Full Description

The Ph.D. degree in Computer Science provides students with an educational and research environment that fosters personal and intellectual growth, flourishes academic goals and develops career paths through necessary training with emerging technologies. The program promotes research, discovery and integration, and is designed for students interested in becoming professional scholars, college and university professors or researchers in private, industrial or government research institutions.

For more information about graduate admissions, visit the graduate admission website. For more information on international admissions, visit the international admission website.

Admission Requirements

• Bachelor's degree in computer science (or closely related field) from an accredited college or university¹
• Minimum 3.000 GPA on a 4.000 point scale 
• Official transcript(s)
• GRE scores
• Résumé
• Goal statement
• Three letters of recommendation
• English language proficiency - all international students must provide proof of English language proficiency (unless they meet specific exceptions) by earning one of the following:
  • Minimum 525 TOEFL PBT score (paper-based version)
  • Minimum 71 TOEFL IBT score (Internet-based version)
  • Minimum 74 MELAB score
  • Minimum 6.0 IELTS score
  • Minimum 50 PTE score

¹

Students whose records clearly indicate a potential to do doctoral-level work in computer science may be directly admitted and must fulfill the requirements of both the master's and doctorate degrees.

For more information about graduate admissions, please visit the Graduate Studies admission website. For more information on international admission, visit the Office of Global Education’s admission website.

Application Deadlines

• Fall Semester
  • Application deadline: June 15

• Spring Semester
  • Application deadline: November 1

• Summer Term
  • Application deadline: April 1

Applications submitted after these deadlines will be considered on a space-available basis.

Program Learning Outcomes

Graduates of this program will be able to:

1. Have all around breadth-of-knowledge and understanding of essential facts, concepts, principles and theories relating to advanced topics in computer science to be regarded as a scholar of computer science.
2. Demonstrate depth of knowledge at least in one specialized topic.
3. Conduct independent research by advancing the body of knowledge in the area through the doctoral dissertation research.
4. Clearly articulate advanced research problems and their solutions.
5. Present general computer science topics in a learning environment.
6. Develop and write publishable papers that clearly articulate advanced research problems and their solutions.
7. Demonstrate integrative and deep knowledge of essential literature, facts, concepts, principles and theories relating to a chosen area of research.
8. Perform complete and thorough literature searches.
9. Evaluate, comprehensively and critically, the extent to which a particular work relates to and/or contributes to a given field.
10. Publish and participate in a chosen research community.

Program Requirements

Major Requirements

¹

Maximum 9 credit hours of CS 89098 or CS 89991 may be applied towards the degree.

²

Students must make a public presentation of project and/or research work (excluding dissertation defense and candidacy examination) at least two times before graduation. The presentation must take place in the doctoral seminar at least one full term before graduation and not more than two years after entering the program. The doctoral seminar is offered for 1 or 2 credit hours; therefore, the student must enroll in this course at least two times. This course can be taken multiple times but only 3 credit hours count toward the degree.

³

Each doctoral candidate, upon admission to candidacy, must register for CS 89199 for a total of 30 credit hours. It is expected that a doctoral candidate will continuously register for Dissertation I, and thereafter CS 89299, each semester, until all requirements for the degree have been met. A dissertation describes original research performed by the student. The dissertation topic must be approved by the advisor and graduate coordinator. A dissertation committee, made up of graduate faculty, must be formed to assess the quality and value of the work. A public dissertation defense is made by the student. The final dissertation and defense must be approved by the advisor and dissertation committee.

Graduation Requirements

Proficiency Requirements and Candidacy

Students must clear core area proficiency requirements. Students admitted after completing a master's of computer science degree must take the Prelims examination within the first two semesters. Other students, including directly-admitted Ph.D. students, must take the Prelims examination within the first three semesters

The candidacy examination is a comprehensive examination in the field of the major subject. The format of the candidacy examination will be determined by the student’s Candidacy Examination Committee, which is composed of the student’s advisor and two other graduate faculty members. The Candidacy Examination Committee must be approved by the graduate coordinator. The student must complete the candidacy examination at least one year before the dissertation defense.

• Delivery:
  • In person
• Location:
  • Kent Campus

Full Description

The Master of Science degree in Data Science provides a focus on developing scientists who will understand the theories, methods and tools of data science and apply data science to solving research and workplace questions in the natural, health and social sciences for businesses and industries.

Data science is a STEM discipline founded on the principles of mathematics and the sciences and developed through a synthesis of mathematics and computer science. One may think of data science as a blending together of methods and ideas from analysis, statistics, databases, big data, artificial intelligence, numerical analysis, graph theory and visualization for the purposes of finding information in data and applying that information to solving real-world problems.

For more information about graduate admissions, visit the graduate admission website. For more information on international admissions, visit the international admission website.

Admission Requirements

• Bachelor’s degree from an accredited college or university
• Minimum 3.000 undergraduate GPA (on a 4.000-point scale)
• Prerequisite mathematics and computer science courses¹
• Official transcript(s)
• Effective for fall 2024 admission term, GRE scores will be required
• Two letters of recommendation
• English language proficiency - all international students must provide proof of English language proficiency (unless they meet specific exceptions) by earning one of the following:
  • Minimum 525 TOEFL PBT score
  • Minimum 71 TOEFL IBT score
  • Minimum 74 MELAB score
  • Minimum 6.0 IELTS score
  • Minimum 50 PTE score
  • Minimum 100 Duolingo English score

¹

Students entering the program are expected to have previously completed courses in linear algebra (equivalent to MATH 21001 or MATH 21002), statistics (equivalent to MATH 20011), advanced calculus (equivalent to MATH 22005), discrete mathematics/structures (equivalent to MATH 31011 or CS 23022), programming and data structures (equivalent to CS 23001) and database systems (equivalent to CS 33007). Applicants have not completed all the prerequisite courses may be admitted conditionally (based on a wholistic review of their application) until they complete the remaining courses being before beginning the program’s coursework.

Application Deadlines

• Fall Semester
  • Application deadline: June 15

• Spring Semester
  • Application deadline: November 1

• Summer Term
  • Application deadline: April 1

Applications submitted after these deadlines will be considered on a space-available basis.

Program Learning Outcomes

Graduates of this program will be able to:

1. Ask the questions so that problems in a particular business or industrial situation become clear.
2. Determine if the problem may be addressed with data science methods and tools, and if yes, propose potential methods for solving the problems.
3. Make suggestions for how data science may be used to enhance the quality and value of currently existing products (whether the products are physical or methods) and how data science may be used in the development of new products.

Program Requirements

Major Requirements

Graduation Requirements

The culminating experience requirement is a master’s thesis or an integrated learning experience.

The master’s thesis requires a written thesis, a public defense of the thesis and approval by the student’s supervisory committee. Students must form a master's thesis committee, which will include the advisor and at least two other graduate faculty members. The thesis topic and committee must be approved by the advisor and graduate coordinator. The final version of the thesis must be approved by the advisor, thesis committee and graduate coordinator.

The integrated learning experience may include a substantial capstone project or a capstone project and internship. Students must prepare a written document explaining and/or demonstrating their capstone project or internship activity and its significance. In addition, students must give a public presentation of their capstone project or internship, and the written document and presentation must be approved by their supervisory committee.

Roadmap

This roadmap is a recommended semester-by-semester plan of study for this major. However, courses designated as critical (!) must be completed in the semester listed to ensure a timely graduation.

• Delivery:
  • In person
• Location:
  • Kent Campus

Full Description

The Master of Science degree in Artificial Intelligence prepares students with a focused educational and research environment to develop career paths through necessary learning and training with emerging artificial intelligence technologies and applications to intelligent analytics, smart homes and communities and robotics and automation. Graduates have technical knowledge and research and development skills necessary for applying artificial intelligence to industry, community and military. These areas include sectors requiring intelligent pattern-analysis of big data such as retail, healthcare, biology, psychology and intelligent human-machine interactions and interfaces.

For more information about graduate admissions, visit the graduate admission website. For more information on international admissions, visit the international admission website.

Admission Requirements

• Bachelor's degree in artificial intelligence, computer science, computer engineering or related area from an accredited college or university (A degree in artificial intelligence, computer science, computer engineering or related area is no longer required starting with spring 2024 admission)
• Minimum 3.000 undergraduate GPA on a 4.000-point scale
• Core components of an undergraduate computer science curriculum (no longer required starting with spring 2024 admission)¹
• Course Proficiency: Successful course completion of high-level algebra, geometry and calculus (equivalent to MATH 12002, MATH 12003, MATH 21001) is required starting with spring 2024 admission²
• Official transcript(s)
• GRE scores
• Résumé
• Goal statement
• Three letters of recommendation (Two letters of recommendation are required starting with spring 2024 admission)
• English language proficiency - all international students must provide proof of English language proficiency (unless they meet specific exceptions) by earning one of the following:
  • Minimum 525 TOEFL PBT score (paper-based version)
  • Minimum 71 TOEFL IBT score (Internet-based version)
  • Minimum 74 MELAB score
  • Minimum 6.0 IELTS score
  • Minimum 50 PTE score
  • Minimum 100 Duolingo English Test score

Highly qualified students lacking preparation in some standard areas may be considered for admission on a case-by-case basis.

¹

Prospective students must successfully have completed undergraduate coursework in algorithms, databases, data structures, programming skills and probability and statistics. In addition, course(s) in operating systems is recommended. Highly qualified students lacking preparation in certain standards areas may be admitted.

²

Recommended but not required: Successful course completion in computer programming, discrete structures, data structures and abstraction, operating systems, database and computer algorithms (equivalent to CS 13011, CS 13012, CS 23001, CS 23022, CS 33007, CS 33211, CS 46101).

Application Deadlines

• Fall Semester
  • Application deadline: June 15
• Spring Semester
  • ​Application deadline: November 1
• Summer Term
  • Application deadline: April 1

Applications submitted after this deadline will be considered on a space-available basis.

Program Learning Outcomes

Graduates of this program will be able to:

1. Combine intelligent analytics and automation, human-computer interaction and robotics techniques to optimize and automate transportation, industrial processes and/or healthcare processes.
2. Apply machine learning techniques on big data to predict, classify, data mine and explore patterns.
3. Apply intelligent visualization and Internet-based techniques for smart homes and communities.
4. Perform research, discovery and integration by applying knowledge of artificial intelligence theory and techniques.

Program Requirements

Major Requirements

Progression Requirements

Students should complete a minimum of two required courses and either CS 53302, CS 64301 or CS 67302 before taking elective courses.

Students must maintain a minimum 3.000 GPA. Students earning less than a 3.000 GPA or earning a C grade or lower in two courses will be placed on academic probation.

Graduation Requirements

• Delivery:
  • In person
• Location:
  • Kent Campus