Graduate School Bulletin

Requirements for the M.S. Degree in Computer Engineering

Admission to the MS program in Computer Engineering requires the student to have completed a Bachelor degree in Computer Engineering or Computer Science. Students with a Bachelor degree in Electrical Engineering could also be admitted if they have taken or will take the following courses or their equivalent:

ESE 345 Computer Architecture;

ESE 280 Embedded Microprocessor Systems Design I;

ESE 333 Real-Time Operating Systems.

A candidate for the master’s degree may petition to transfer a maximum of 12 graduate credits from another institution towards the master’s degree requirements. Students transferring from non-matriculated status are also limited to a maximum of 12 credits for the master’s degree.

I. Non-Thesis Option

1. At least 30 graduate credits with a cumulative and departmental grade point average of 3.0 or better. Among these 30 credits, up to six credits may be from combination of ESE 597, ESE 599, or ESE 698. Only 3 credits of ESE 698 and up to 3 credits of ESE 597 may be used. Any non-ESE course will need prior approval given by the Graduate Program Director before a student can register.

2. At least one (1) course from each of the following sub-areas:

Hardware:      

ESE 507 Advanced Digital System Design & Generation

ESE 536/CSE 626 Switching and Routing in Parallel and Distributed Systems,

ESE 545 Computer Architecture,

ESE 565 Parallel Processing Architectures

ESE 566 Hardware-Software Co-Design of Embedded Systems,

ESE 587 Hardware Architectures for Deep Learning.

Networking:      

ESE 505 Wireless Communications,

ESE 506 Wireless Network,

ESE 546 Networking Algorithms and Analysis,

ESE 548 Computer Networks,

CAD and VLSI:  

ESE 530 Computer Aided Design,

ESE 549 Advanced VLSI System Testing,

ESE 555 Advanced VLSI System Design,

ESE 556 VLSI Physical and Logic Design Automation,

ESE 575 Advanced VLSI Signal Processing Architecture.

At least two (2) courses from the sub-area:

Theory and Software:  

ESE 501: System Specification and Modeling

ESE 533: Convex Optimization and Eng. Applications.

ESE 534: Cyber Physical systems.

ESE 543: Mobile Cloud Computing

ESE 554 Computational Models for Computer Engineers

ESE 558: Digital Image Processing I

ESE 588 Pattern Recognition

ESE 589: Learning Systems for Engineering Applications

ESE 590: Practical Machine Learning and Artificial Intelligence

*CSE 506 Operating Systems

*CSE 510 Hybrid Systems

*CSE 548/AMS 542 Analysis of Algorithms

* Ability of ECE students to enroll into CSE and AMS courses cannot be guaranteed.

3. At least three (3) additional regular lecture based courses. ESE 597, ESE 599, ESE 697, ESE 698 and ESE 699 are not counted as regular courses. Topics course, ESE 670, can be counted only once as a regular course.

4. At least one (maximum three) credit of ESE 597. Graduate Program Director approval is required (see graduate student guide for details). In exceptional circumstances, the Graduate Program Director can approve a replacement of ESE 597 credit by ESE 599, ESE 699 or ESE 698.

II. Thesis Option

1. Students must inform the department in writing at the end of their first semester if they would like to choose the M.S. Thesis Option.

2. At least 30 graduate credits with a cumulative and departmental grade point average of 3.0 or better. Among these 30 credits, at least six credits of ESE 599, with a maximum of 12 credits total being taken from combination of ESE 599, ESE 597, or ESE 698. Only three credits of 698 and up to 3 credits of ESE 597 can be used. Any non-ESE course will need prior approval given by the Graduate Program Director before a student can register.

3. At least one (1) course from each of the following sub-areas:

Hardware:        

ESE 507 Advanced Digital System Design & Generation

ESE 536/CSE 626 Switching and Routing in Parallel and Distributed Systems,

ESE 545 Computer Architecture,

ESE 565 Parallel Processing Architectures

ESE 566 Hardware-Software Co-Design of Embedded Systems,

ESE 587 Hardware Architectures for Deep Learning

Networking:      

ESE 505 Wireless Communications,

ESE 506 Wireless Network,

ESE 546 Networking Algorithms and Analysis,

ESE 548 Computer Networks,

CAD and VLSI:  

ESE 530 Computer Aided Design

ESE 549 Advanced VLSI System Testing,

ESE 555 Advanced VLSI System Design,

ESE 556 VLSI Physical and Logic Design Automation,

ESE 575 Advanced VLSI Signal Processing Architecture.

At least two (2) courses from the sub-area:

Theory and Software:  

ESE 501: System Specification and Modeling

ESE 533: Convex Optimization and Eng. Applications.

ESE 534: Cyber Physical systems.

ESE 543: Mobile Cloud Computing

ESE 554 Computational Models for Computer Engineers

ESE 558: Digital Image Processing I

ESE 568 Computer Vision

ESE 588 Pattern Recognition

ESE 589: Learning Systems for Engineering Applications

ESE 590: Practical Machine Learning and Artificial Intelligence

*CSE 506 Operating Systems

*CSE 510 Hybrid Systems

*CSE 548/AMS 542 Analysis of Algorithms

* Ability of ECE students to enroll into CSE and AMS courses cannot be guaranteed.

4. At least one (1) additional regular lecture based course. ESE 597, ESE 599, ESE 697, ESE 698 and ESE 699 are not counted as regular courses. Topics course, ESE 670, can be counted only once as a regular course.

5. At least one (maximum three) credit of ESE 597. Graduate Program Director approval is required (see graduate student guide for details). In exceptional circumstances, the Graduate Program Director can approve a replacement of ESE 597 credit by ESE 599, ESE 699 or ESE 698.

6. Students must satisfactorily complete a thesis (see graduate student guide for details).

Requirements for the Ph.D. Degree in Computer Engineering

A. Major and minor area requirements

1. Major area requirement is satisfied by taking minimum of three (3) courses from a selected major area with minimum GPA of 3.5. See Graduate Student Guide for preapproved lists of courses for each area.

2. Minor area requirement is satisfied by taking courses from other areas (different from the selected major area) with minimum GPA of 3.0. Students with BS degree are required to take two (2) courses from other areas (one or two areas) while students with MS degree  are required to take one (1) course.

B. Course Requirements

1. A minimum of 14 regular courses (42 regular graduate course credits) beyond the BS degree (including courses taken to satisfy major and minor requirements). The choice must have the prior approval of the designated faculty academic advisor. Any non-ESE course will need prior approval given by the Graduate Program Director before a student can register.
2. The ESE 697 Practicum in Teaching (3 credits) is required to satisfy the teaching requirement. Students must be advance to candidacy in order to take this course.
3. The courses ESE 597, ESE 598, ESE 599, ESE 698, and ESE 699 are not counted as regular courses.
4. Courses presented under the title ESE 670 Topics in Electrical Sciences that have different subject matters, and are offered as formal lecture courses, are considered different regular courses but may not be counted more than twice.
5. Prior MS degree in ECE or related area can reduce the course requirements down to six (6) regular courses.

C. Advancement to Candidacy

After successfully completing all major/minor/course requirements (except ESE 697) the student is eligible to be recommended for advancement to candidacy. This status is conferred by the dean of the Graduate School upon recommendation from the chairperson of the department.

It is strongly recommended that doctoral students Advance to Candidacy within 2.5 years if admitted with a BS degree (after earning 42 regular course credits), or within 1.5 years if admitted with an MS degree (after earning 18 regular course credits).

D. Preliminary Examination

A student is expected to pass the preliminary examination within 1.5 years after advancement to candidacy. Both a thesis topic and the thesis background area are emphasized. Students must pass the Preliminary Examination at least ONE year prior to their Defense. See Graduate Student Guide for details.

E. Dissertation

The most important requirement for the Ph.D. degree is the completion of a dissertation, which must be an original scholarly investigation. The dissertation must represent a significant contribution to the scientific and engineering literature, and its quality must be compatible with the publication standards of appropriate and reputable scholarly journals.

F. Approval and Defense of Dissertation

The dissertation must be orally defended before a dissertation examination committee, and the candidate must obtain approval of the dissertation from this committee. The committee must have a minimum of four members at least three (3) of whom are faculty members from the Department including the research advisor and committee chair, as well as at least one (1) member from outside of the Department. (Neither the research advisor nor the outside member may serve as the chair). On the basis of the recommendation of this committee, the dean of engineering and applied sciences will recommend acceptance or rejection of the dissertation to the dean of the Graduate School. All requirements for the degree will have been satisfied upon the successful defense of the dissertation.

G. Residency Requirement

The student must complete two consecutive semesters of full-time graduate study. Full-time study is 9 credits minimum per semester.

H. Time Limit

All requirements for the Ph.D. degree must be completed within seven (7) years after completing 24 credits of graduate courses in the department.

Certificates

Admission to the certificate programs is limited to students enrolled in either the MS or PhD programs in the Department of Electrical & Computer Engineering. Students may receive the certificate if they have no more than 12 graduate credits in the department as of the start of Spring 2018.

To apply for the Certificate Program, a student must complete the “Permission to Enroll in a Secondary Certificate Program” form (which requires signatures) from the Graduate School website, and submit it within the first week of the semester when they start the certificate.

1. Networking & Wireless Communications Certificate

Matriculated students only.

Networking and wireless communications are key technologies in today’s technological world.  Networks such as the Internet as well as telephone, cable and wireless networks serve to interconnect people and computers in a ubiquitous and cost effective way.  The area of wireless communications in particular has grown rapidly in recent years and has utilized networking technology to be successful.  There is a large industrial base involving networking and wireless communications in terms of equipment and software providers, service providers and end users.  Moreover this technology has made the average consumer’s life more productive, flexible and enjoyable.

The Stony Brook Certificate Program in Networking and Wireless Communications is designed to give matriculated students validated graduate level instruction in this area of much recent interest. The program can be completed in a reasonable amount of time as it involves only four courses. These are regular Stony Brook graduate level courses taught by Stony Brook faculty. The SUNY approved certificate program can be tailored to the needs of the individual student. Courses used for the certificate program can also be used toward the MS or PhD degree by matriculated students.

To receive the Stony Brook Certificate in Networking and Wireless Communications, a student must complete FOUR required courses as specified below, with at least a B grade in each course.

At least ONE course from the following:

ESE 505: Wireless Communications
ESE 506: Wireless Network

At least ONE course from the following:

ESE 532: Theory of Digital Communications
ESE 546: Networking Algorithms and Analysis
ESE 548: Computer Networks

In addition to the above, if needed, courses may be selected from:

ESE 503:  Stochastic Systems
ESE 504:  Performance Evaluation of Communication and Computer Systems
ESE 522:  Fiber Optic Systems
ESE 528:  Communication Systems
ESE 531:  Statistical Learning and Inference
ESE 536:  Switching and Routing in Parallel and Distributed Systems
ESE 543:  Mobile Cloud Computing
ESE 544:  Network Security Engineering
ESE 547:  Digital Signal Processing
ESE 550:  Network Management and Planning
ESE 552:  Interconnection Networks

Students must request the Certificate from Professor Thomas Robertazzi via Email Thomas.Robertazzi@stonybrook.edu once the program is completed.

2. Engineering Machine Learning Systems

Matriculated students only.

The Engineering Machine Learning Systems certificate program educates about the mathematical theory, fundamental algorithms, and optimized engineering of computational learning systems used in real-world, big data applications. Students will also study modern technologies used in devising such data systems, including software tools, architectures, and related hardware structures. Comprehensive, hands-on student projects on designing, implementing, and testing real-world learning systems are part of the certificate program. The certificate program includes a total of four courses: three required courses and one elective course. 

To receive the Stony Brook certificate in the Engineering Machine Learning Systems, a student must be currently enrolled in an MS or PhD program in the Electrical and Computer Engineering Department and must complete four courses as specified below, with at least a B grade in each course.

Foundations (1 required):                               ESE 503 Stochastic Systems

Fundamental Methods (2 required):               ESE 588 Fundamentals of Machine Learning

                                                                        ESE 589 Learning Systems for Engineering Systems

Applications (1 out of three electives):          ESE 568 Computer and Robot Vision,
                                                                         ESE 587 Hardware Architectures for Deep Learning,
                                                                         ESE 590 Practical Machine Learning
                                                                         BMI 511/ESE 569 Translational Bioinformatics 

3. Engineering the Internet of Things

Matriculated students only.

The Engineering the Internet-of-Things certificate program provides the fundamental principles, popular technologies and optimized engineering of Internet-of-Things applications and systems. Students gain a broad set of skills and knowledge for IoT development and innovation, including sensors and interfaces, RF communication, microcontroller and embedded systems, wireless radios, network protocols, cloud services and security techniques. Students learn how to design, implement and evaluate IoT systems and applications through hands-on projects on popular embedded system hardware. The certificate program includes a total of four courses: three required courses and one elective course.    

To receive the Stony Brook certificate in the Engineering the Internet-of-Things, a student must be enrolled in an MS or PhD program in the Electrical and Computer Engineering Department and must complete four courses as specified below, with at least a B grade in each course.

Foundations (1 required):                   ESE 566 Hardware Software Co-design for Embedded Systems

Basic Skills and Knowledge (2 required):        ESE 506 Wireless Network
                                                                          ESE 525 Modern Sensors in Artificial Intelligence: Applications

Cloud and Security (1 out of two electives):   ESE 543 Mobile Cloud Computing
                                                                         ESE 544 Network Security Engineering

After completing the necessary courses, students must request/apply for completion of the certificate program on SOLAR or through the Graduate School website.