Master of Science in Computer Engineering
Mission
The M.S. in Computer Engineering provides students with the knowledge, skills, and research competencies necessary for pursuing professional careers or doctoral studies in the field of computer engineering.
Program Educational Objectives
The M.S. in Computer Engineering provides a learning-centered environment where accomplished faculty members share their experience and knowledge with students so that graduates will:
- Be capable of integrating undergraduate fundamentals to solve complex electrical and computer engineering problems. They will have comprehension of advanced topics in several areas, with depth in at least one area.
- Have the ability to conduct research or execute development projects and to proficiently document the results.
Student Outcomes
Graduates are expected to be able to demonstrate the ability to:
- apply knowledge from undergraduate and graduate education to identify, formulate, and solve new and complex electrical and computer engineering problems
- plan and conduct an organized and systematic study on a significant topic within the field
- communicate both orally and in writing at a high level of proficiency in the field of study
Admission Requirements
Applicants for admissions to the program must hold a degree of Bachelor of Science in Engineering or Bachelor of Engineering, from a recognized university. A minimum cumulative Grade Point Average (GPA), on a 4.0 scale, of 2.75 and a minimum Major GPA of 2.75, or their equivalent, is required.
Bachelor of Science in Engineering holders, from a 120-credit program, must complete an additional 12 credits of engineering courses prior to their enrollment in the Masters program. No credit toward the graduate degree is given for these courses.
Candidates must submit complete applications by following the steps available at Graduate Applicants | Apply to LAU. The application must include:
- Official transcripts
- Curriculum vitae
- One letter of recommendation from a full-time faculty who is familiar with the applicant’s academic history
Students with a Bachelor of Engineering may transfer up to 18 credits from their undergraduate BE program, provided that the transferred credits correspond to graduate courses in the MS in Computer Engineering and that the student has scored at least a grade of “B” on each of these courses. Credits transfer is governed by the Academic Rules and Procedures for graduate programs.
Curriculum
Students are required to complete 30 credits for graduation: 9 credits of required courses and 21 credits of elective courses.
Required Courses (9 credits)
Elective Courses (21 credits)
MS in computer engineering students must choose their elective courses (21 credits) according to the following criteria:
- MS COE students must take four different courses (12 credits) from COE tracks; the remaining three elective courses (9 credits) can be picked from any track (COE or ELE).
- Students wishing to specialize in a specific concentration can choose at least two of their elective courses from that concentration area (AI, Hardware, Software, Communications etc.)
Selected Courses
ELE Track
Communication and Signal Processing (ELE track)
- ELE731 Optical Fiber Communications (3 cr.)
- ELE735 Information and Coding Theory (3 cr.)
- ELE772 Digital Image and Video Processing and Compression (3 cr.)
Integrated Circuits, Electronics, and Control (ELE track)
- ELE757 Simulation of Electronic Circuits (3 cr.)
- ELE799D Topics: Biomechatronics (3 cr.) offered with MCE540
Electric Power and Energy Systems (ELE track)
- ELE721 Electrical Energy Storage Systems (3 cr.)
- ELE724 Faulted Power Systems (3 cr.)
- ELE726 Renewable Energy (3 cr.)
- ELE729 Design & Operation of Smart Grids (3 cr.)
COE Track
Computer Hardware (COE track)
- COE723 High Performance Computer Architecture (3 cr.)
- COE725 VLSI Design (3 cr.)
- COE529 Testing for Digital Integrated Circuits (3 cr.)
Computer Software and Networks (COE track)
- COE745 Information Security (3 cr.)
AI Systems Engineering (COE track)
- COE743 Intelligent Data Processing & Applications (3 cr.)
- COE744 Intelligent Engineering Algorithms (3 cr.)
- COE746 Machine Learning (3 cr.)
- COE747 Deep learning (3 cr.)
- COE748 Large Language Models (3cr.)
- COE774 Computer Vision and Deep Learning (3 cr.)