UMKC Catalog


Computer Science
Discipline Coordinator
Vijay Kumar, (816) 235-2366,

Click here to see Computer Science faculty who are members of the doctoral faculty. 

Computer Science is a discipline in the Interdisciplinary Ph.D. Program administered by the School of Graduate Studies.

Note: The discipline-specific requirements listed here are in addition to the requirements listed in Interdisciplinary Ph.D. Application Procedure and Minimum Criteria for Admission and Minimum Interdisciplinary Ph.D. Academic Regulations and Degree Requirements.

Discipline-Specific Admission Requirements

A student who meets the minimum discipline requirements stated below will be considered for regular full admission to the Ph.D. program. A student who meets the requirements partially but shows high potential for advanced-level work may be considered for provisional admission. Admission also depends on factors such as number of seats available, resources available in the area of student's interest, availability of adviser, the quality of previous work, etc. A student not qualifying for admission to the Ph.D. program may be automatically considered for admission to the M.S. computer science program.

Academic Preparation

The applicant must have a bachelor's degree and/or a master's degree in computer science, computer engineering, electronics, communications engineering or any other field requiring substantial training in at least one of the above fields and in mathematics with a GPA of 3.5 or betteron a 4.0 scale, cumulative as well as in the major field; and a GPA of 3.5 or better on a 4.0 scale, in all post-baccalaureate or post-master's degree work.

Aptitude for Advanced Work

The student must demonstrate an aptitude for advanced-level work through national/international standardized examinations such as the GRE. The expected performance level is the 70th percentile in the quantitative portion of the GRE examination. The student must also show an excellent performance in all of his or her coursework.

Proficiency in English

The student must demonstrate his or her proficiency in oral and written communication in English through national/international standardized English examinations such as TOEFL, verbal portion of the GRE, etc. The expected proficiency level is the 50th percentile in the verbal portion of the GRE or a TOEFL score of 263 or above (230 CBT). UMKC students may also satisfy this requirement by obtaining an English Proficiency Certification from the English Department. [Note: As per University policy, all international students go through the English proficiency test upon arrival to the campus, regardless of their scores in TOEFL or verbal portion of GRE or any other test. A student's adviser may also require the student to take the above test, irrespective of the student's native language. As a result of this test, the student may be required to improve his or her oral and written communication in English before enrollment in the courses of the chosen disciplines.]


The student must provide at least three recommendation letters, identifying clearly his or her academic achievements and exceptional quality, from the professors from his or her previous institution(s). If the applicant has been out of school for several years, recommendation letters from his or her supervisors (technical) will be acceptable. However, even in this situation, a recommendation letter from his or her last academic institution is highly recommended. A recommendation from a faculty member in the Computer Science Electrical Engineering (CSEE) Department at UMKC must be provided if the student has taken courses from or worked with the CSEE faculty.

Statement of Goals and Objectives

The applicant must provide a 250- to 500-word essay on his or her goals and objectives of pursuing the Ph.D. in the chosen fields. This is an important document for reviewing the application. Applicants, therefore, are advised to provide a clear account of their academic achievements and plans for higher study.

Admission at an Advanced Level

An applicant who has already completed significant graduate coursework (15 or more semester hours of post-master's work or 30 or more hours of post-bachelor's work) toward a Ph.D. at another institution must provide reasons for changing institutions. The applicant must also provide a letter of endorsement from a computer science doctoral faculty member indicating willingness to be the student's research adviser.


Alternate Admission Criteria

The applicant may have received a bachelor's degree or a master's degree in computer science, computer engineering, electrical engineering or electronics, or any other related field with substantial training in mathematics. An applicant not meeting the minimum admission requirements, or not having sufficient academic preparation (stated below under prerequisite knowledge) for advanced work in the chosen primary discipline(s), may be considered for provisional admission by the CSEE Department Ph.D. committee if the committee sees high potential and preparation for advanced work from the rest of the applicant's credentials. Evidence of high potential might be pertinent work experience, published papers or extremely high achievement in related areas. In any case, the required GPA (or GPAs) must be at least 3.0 on a 4.0 scale, and the coursework deficiencies for doctoral study in computer science must not be more than 18 semester hours. Applicants with an established research or publication record in a quantitative science are encouraged to apply.


Qualifying Requirements for Full Admission

Prerequisite Knowledge

It is expected that a Ph.D. applicant selecting Computer Science as the primary discipline have the level of preparation represented by the following courses. An applicant with only a B.S. degree in computer science must have at least a GPA of 3.25/4.0 and an applicant with at least a year of graduate work must have at least a GPA of 3.5/4.0 before attempting advanced study.

Length of Time to Complete Qualifying Requirements:

When a student is admitted provisionally, the CSEE Ph.D. Committee will specify, and the UMKC Interdisciplinary Executive Committee will confirm, the conditions and length of time available to satisfy them to achieve full admission status.


Suggested Compatible Co-disciplines

Telecommunications and computer networking, electrical and computer engineering, mathematics, physics, chemistry (computational focus), engineering (civil and mechanical focus), cell biology and biophysics, molecular biology and biochemistry, oral biology, pharmaceutical sciences, pharmacology, geosciences, and economics. A co-discipline outside of this list may be considered in exceptional cases.

Core Program Requirements

The amount of work required for the Ph.D. depends on the student's level of preparation. For example, a student entering the Ph.D. program after earning a bachelor's degree may expect to do significantly more work compared to the student who enters after earning a master's degree.

Computer Science as a Co-Discipline

A Ph.D. student selecting Computer Science as co-discipline is expected to have the level of preparation represented by the following courses before attempting advanced study:

A Ph.D. student must clear the qualifying test and comprehensive test before defending his or her dissertation. The discipline's course requirements and qualifying test procedure is described below.

Discipline Course Requirements and Qualifying Test

The total Interdisciplinary Ph.D course credit (didactic) requirement is 30 hours which is divided into (a) primary discipline (12 credits), (b) co-discipline (9 credits), and (c) the remaining 9 credits can be completed either by doing graduate level courses at UMKC in any participating discipline or credits can be transferred from students’ previous institutions. This credit transfer must be approved by the CSEE Department Ph.D. committee/

Ph.D. Qualifying Test for Computer Science Discipline

The qualifying test is conducted to confirm that the student has a sound understanding of the fundamentals of computer science and has developed good problem-solving skills and research potential. This document includes the syllabus and describes the procedure for taking the qualifying test in the Computer Science discipline.


The student must be fully admitted to the Ph. D program at UMKC.  Students admitted provisionally will have to satisfy all requirements stipulated in the letter of admission before being fully admitted.

Duration for clearing Qualifying Test

A student, irrespective of being full-time or part-time, must take the qualifying test by the third semester from the date he or she is fully admitted.  For example, if a student is fully admitted in fall 2009, then he or she must take the qualifying test in Fall 2010. Failure to do so will disqualify the student from continuing in the Ph.D. program.  Upon consultation with his/her interim adviser, a student may opt to clear the qualifying test earlier than the third semester.  If a student fails the qualifying test in the first attempt then he or she MUST retake it in the subsequent semester.  Failure to clear the test in the second attempt disqualifies the student from remaining in the Ph.D. program.

Qualifying Test Dates

Qualifying tests are administered twice a year, on the second Friday of April and November.

Qualifying Test Procedure

1. Registration:  Eligible Ph.D. students must register to take the test.  The registration deadline is March 31 for the April test and October 31 for the November test.  Eligible students should send an e-mail with the following information to the Student Services Coordinator (Coretta Carter) and the Discipline Coordinator.

a. Name
b. Student ID
c. E-mail
d. Primary discipline and Co-discipline
e. Interim advisor’s name
f. Preferred semester for taking the test.

2. Registration Notification: Students will be notified by e-mail.

3. Taking the Test:  Students take the test on the prescribed date.

Test Duration:  Four hours.

Test Result

The Discipline Coordinator will make the result of the test available within two weeks from the date of the test. The minimum passing grade is 70%.  The result could be one of the following.

a. Pass:  The student proceeds to the next level of the Ph.D. curriculum. He or She prepares the plan of study and finalizes the composition of his or her supervisory committee.  The plan is submitted to the graduate office for approval.

b. Fail:  If a student fails then he/she must retake and clear the test in the subsequent semester.  Failure to clear the test in the second attempt will disqualify the student from continuing with the Ph.D. program.

c. Case for discussion: If a student scores between 67% and 70%, then the case will be reviewed.  This discussion is open to any doctoral faculty member of the discipline.  If the decision of the committee is a Fail on the second attempt then the student cannot continue in the Ph.D. program.

Test Format

The test will have two parts: Fundamental part and Discipline part. A student will answer a set of questions from each part as indicated on the test.

Contents of the Fundamental part

The qualifying test will contain questions from the topics listed below.  The questions in the Fundamental part will be based on the material typically taught in the specified undergraduate courses. The students must have a sound understanding of these topics and are expected to demonstrate this in their answers.

Fundamental part: A student must answer at least ONE question from each of the following areas.

a. Discrete Structures (Based on COMP-SCI 191 and COMP-SCI 291)
b. Data Structures (Based on COMP-SCI 303) and Algorithms (Based on COMP-SCI 404)
c. Basic Probability and Modeling (Based on COMP-SCI 394R)
d. Operating Systems (Based on COMP-SCI 431)

Contents of the Discipline part

The questions in the Discipline part will be based on undergraduate and graduate material. The syllabi of each area list a set of topics from which questions will be selected.

Discipline part: A student must select any TWO of the following areas from which to answer the required set of questions specified on the test.

a. Bioinformatics (Based on COMP-SCI 5566)
b. Cryptography (Based on COMP-SCI 5596A).
c. Databases (Based on COMP-SCI 470 and COMP-SCI 5570).
d. Design and Analysis of Algorithms (Based on COMP-SCI 5592)
e. Performance Modeling (Based on COMP-SCI 494R and COMP-SCI 5594)
f. Software Engineering (Based on COMP-SCI 451 and COMP-SCI 5551)

Computer Science as Co-discipline

A student will answer any 4 questions from the entire test. For example, a student may select all four questions from any one part or from both parts combined.

Ph.D. Qualifying Test Syllabus

The syllabus lists the main topics in each area.  Students are strongly advised to consult the listed textbooks to prepare for the test.

1. Discrete Structures

Mathematical induction, Relations, Counting methods (Permutation, Combination, Pigeon-hole principle), Recurrent relations, Graph theory.

Reference book

  • Discrete Mathematics by Richard Johnsonbaugh.

2. Data Structures and Algorithms

Basic knowledge of algorithm complexity (Big-Oh, Big-Omega, Big Theta, best, worst, and average case analysis, etc.), Binary trees, Binary search trees, AVL trees, Heaps, B-trees, B+ trees, Graphs, Hashing (Static, Dynamic, and Extendible), Huffman codes, Divide-and-conquer, Searching, Sorting, In-order, Pre-order, and Post-order traversals, Breadth first, Depth first graph traversal), Spanning trees and Shortest path.

Reference books

  • Data Structures and Algorithm Analysis in C++ by Mark Allen Weiss, Addison Wesley.
  • A Practical Introduction to Data Structures and Algorithm Analysis by Clifford A. Shaffer, Prentice Hall.

3. Basic Probability and Modeling

Sample spaces and events; Conditional probabilities; Discrete random variables (Binomial, Geometric, Poisson), Continuous random variables (Uniform, Exponential, Normal, Gamma), Moment generating functions, Moments and expectation, and Conditional expectation.

Reference books

  • Introduction to Probability Models by Ross, Academic Press.
  • Probability and Statistics with Reliability, Queuing and Computer Science Applications (Chapters 1-5), 2nd Edition, K. S. Trivedi, John Wiley & Sons.

4. Operating Systems

Process Management (synchronization, concurrency, deadlock), Memory management, Process and Job scheduling, Performance models (expected behavior), and File Systems.

Reference books

  • Operating System Concepts by Silberschatz and Galvin, John Wiley.
  • Operating Systems:  Design and Implementations by Tannenbaum, Prentice Hall.

5. Bioinformatics

Algorithms for Sequence and Structural analysis of Biopolymers (Pairwise Sequence analysis, Multiple Sequence Analysis, Structural alignment, Structure prediction, Phylogenetics), Bionformatic Databases & Ontologies, Bioinformatic Approaches to System-wide studies.

Reference books

  • Bioinformatics: Sequence and Genome Analysis by David Mount. Cold Spring Harbor (2nd edition).

6. Cryptography

Conventional and Public-key crypto-algorithms, including DES, RSA, Discrete-Logarithm based algorithms, Diffie-Hellman public-key distribution algorithm, mental poker, secret sharing, secure email, SSL, X.509, and Wireless security.

Reference books

  • Cryptography and Network Security: Principles and Practice, 4th Edition  by William Stallings.  Prentice-Hall.

7. Database Systems

Database Modeling, E-R Model, Relational Data Model, Data storage and Indexing schemes, Relational Database Design (Functional Dependencies and Modification anomalies), Relational Algebra and Relational Calculus, Query Processing and Optimization, Transaction Management (Transaction structure, transaction processing, serial and concurrent execution, and concurrency control mechanisms), Database Recovery.

Reference books

  • Fundamentals of Database Systems, Elmasri/Navathe, Addison-Wesley.
  • Database Management Systems, Ramakrishnan and Gehrke, McGraw-Hill.
  • Concurrency Control and Recovery in Database Systems, Bernstein, Hadzilacos, and Goodman.  AW.  This book is out of print but freely available on the internet 

8. Algorithms

Divide and Conquer method, Dynamic programming, Greedy algorithms, Depth-first and Breadth-first search, Shortest path algorithms, Minimum spanning trees, NP-completeness.

Reference books

  • Introduction to Algorithms. 2nd Edition, by T.H. Corman, C.E. Leiserson, R. L. Rivest, C. Stein. McGraw Hill.
  • The Design and Analysis of Computer Algorithms, by A.V. Aho, J.E. Hopcroft, J.D. Ullman. Addison-Wesley.

9. Performance Modeling

Construction of probabilistic models for performance prediction of computer systems or components, The Poisson Process, Properties of the exponential distribution, State classification (Periodic, Irreducible, Homogeneous, and Ergodic Markov chains), and M/M/1 type models.

Reference books

  • Queuing Systems I, (Chapters 1-4), by L. Kleinrock, John Wiley
  • Probability Models, (Chapters 4, 5, and 8.1-8.3) by S. Ross, Academic Press
  • Probability, Statistics & Queuing Theory, (Chapters 4-5.2) by A. Allen, Academic Press
  • Probability & Statistics with Reliability, Queuing (Chapters 6-8.4) by K. Trivedi, Wiley.

10. Software Engineering

Software process models, Software measurement, Metrics for software quality, Software project estimation techniques and models, Requirement analysis and analysis modeling, Software architecture, Software design methods, and Software design patterns, Real-time Software design, Software testing methods, Object-Oriented concepts and principles (UML analysis/design/testing), Formal methods for software engineering, Software reuse, CASE tools.

Reference books

  • Software Engineering: A Practitioner's Approach by Roger Pressman McGraw-Hill.
  • Object-Oriented methods: Principles & Practice by Ian Graham, Addison-Wesley


Requirements for Comprehensive Exams

The comprehensive exam is administered by the student's supervistory committee. A student can either take a written test or opt for an oral presentation covering both primary and co-discipline areas. Discussion with and agreement from the student's supervisory committee is required before choosing the desired option.

The candidates should contact the CSEE Division office and the chair of their doctoral committee for additional information. 

Financial Aid

All admitted international and non-resident Ph.D. students automatically receive financial aid in the form of significantly reduced fees.  In addition, they can apply to various forms of financial aid (such as graduate research assistantships, graduate teaching assistantships, graduate fellowships)  through the CSEE Department and the School of Graduate Studies. Note that a GTA can only be awarded to a student after the student has successfully demonstrated his or her teaching potential to a committee and has successfully passed the English language test. Contact the discipline coordinator for more information.