Electrical and Computer Engineering
| Department | Electrical and Computer Engineering |
| Degrees Offered | Master of Science in Electrical and Computer Engineering (MSECE) |
| PhD in Computer Engineering | |
| Website | https://www.uab.edu/ece/ |
| Director | Arie Nakhmani, PhD |
| Phone | (205) 975-0801 |
| anry@uab.edu |
The Department of Electrical and Computer Engineering offers two advanced degrees: a Master of Science in Electrical and Computer Engineering (MSECE) and a PhD in Computer Engineering. The MSECE program equips students for professional careers in industry or further academic pursuits, such as doctoral programs or professional schools. The PhD in Computer Engineering is designed to prepare students for research and professional roles in both industry and academia. The PhD program is a collaborative effort between the University of Alabama at Birmingham (UAB) and the University of Alabama in Huntsville (UAH), with both institutions contributing to the curriculum and research opportunities.
Financial Support
Fellowships and/or assistantships may be available for well-qualified students admitted into the graduate program.
There are fellowships and scholarships available through the Graduate School.
Master of Science in Electrical and Computer Engineering
The Master of Science in Electrical and Computer Engineering (MSECE) prepares students for a professional career in industry or entry into a doctoral program or professional school. The MSECE program builds upon the broad foundation provided by a Bachelor of Science in Electrical and Computer Engineering by supplying depth in specific electrical and computer engineering areas through advanced coursework and a thesis or project experience.
Admission Requirements
Admission to the MSECE degree program requires the following:
- An undergraduate degree in electrical engineering, computer engineering, or related fields. Students without sufficient background may be required to complete prerequisite courses based on their prior coursework and their plan of study, which will be defined at the time of admission.
- A 3.0 GPA or higher on a 4.0 scale, or at least 3.0 for the last 60 semester hours completed;
- Three letters of recommendation concerning the applicant's previous academic and professional work;
- Resume or Curriculum Vitae (CV)
- International applicants must submit English proficiency scores in accordance with the UAB Graduate School requirement. Click here for details
- Original transcripts from all colleges and universities attended since high school (detailed instructions are included during the online application process)
Scores on the GRE General Test are not required.
Fast Track (Early Acceptance) MSECE Program
High-achieving, UAB Electrical and Computer Engineering undergraduate students may begin work toward their MSECE degree while still undergraduates. To be considered for this program, students must have junior-level standing (more than 60 hours completed), have completed at least 3 of the required junior-level ECE courses, and have a UAB GPA of at least 3.0. Applicants are expected to have already selected a research mentor for their graduate studies, which will typically be a continuation of their undergraduate research. One of the letters of recommendation must be from the research mentor. Students may pursue either the Plan I or Plan II MSECE option.
To learn more about the Fast Track program including additional requirements and how to apply visit the Graduate School's ALO page.
Accelerated Bachelor's-Master's (ABM) Program
Electrical and Computer Engineering offers an Accelerated Bachelor's- Master's (ABM) option for high-achieving undergraduate students pursuing a BSECE degree at UAB. In this program, up to 12 credit hours will be shared between the BSECE and MSECE programs. The courses approved for shared credit for students pursuing an ABM are all EE 400 and EE 500 level courses, except EE 490, EE 491, EE 492, EE 498, EE 499, EE 590, and EE 591.
To be considered for this program, students must have junior-level standing (more than 60 hours completed), have completed at least 3 of the required junior-level ECE courses, and have a UAB GPA of at least 3.5. Applicants are expected to have already selected a research mentor for their graduate studies, which will typically be a continuation of their undergraduate research. One of the letters of recommendation must be from the research mentor. Students may pursue either the Plan I or Plan II MSECE option. A successful graduate of ABM will earn both a bachelor's degree and a master's degree in ECE from UAB in an accelerated timeframe compared to the independent completion of two degrees.
To learn more about ABM program, including additional requirements and how to apply, visit the Graduate School's ALO page.
Plan I (Thesis Option)
The Plan I Master's degree requires completion of at least 33 credit hours of graduate work.
- 18 credit hours of approved graduate-level courses appropriate to the student's area of technical specialization
- 6 credit hours of approved courses having a mathematical emphasis
- 9 credit hours of EE 699 Thesis Research
Students must be admitted to candidacy prior to enrolling in EE 699. A student is eligible for admission to candidacy after (1) a written thesis proposal has been orally presented to the committee and approved and (2) completion of Responsible Conduct of Research (RCR) training. Admission to candidacy must take place at least one semester before the student may graduate.
Plan II (Non-Thesis Option)
The Plan II Master's degree requires completion of at least 33 semester hours of graduate work.
- 24 credit hours of approved graduate-level courses appropriate to the student's area of technical and professional specialization;
- 6 credit hours of approved courses having a mathematical emphasis;
- 3 credit hours of EE 697 Graduate Project
Additional Academic Policies
Special Topics (590/690/790) courses and Independent Study (591/691/791) courses are reviewed for degree applicability for each program in the School of Engineering. No more than 6 combined credit hours of Special Topics and/or Independent Study courses will be applied to the degree without appeal to and approval from the Program Director.
The School of Engineering offers similar courses at the 400/500 and 600/700 levels. While the higher-numbered course has more advanced content, there is a significant overlap in topics. Therefore, students are not allowed to take a 500-level or 700-level course for credit if they have previously taken the related 400-level or 600-level course, respectively.
PhD in Computer Engineering
The PhD degree prepares students for professional and research careers in industry and academia. The PhD in Computer Engineering is awarded by UAB and is offered as a collaborative program with the University of Alabama in Huntsville (UAH), allowing both UAB and UAH to contribute to the program.
Admission Requirements
Admission to the PhD program include the following:
- An undergraduate degree in electrical engineering, computer engineering, or related fields. Students without sufficient background may be required to complete prerequisite courses based on their prior coursework and their plan of study, which will be defined at the time of admission.
- A 3.0 GPA or higher on a 4.0 scale, or at least 3.0 for the last 60 semester hours completed
- Three letters of recommendation concerning the applicant's previous academic and professional work
- Resume or Curriculum Vitae (CV)
- International applicants must submit English proficiency scores in accordance with UAB Graduate School requirement. Click here for details;
- Original transcripts from all colleges and universities attended since high school (detailed instructions are included during the online application process)
Scores on the GRE General Test are not required.
PhD in Computer Engineering
Post Bachelor Requirements
| Requirements | Hours | |
|---|---|---|
| Computer Engineering Requirement | 18 | |
| Practical Computer Vision | ||
| Engineering Operations | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Computer or Electrical Engineering Requirement | 12 | |
| Practical Computer Vision | ||
| Engineering Operations | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Medical Image Processing | ||
| Machine Learning for Biomedical Engineering Applications | ||
| Neural Computation | ||
| Neural Computation | ||
| Computational Vision | ||
| Computational Vision | ||
| Foundations of Artificial Intelligence in Medicine | ||
| Technical Introductions to Deep Learning in Medicine | ||
| Explainable AI in Medicine | ||
| Mathematics Requirement | 9 | |
| Algebra I: Linear | ||
| Special Topics in Mathematics | ||
| Math Methods in EGR I | ||
| Computational Methods in EGR | ||
| Math Methods in EGR I | ||
| Computational Methods in EGR | ||
| Machine Learning for Biomedical Engineering Applications | ||
| Engineering Analysis | ||
| Applied Matrix Analysis | ||
| General Linear Models | ||
| Estimation & Inference | ||
| Statistical Theory I | ||
| Statistical Theory II | ||
| Nonparametric Methods | ||
| Categorical Data Analysis | ||
| Applied Multivariate Analysis | ||
| Structural Equation Modelling | ||
| Survival Analysis | ||
| Sampling Methods | ||
| Theory of Linear Models | ||
| Bayesian Analysis | ||
| Advanced Bayesian Analysis II | ||
| Stochastic Modeling | ||
| Generalized Linear and Mixed Models | ||
| Advanced Computational Methods | ||
| Random Variables and Processes | ||
| Advanced Communication Theory | ||
| Computer Vision | ||
| Information Theory and Coding | ||
| Digital Image Processing | ||
| Introduction to Neural Networks | ||
| Neural Time Series Data Analysis | ||
| Modern Control Theory | ||
| Intelligent Systems | ||
| Numerical Methods in Engineering | ||
| Introduction to Big Data Analytics | ||
| Machine Learning in Engineering | ||
| Medical Signal Processing | ||
| Random Variables and Processes | ||
| Advanced Communication Theory | ||
| Computer Vision | ||
| Information Theory and Coding | ||
| Digital Image Processing | ||
| Introduction to Neural Networks | ||
| Neural Time Series Data Analysis | ||
| Modern Control Theory | ||
| Intelligent Systems | ||
| Numerical Methods in Engineering | ||
| Introduction to Big Data Analytics | ||
| Machine Learning in Engineering | ||
| Medical Signal Processing | ||
| Supportive Coursework Requirement | 6 | |
| Practical Computer Vision | ||
| Engineering Operations | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Medical Image Processing | ||
| Machine Learning for Biomedical Engineering Applications | ||
| Neural Computation | ||
| Neural Computation | ||
| Computational Vision | ||
| Computational Vision | ||
| Foundations of Artificial Intelligence in Medicine | ||
| Technical Introductions to Deep Learning in Medicine | ||
| Explainable AI in Medicine | ||
| GRD 717 | Principles of Scientific Integrity | 3 |
| EE 799 | Dissertation Research | 24 |
| Total Hours | 72 | |
Post Master Coursework Requirements
| Requirements | Hours | |
|---|---|---|
| Computer Engineering Requirement | 9 | |
| Practical Computer Vision | ||
| Engineering Operations | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Computer or Electrical Engineering Requirement | 6 | |
| Practical Computer Vision | ||
| Engineering Operations | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Medical Image Processing | ||
| Machine Learning for Biomedical Engineering Applications | ||
| Neural Computation | ||
| Neural Computation | ||
| Computational Vision | ||
| Computational Vision | ||
| Foundations of Artificial Intelligence in Medicine | ||
| Technical Introductions to Deep Learning in Medicine | ||
| Explainable AI in Medicine | ||
| Mathematics Requirement | 6 | |
| Algebra I: Linear | ||
| Special Topics in Mathematics | ||
| Math Methods in EGR I | ||
| Computational Methods in EGR | ||
| Math Methods in EGR I | ||
| Computational Methods in EGR | ||
| Machine Learning for Biomedical Engineering Applications | ||
| Engineering Analysis | ||
| Applied Matrix Analysis | ||
| General Linear Models | ||
| Estimation & Inference | ||
| Statistical Theory I | ||
| Statistical Theory II | ||
| Nonparametric Methods | ||
| Categorical Data Analysis | ||
| Sampling Methods | ||
| Theory of Linear Models | ||
| Bayesian Analysis | ||
| Advanced Bayesian Analysis II | ||
| Stochastic Modeling | ||
| Generalized Linear and Mixed Models | ||
| Advanced Computational Methods | ||
| Random Variables and Processes | ||
| Advanced Communication Theory | ||
| Computer Vision | ||
| Information Theory and Coding | ||
| Digital Image Processing | ||
| Introduction to Neural Networks | ||
| Neural Time Series Data Analysis | ||
| Modern Control Theory | ||
| Intelligent Systems | ||
| Numerical Methods in Engineering | ||
| Introduction to Big Data Analytics | ||
| Machine Learning in Engineering | ||
| Medical Signal Processing | ||
| Random Variables and Processes | ||
| Advanced Communication Theory | ||
| Computer Vision | ||
| Information Theory and Coding | ||
| Information Theory and Coding | ||
| Digital Image Processing | ||
| Introduction to Neural Networks | ||
| Neural Time Series Data Analysis | ||
| Modern Control Theory | ||
| Intelligent Systems | ||
| Introduction to Big Data Analytics | ||
| Machine Learning in Engineering | ||
| Medical Signal Processing | ||
| Supportive Coursework Requirement | 3 | |
| Practical Computer Vision | ||
| Engineering Operations | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Technical Communication for Engineers | ||
| Electromagnetic Field Theory I | ||
| Medical Image Processing | ||
| Machine Learning for Biomedical Engineering Applications | ||
| Neural Computation | ||
| Neural Computation | ||
| Computational Vision | ||
| Computational Vision | ||
| Foundations of Artificial Intelligence in Medicine | ||
| Technical Introductions to Deep Learning in Medicine | ||
| Explainable AI in Medicine | ||
| GRD 717 | Principles of Scientific Integrity | 3 |
| EE 799 | Dissertation Research | 24 |
| Total Hours | 51 | |
Committee and Candidacy Requirements
In addition to completing coursework requirements (see below), doctoral students must form a Graduate Dissertation Committeeconsisting of at least five faculty members, including the primary research mentor. At least two committee members must have a primary appointment at UAB in the Department of Electrical and Computer Engineering and one must have a primary appointment at UAH in the Electrical and Computer Engineering Department.
A comprehensive examination is required of all doctoral candidates. This exam is given after:
- All coursework is completed,
- Successful completion of GRD 717 Principles of Scientific Integrity,
- The student’s Graduate Committee, which consists of faculty representatives from both campuses, deems the student to have adequate preparation in the major and minor fields of study.
The examination is conducted by the Graduate Committee and administered on the resident campus. The examination consists of a written part and an oral part. The student presents a dissertation proposal during the oral portion of the examination. The comprehensive examination may only be taken twice.
After successfully passing the exam and defense, the graduate student will then enter into doctoral candidacy. Doctoral candidates must complete a minimum of 24 hours of dissertation research and then develop a dissertation for review by the dissertation committee. The candidate must also present an oral public defense of their dissertation. This must take place at least two semesters before the student may graduate. If the defense is successful, the student then has 10 working days to revise the dissertation and submit its approved form to the Graduate School by the published deadline.
Publication Requirement
Original peer-reviewed research articles in reputable journals or conferences are the standard for demonstrating scientific productivity. The research conducted by ECE doctoral students is expected to result in such publications. Before the degree is awarded, students are required to have at least one “first-author” journal or conference article that has been published (or accepted for publication) and a second that has been submitted to a publication. Many students will be co-authors on collaborative research articles and may also share authorship on review articles, book chapters, conference proceedings, and other forms of scientific communication. Although these works bolster the student’s scientific credentials, they do not count toward the ECE publication requirement. In some cases, first-authorship of an article is shared among multiple individuals. In these cases, the article may count toward the publication requirement of only one ECE doctoral student.
Additional Academic Policies
Special Topics (590/690/790) courses and Independent Study (591/691/791) courses are reviewed for degree applicability for each program in the School of Engineering. No more than 6 combined credit hours of Special Topics and/or Independent Study courses will be applied to the Computer Engineering PhD without appeal to and approval from the Program Director.
The School of Engineering offers similar courses at the 400/500 and 600/700 levels. While the higher-numbered course has more advanced content, there is a significant overlap in topics. Therefore, students are not allowed to take a 500-level or 700-level course for credit if they have previously taken the related 400-level or 600-level course, respectively.
Graduate Certificate in Applied Data Analytics and AI Engineering
| Requirements | Hours | |
|---|---|---|
| EE 655/755 | Cloud Computing | 3 |
| EE 656/756 | Introduction to Big Data Analytics | 3 |
| EE 658/758 | Machine Learning in Engineering | 3 |
| Elective Options | 6 | |
Choose two of the following: | ||
| Introduction to Neural Networks | ||
| Object-Oriented Design | ||
| Intelligent Systems | ||
| Numerical Methods in Engineering | ||
| Software Engineering | ||
| Software Engineering Large Systems - I | ||
| Mobile Computing | ||
| Total Hours | 15 | |
Graduate Certificate in Medical Signal Image Analysis
| Requirements | Hours | |
|---|---|---|
| EE 623/723 | Computer Vision | 3 |
| EE 643/743 | Numerical Methods in Engineering | 3 |
| EE 660/760 | Medical Signal Processing | 3 |
| Elective Options | 6 | |
Choose two of the following: | ||
| Medical Image Analysis | ||
| Random Variables and Processes | ||
| Digital Image Processing | ||
| Introduction to Neural Networks | ||
| Neural Time Series Data Analysis | ||
| Cloud Computing | ||
| Introduction to Big Data Analytics | ||
| Machine Learning in Engineering | ||
| Total Hours | 15 | |