Program Director
Qinru Qiu, 4-206 Center for Science and Technology, 315-443-4440, Fax: 315-443-2583; qiqiu@syr.edu
Faculty
Thomas Barnard, Howard A. Blair, Tomislav Bujanovic, Stephen J. Chapin, Biao Chen, C.Y. Roger Chen, Shiu-Kai Chin, Jun Hwan (Brandon) Choi, Wenliang (Kevin) Du, Sara Eftekharnejad, Ehat Ercanli, Makan Fardad, James W. Fawcett, Prasanta Ghosh, Jennifer Graham, Mustafa Cenk Gursoy, Robert Irwin, Can Isik, Andrew Chung-Yeung Lee, Jay Kyoon Lee, Yingbin Liang, Duane L. Marcy, Kishan G. Mehrotra, Chilukuri K. Mohan, Jae C. Oh, Susan Older, Vir Phoha, Qinru Qiu, James S. Royer, Jeffrey Saltz, Tapan K. Sarkar, Fred Schlereth, Q. Wang Song, Sucheta Soundarajan, Jian Tang, Yuzhe (Richard) Tang, William C. Tetley, Pramod K. Varshney, Senem Velipasalar, Li Wang, Hong Wang, Yanzhi Wang, Heng Yin, Edmund Yu, Reza Zafarani
Description:
Syracuse University’s mission is to prepare students for the world through an inclusive, interdisciplinary and collaborative education in which they reach beyond the classroom to test what they learn and engage with their industry community.
The MSCE courses cover a variety of specific subjects and skills within:
Hardware Systems
Software Systems
Security and Assurance Systems.
Accreditation:
Accredited by Middle States Association of Colleges and Schools
Admission:
Candidates are required to hold a Bachelor of Science degree and have acquired at least three years of industry experience in one of the following or a related field:
Electrical
Electronics
Communication
Computer
Software engineering
GRE Verbal score of 150 or better (using New GRE Score System); GRE Quantitative score of 155 or better (using New GRE Score System); GRE Analytical (multiple choice) score of 650 or better, or a score of 3.5 or better in the new Analytical Writing; for international students: TOEFL computer-based score of 223 (Internet-based score 85; paper-based score 563) or better; grade point average (GPA) of 3.0/4.0 or better.
Financial Support:
Syracuse University has a variety of financial aid programs to support graduate study, including scholarships, assistantships, and fellowships. These programs are administered within each of the University’s academic departments, so the fastest and easiest way to determine what aid you may be eligible for is to connect with specific school or college staff. Federal Unsubsidized Loans for masters, professional and doctoral students are available for up to $20,500, (see eligibility requirements).
Federal financial aid, including loans, requires that you file the Free Application for Federal Student Aid (FAFSA).
Facilities:
Classes are taught entirely online. Classrooms are equipped with at least two cameras, microphones (for the instructor and students) smart boards and/or tablet monitors and each class session will be webcast live.
Online students have the option to attend the live class session through an online web conferencing platform or view the recording after the class has ended. The web conferencing platform provides interface includes three pods: 1) Camera view of the instructor, 2) Display of the smart board or tablet monitor and 3) Chat tool through which students can pose questions to the instructor and other students. The audio feed will include the instructor and students in the classroom.
Software-based labs are completed using various applications that are downloaded or accessed remotely by the student. These labs are supported by live and recorded explanations and demonstrations by faculty and teaching assistants. In some classes, live support sessions are held online to assist students while they are completing the labs in their locations.
Labs that require tactile manipulation of instruments can be completed locally if the student has access to appropriate equipment (oscilloscope, function generator, multi-meter, etc.). Students record their experiment results and report back to the instructor. In some cases student may be asked to capture their work on video or still images.
Learning Outcomes:
- Produce a computational solution to a problem that is reproducible and can be comprehended by others in the same field.
- Communicate across disciplines and collaborate in a team.
- Model complex systems appropriately with consideration of efficiency, cost and data availability.
- Use computation for advanced data analysis.
- Create or enable a breakthrough in a domain in science.
- Take advantage of parallel and distributed computing and other emerging modes of computation, both in algorithms and in code implementation.
- Evaluate and compare multiple computational approaches to a scientific challenge and choose the most appropriate and efficient one.
- Apply techniques and tools from software engineering to build robust, reliable, and maintainable software.