• Cyber Security Program
• Cyber Politics & Government

Fill out the form to get your copy of this whitepaper and find out what it takes to join a growing industry:

• I have read and agree to the Privacy Policy and would like to be updated on ISC2 certifications, educational resources, and offers.
• Carnegie Mellon University
• cybersecurity
• skill development
• University of Cambridge

Featured news

• What is cybersecurity mesh architecture (CSMA)?
• New SOHO router malware aims for cloud accounts, internal company resources
• Dropbox says attackers accessed customer and MFA info, API keys
• eBook: Do you have what it takes to lead in cybersecurity?
• eBook: Why CISSP?
• The CISO’s guide to reducing the SaaS attack surface
• Ransom recovery costs reach $2.73 million
• 2024 Data Breach Investigations Report: Most breaches involve a non-malicious human element

More From Forbes

Clemson to offer m.s. in computer science via coursera; no application required.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

Clemson University will offer an online M.S. in Computer Science for a total of $20,280 intuition.

Clemson University will partner with Coursera Coursera to offer a fully online Master of Science in Computer Science degree. The announcement was made in a blog release by Marni Baker Stein, chief content cfficer at Coursera , the online learning platform and a pioneer of Massive Open Online Courses (also known as MOOCs),

The program, which will have an artificial intelligence focus, is designed to be both affordable and uniquely accessible.

Instead of having to complete a formal application, students who hold a bachelor’s degree in any field from an accredited college and earn a B average in two introductory Clemson courses through Coursera will be automatically accepted. They will have 20% of the degree already completed.

Tuition for the complete program is set at $20,280 — 35% less than the comparable hybrid program.

“This Master of Science in Computer Science program is timely, industry-relevant and thoughtfully designed to be approachable to learners from many backgrounds, for example those looking for opportunities for mid-career advancement,” said Brian Dean, professor and C. Tycho Howle Director of the Clemson School of Computing, in the release.

“The modern and cutting-edge curriculum ensures that learners can succeed, whether they hold a formal computer science background or whether their computing background comes from prior real-world experience,” Dean added. “We are excited to be able to partner with Coursera to offer this program at Clemson University.”

Enrollment for the new program is scheduled to begin on May 1, 2024, with the first courses beginning in August 2024.

Clemson anticipates that most students will be able to complete the program in 20 to 36 months, preparing them for careers such as software development, information security analysis, and computer research. Students will be able to watch lecture videos at any time while engaging with peers and tenure-track Clemson faculty in live course sessions and office hours.

Your Best Look Yet At The New iPhone 16

The richest person in every state 2024, trump media stock djt at risk of a new short selling plunge.

The 10-course MSCS program will feature:

• An AI-first curriculum. Five of the 10 courses will be focused on AI.
• An emphasis on ethics. To promote ethical use of AI, students will be taught to examine the implications of each AI system before exploring it further.
• A combination of theory with real-world skills. Students will first learn core software engineering principles before tackling more advanced topics, including deep learning, data science, and data mining.
• A hands-on approach to learning. Students will be expected to complete complex projects in real-world computing environments, enabling them to build a substantial portfolio demonstrating they know how to apply their knowledge.

“We’re honored to partner with Clemson on this affordable, accessible, and incredibly relevant degree,” said Coursera’s Stein. “Together, we’ll educate future technical leaders, who will thoughtfully use AI to solve society’s most pressing challenges and create a positive impact.”

Clemson’s use of a performance-based admission process is an innovation that bears watching. While the use of standardized admissions tests continues to be hotly debated in higher education circles, actual course performance could prove a fairer and easier alternative for making admission decisions, particularly for certain graduate programs.

• Editorial Standards
• Reprints & Permissions

Using Graduate Student Research as an Effective Recruitment Tool

Students in the Mechanical Engineering and Materials Science Department presented research on a range of topics to show prospective candidates what’s possible at Duke.

Duke’s MEMS department’s recent research symposium served as a crucial platform for graduate students to present their work to an audience of would-be Blue Devils. The event proved instrumental in highlighting the interdisciplinary nature of the department, showcasing a selection of research presentations from current MEMS graduate students. 

The symposium included more space for informal interactions with students and visitors, as posters stood outside the conference room in the Wilkinson Engineering Building with groups gathered around exchanging ideas. Lawrie Virgin, professor in the MEMS department and director of graduate studies, says it was the first time the symposium was utilized as a recruitment event.

The combination of posters and talks showcased the wide range of research being conducted in the department, providing the recruits with some in-depth access to current research projects. Lawrie Virgin Professor in the MEMS Department and Director of Graduate Studies Google Logo

“The combination of posters and talks showcased the wide range of research being conducted in the department, providing the recruits with some in-depth access to current research projects,” Virgin said. “It also allowed our current students to gain some experience in preparing their posters and engaging in talks with prospective students.”

The MEMS graduate students organizing the symposium brought their multidisciplinary research in the hopes of conducting another event in the future. “My research presentation covered the synthesis of biocompatible polymers, which can be used to 3D print medical devices,” said Maddiy Segal, a PhD candidate in mechanical engineering and materials science and member of Matthew Becker’s, Hugo L. Blomquist distinguished professor of chemistry, research group. 

“The research symposium was a valuable tool to practice presenting our findings to a more general audience. While PhD students have many opportunities to discuss their research with other scholars in their field, finding opportunities to showcase research to a broader audience is less frequent but just as important,” she shared.

The graduate student committee of the MEMS department led the charge in bringing the event to a wider audience, with committee members focusing on organizing more ways to engage with other students considering coming to Duke. “I think this first symposium was a huge success,” said Annika Haughey, a PhD candidate in the TAST NRT program . 

“We had students presenting from all corners of the department–from aeroelasticity research to materials, as well as surgical robotics. I think the students gained valuable experience presenting and communicating their work effectively,” she said.   

Other students reveled in the opportunity to engage with collaborators and learn about the work of their peers. Defne Circi, a graduate student in MEMS, says the symposium sparked greater appreciation for her colleagues. “I connected with fellow computer science master’s students from the MEMS department,” she explained. “And the presentation broadened my perspective on the variety of research endeavors within our department. Personally, the experience rekindled my appreciation for the dynamic of live presentations and the irreplaceable aspect of face-to-face communication.”

Graduate Student Research

Engineering students at Duke are diving deeper into research that matters

Computer Programming and Data Analysis for Dummies

A new CEE class explores how a ChatGPT plugin removes entry barriers to the Wolfram family of data analysis tools for students in any discipline

DIBS Trainee Spotlight: Paris Brown

Paris Brown, an engineer, ceramist, and pilot—is charting a course toward neuroethics and science policy after graduation