The School of Computing and Information Science offers the Master of Science (M.S.) and the Doctor of Philosophy (Ph.D.) degrees in computer science. The M.S. degree provides an intensive course of study in areas of faculty research interest. It provides the student with an excellent understanding of computer science that provides a solid foundation for many advanced jobs in the field.

The Ph.D. is granted to highly-qualified students who have completed a rigorous course of study and research training resulting in the preparation and defense of a dissertation describing original computer science research. The Ph.D. is the highest academic degree. It confers the right to use the title “doctor” and opens the door to rewarding and fulfilling careers in academia and industry.

The doctoral student will obtain a broad and deep graduate-level background in computer science, with particular depth in a chosen area of specialization. The student will engage in research from almost the beginning of the program and will receive extensive training in computer science research over the course of the program under the direction of a faculty advisor.

Requirements for the M.S. Degree

An M.S. student has one of three options: preparing and defending a master’s thesis, completing and presenting a master’s project, or a courses-only set of requirements. The thesis option is the traditional route to an M.S. degree.  Although the thesis requires substantially more work by the student than the project, it allows a more in-depth examination of a problem.  The thesis option prepares the student for a career in research and development or for further graduate work in a Ph.D. program. The master’s project is more targeted and applied than the thesis and has more course work.  A courses-only option is primarily for students seeking jobs in industry.

All three options require thirty (30) credit hours of course work, twenty-four (24) credit hours from among the approved Computer Science graduate curriculum, mostly COS courses at the 500 level and above, and six (6) credit hours possibly from outside computer science at the 400 level and above:

1. Required core courses (6 hours; must be passed with a grade of B or better):
   1. Research ethics (1 credit hour) INT 601 - Responsible Conduct of Research.
   2. Professional communications (public speaking, professional writing, online resource development).  These will be offered as two one-credit courses. (2 credit hours) SIE 501 - Introduction to Graduate Research, SIE 502 - Research Methods.
   3. One graduate-level Computer Science Theory course.  (3 credit hours) either COS 550 - Theoretical Computer Science or COS 554 - Algorithms
2. Breadth requirements - three graduate-level Computer Science courses selected from four breadth categories (at least three categories must be selected) (9 hours).  Breadth categories including representative courses include:
   1. Artificial Intelligence
      • COS 580 - Topics in Artificial Intelligence
      • COS 573 - Computer Vision
      • COS 575 - Machine Learning
      • COS 576 - Interpretability and Explainability in Machine Learning
   2. Data and Information
      • DSE 501 - Statistical Foundations of Data Science
      • COS 535 - Information Privacy Engineering
      • COS 565 - Data Visualization
      • COS 580 - Topics in Database Management
      • COS 582 - Introduction to Data Science
   3. Applications
      • COS 515 - Simulation and Modeling
   4. Systems
      • COS 520 - Software Engineering
      • COS 530 - Cybersecurity
      • COS 540 - Computer Networks
      • COS 541 - Cloud Computing
3. Two COS course electives (6 credit hours).  This requirement represents formal COS course or courses formally accepted in the Computer Science graduate curriculum at the 500 level or above and cannot be satisfied with research credits or Independent study.
4. One additional elective course (3 credit hours) approved by the M.S. Advisory Committee, not limited to computer science, however the course must be at the 400 level or above and cannot be satisfied with research credits or independent study.
5. Six (6) additional credit hours:

EITHER

An M.S. thesis, usually a research effort executed under six (6) thesis research credit hours of COS 699.

OR

An applied M.S. project executed under three (3) research credit hours of COS 699 and a COS course elective (3 credit hours) of a formal COS course or courses formally accepted in the Computer Science graduate curriculum at the 500 level or above (cannot be satisfied with research credits or Independent study).

OR

A course-only option:  a COS course elective (3 credit hours) of a formal COS course or courses formally accepted in the Computer Science graduate curriculum at the 500 level or above (cannot be satisfied with research credits or Independent study) and an elective course (3 credit hours) approved by the M.S. Advisory Committee, not limited to computer science, however the course must be at the 400 level or above (cannot be satisfied with research credits or independent study).

As per Graduate School requirements, students will form an advisory committee consisting of a minimum of three members, at least two of whom are from the computer science graduate faculty.  The student’s advisor must be a member of the computer science graduate faculty, or UMaine faculty approved by a 2/3rds majority vote of the computer science graduate faculty, or co-advised by a member of the computer science graduate faculty.

Courses from outside the COS catalog may be added to the computer science graduate curriculum by a formal petition by the student and 2/3rds majority vote of the Computer Science Graduate Faculty.  Students who complete the M.S. curriculum are urged to consider continuing on to the Ph.D. program; the M.S. requirements are a subset of the Ph.D. program.

For students choosing the thesis option, a thesis must be prepared as required by the Graduate School and defended publicly. For the project option, the student must give a public presentation of the project.

Requirements for the Ph.D. Degree

The Ph.D. program is designed to prepare the student to conduct research in computer science and to hold positions in academia and industry. The student is required to carry out in-depth, independent, publishable research that is an original contribution in the field. He or she will be involved in research soon after entering the program.

There are several steps for earning a Ph.D.:

• Coursework, Computer Science Primary Doctoral Curriculum (see below)
• Preparing a dissertation proposal and passing the Proposal Defense and Doctoral Comprehensive Examination (see below)
• Admission to candidacy
• Completing the dissertation research and any research-directed coursework
• Dissertation preparation and Pre-defense (see below)
• Successful defense of a dissertation

At all stages of their work, the student is guided by their advisor and an advisory committee. The student is urged to choose an advisor as early in the program as possible. The advisor must be a member of the computer science graduate faculty, or UMaine faculty approved by a 2/3rds majority vote of the computer science graduate faculty, or co-advised by a member of the computer science graduate faculty.  The advisor will help the student form their advisory committee. The Graduate School requires that the advisory committee consist of five (or more) members at least three of whom should be members of the Computer Science Graduate Faculty, including the student’s advisor, and it highly recommends that one committee member be selected from the graduate faculty of another program other than the student’s. The computer science graduate faculty allows this external member to be from another university as well.

Coursework

The student is required to complete 43 credit hours in an approved program of study.  If the student completed an M.S. thesis, they may count their thesis toward three (3) credit hours of their doctoral coursework.  The program of study will be developed in consultation with the student’s advisory committee.  The Computer Science Primary Doctoral Curriculum will comprise:

 

1. Required core courses (7 hours; must be passed with a grade of B or better):
   1. Research ethics (1 credit hour) INT 601 - Responsible Conduct of Research.
   2. Professional communications (public speaking, professional writing, online resource development).  These will be offered as three one-credit courses. (2 credit hours) SIE 501 - Introduction to Graduate Research, SIE 502 - Research Methods, SIE 693 - Graduate Seminar.
   3. One graduate-level Computer Science Theory course.  (3 credit hours) either COS 550 - Theoretical Computer Science or COS 554 - Algorithms
2. Breadth requirements - three graduate-level Computer Science courses selected from four breadth categories (at least three categories must be selected) (9 hours).  Breadth categories including representative courses include:
   1. Artificial Intelligence
      • COS 580 - Topics in Artificial Intelligence
      • COS 573 - Computer Vision
      • COS 575 - Machine Learning
      • COS 576 - Interpretability and Explainability in Machine Learning
   2. Data and Information
      • DSE 501 - Statistical Foundations of Data Science
      • COS 535 - Information Privacy Engineering
      • COS 565 - Data Visualization
      • COS 580 - Topics in Database Management
      • COS 582 - Introduction to Data Science
   3. Applications
      • COS 515 - Simulation and Modeling
   4. Systems
      • COS 520 - Software Engineering
      • COS 530 - Cybersecurity
      • COS 540 - Computer Networks
      • COS 541 - Cloud Computing
3. Elective coursework with an optional M.S. thesis:

EITHER

Two COS course electives (6 credit hours) AND a COS M.S. thesis.  This requirement represents formal COS course or courses formally accepted in the Computer Science graduate curriculum at the 500 level or above and cannot be satisfied with research credits or Independent study.

OR

Three COS course electives (9 credit hours).  This requirement represents formal COS course or courses formally accepted in the Computer Science graduate curriculum at the 500 level or above and cannot be satisfied with research credits or Independent study.

 

After completion of the Computer Science Primary Doctoral Curriculum the student may prepare and defend their Dissertation Proposal as part of their comprehensive examination and admission to candidacy.   Additional coursework, tailored to the dissertation topic and doctoral research will be required:

 

1. Two course electives (6 credit hours) approved by the student’s Ph.D. Advisory Committee, not limited to computer science, however the courses must be at the 400 level or above (cannot be satisfied with research credits or independent study).  It is expected that these electives will be directly related to the Ph.D. dissertation research.
2. A minimum of twelve (12) credit hours of Thesis/Research credits (COS 699).

Students admitted from the University of Maine who have taken one or more of these courses as an undergraduate must take an approved substitute course in those areas. Students from elsewhere who have had similar courses may ask for a waiver for one or more breadth courses and provide the Graduate Coordinator with sufficient documented evidence of expertise in the area. This will be evaluated on a case-by-case basis. Except in rare cases the student will be required to take the breadth courses as stated.

The 1-credit hour research ethics course should generally be taken the first semester that the student is in the program, followed by the other required courses on graduate research, research methods, and the graduate seminar. During this sequence, the student will be introduced to what it means to be a Ph.D. student, and they will be introduced to the program, the computer science graduate faculty, and their research.

Proposal defense and Oral Comprehensive Examination

Somewhere between 1.5 and 2.5 years after entering the program, the student will provide to the committee a written dissertation proposal describing the proposed research topic, the research performed to date, a complete review of relevant literature, and plans for carrying out the proposed research. The student’s proposal will then be subject to examination by the Ph.D. committee during an oral defense.  This oral examination constitutes the student’s Ph.D. Comprehensive examination, and the committee can pass the student, ask for modifications, or require a new proposal presentation (see Admission to Candidacy below). 

Admission to Candidacy

Once the student has completed the Computer Science Primary Doctoral Curriculum (the research ethics seminar, the research methods sequence, the theory requirement, all required breadth requirements, and the initial elective coursework or M.S. thesis), and has prepared their dissertation proposal and passed the Proposal defense and Oral Comprehensive Examination, then by a vote of the computer science graduate faculty they can be admitted to candidacy for the degree.

As stated above, this oral examination constitutes the student’s Ph.D. Comprehensive examination, and will include both a review of the topic, groundwork, and planning of the dissertation as well as an examination of the student’s preparation to embark on the proposed research program.  The committee can pass the student, ask for modifications, or require a new proposal presentation.

The combined proposal defense and candidacy exam is an oral presentation and interview attended by the Ph.D. advisory committee and is not open to the public.

If the student should not pass their combined proposal defense and candidacy exam, they are permitted a second attempt at this Ph.D. Comprehensive examination no sooner than 3 months but not more than 1 year after the initial exam.

Dissertation Preparation and Pre-Defense

The student will complete their dissertation research and any research-directed coursework.  Under the direction of their advisor and with consultation of their committee, they will prepare a draft of their doctoral thesis.

Six  (6) to twelve (12) months before the final dissertation defense, the student will give a pre-defense presentation to the committee.  It is expected that a draft of the thesis will be largely completed at this time.  The pre-defense dissertation presentation is an oral presentation and interview attended by the Ph.D. advisory committee and is not open to the public.

Dissertation

The dissertation is a major written work that describes the student’s original, publishable contribution to the field of computer science research. The student’s advisory committee guides the student’s work on the dissertation. Upon completion, the dissertation is defended at a public presentation.   The candidate will present their research and be subjected to cross examination not only by their advisory committee but also by the members of the audience.

The Ph.D. Advisory Committee will confer and vote in private on the results of the Ph.D. dissertation defense.  At its discretion, the committee may also invite other non-voting members into the conference including but not limited to the COS Graduate Program Coordinator, members of the COS Graduate Program Committee, and the SCIS Director.  For the candidate to successfully pass their examination, only one dissenting vote of the advisory committee is allowed.  The Ph.D. Advisory Committee can pass the student, ask for modifications to the dissertation, or require a new presentation of the dissertation defense.

If the candidate should not pass their Ph.D. dissertation defense, they are permitted a second attempt of their Ph.D. dissertation defense no sooner than 3 months but not more than 1 year after the initial defense.

Petitions

In order to request an exception to the limitation of two attempts at an oral examination or defense, or other rules outlined here, the student (or candidate) should petition the COS Graduate Program Committee by submitting a written explanation of their request to the COS Graduate Program Coordinator with justifications of why such an exception should be granted.  The petition should be endorsed by at least three members of their Ph.D. Advisory committee.

Other Policies

The Computer Science Graduate Program is inherently part of the UMaine Graduate School and is governed under its rules and policies.  For the resolution of any policies, procedures, or rules not covered here, students and faculty are referred to the UMaine Graduate School Policies and Regulations.

 

Graduate Faculty

Sudarshan S. Chawathe, Ph.D. (Stanford University, Computer Science, 1999). Associate Professor. Areas of interest: autonomous and semistructured databases. (chaw@cs.umaine.edu)

Chaofan Chen, Ph.D. (Duke University, Computer Science, 2020). Assistant Professor. Areas of interest: interpretable machine learning and applications of machine learning in high-stakes decision making. (chaofan.chen@maine.edu)

Phillip M. Dickens, Ph.D. (University of Virginia, Computer Science, 1993), Associate Professor. Areas of interest: high-performance computing, grid computing, distributed systems, distributed simulation, networking protocols, performance modeling. (dickens@cs.umaine.edu)

James L. Fastook, Ph.D. (University of Maine, Physics, 1975), Professor. Areas of interest: glacial modeling, finite elements, non linear differential equations, vector and parallel processing, supercomputers. (fastook@maine.edu)

Sepideh Ghanavati, Ph.D. (University of Ottowa, Computer Science, 2013), Assistant Professor. Areas of interest: privacy and security in software engineering, privacy and security for Internet of Things (IoT), regulatory compliance software engineering, usable privacy, machine learning and deep learning for privacy policy analysis, privacy by design and privacy requirements analysis, goal-oriented requirements modeling and requirements engineering (sepideh.ghanavati@maine.edu)

Torsten Hahmann, Ph.D. (University of Toronto, Computer Science, 2013). Associate Professor. Areas of interest: artificial intelligence (knowledge representation, logic, automated reasoning), spatial informatics, spatial AI, knowledge and ontology engineering, theoretical computer science. (torsten.hahmann@maine.edu)

Penny Rheingans, Ph.D. (University of North Carolina Chapel Hill, Computer Science, 1993). Director SCIS, Professor. Areas of interest: visualization of spatial and non-spatial data, including the visualization of predictive models, data with associated uncertainty, and data about student success. Visualization based on perception and illustration. Dynamic and interactive representations and interfaces, and the experimental validation of visualization techniques. Computer science pedagogy and gender issues in technology education. (penny.rheingans@maine.edu)

Roy M. Turner, Ph.D. (Georgia Institute of Technology, Computer Science, 1989), Associate Professor. Areas of interest: artificial intelligence (problem solving, planning, context -sensitive reasoning), cooperative distributed problem solving, multiagent systems, control of autonomous underwater vehicles, computational ecology, applications of AI to biology. (rmt@cs.umaine.edu)

Manuel Woersdoerfer, Ph.D. (Goethe University, Germany, Business Ethics, 2011). Assistant Professor. Areas of interest: engineering and computer ethics (especially big data ethics and information privacy)., business (ethics) and human rights, (political) Corporate Social Responsibility (CSR) and corporate citizenship, multi-stakeholder CSR-initiatives (especially equator principals framework and U.N. guiding principles on business and human rights), sustainable finance and finance ethics, climate ethics/justice (with special focus on financial institutions and climate change mitigation), constitutional economics, neoliberalism and social market economy, behavioral and happiness economics, economic psychology and neuroeconomics, economic policy (with a special focus on European integration and politics).  (manuel.woersdoerfer@maine.edu)

Salimeh Yasaei Sekeh, Ph.D. (Ferdowsi University of Mashhad, Iran, Inferential Statistics, 2013). Assistant Professor. Areas of interest: machine learning algorithms design and analysis, data science and developing theory and algorithms for data analysis. applications of machine learning approaches in real-time problems, design, improvement, and analysis of deep learning techniques, data mining and pattern recognition, statistical machine learning and signal processing, network structure learning with applications in biology.  (salimeh.yasaei@maine.edu)

Terry S. Yoo, Ph.D. (University of North Carolina Chapel Hill, Computer Science, 1996). Associate Professor. Areas of interest: open source software project management, large data initiatives, data Science, 3D multiscale medical image analysis and data visualization, computer graphics, image-based search, computational geometry, 3D printing, high-resolution 3D electron microscopy, Computer vision.  (terry.yoo@maine.edu)