Program Overview

Electrical Engineering, MS

The Electrical and Computer Engineering Department, in the College of Engineering and Applied Sciences, is involved in graduate teaching and research in many areas, including communications and signal processing, networking, computer engineering, power engineering, semiconductor devices and quantum electronics, circuits and VLSI. The department has laboratories devoted to research and advanced teaching in the following areas: computing, engineering design methodology, high-performance computing and networking, parallel and neural processing, machine vision, fiber optic sensors and computer graphics, micro and optoelectronics, power electronics, electric power and energy systems, VLSI, telerobotics, DNA sequencing, digital signal processing, and communications.

Admission Requirements

• A bachelor’s degree in electrical or computer engineering or computer science from an accredited college or university.
• A minimum grade point average of B in all courses in engineering, mathematics, and science.
• GRE V150, Q159, WA3 (if required by the graduate school); TOEFL 80, IELTS 7 (for international applicants); 3 recommendation letters.

Degree Requirements

The MS degree in the Department of Electrical and Computer Engineering requires the satisfactory completion of a minimum of 30 graduate credits.

Non-Thesis Option

• 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, and ESE 698.
• Only 3 credits of ESE 698 and up to 3 credits of ESE 597.
• Any non-ESE course will need prior approval given by the Graduate Program Director before a student can register.

CORE Courses

• Minimum of eight (8) regular courses.
• Of these eight, at least seven (7) regular courses must be taken in the department; three of the seven must be selected from the following CORE Courses:
  • ESE 502 - Linear Systems
  • ESE 503 - Stochastic Systems
  • ESE 511 - Solid-State Electronics
  • ESE 538 - Nanoelectronics
  • ESE 516 - Integrated Electronic Devices and Circuits I
  • ESE 520 - Applied Electromagnetics
  • ESE 528 - Communication Systems
  • ESE 532 - Theory of Digital Communication
  • ESE 505 - Wireless Communications
  • ESE 545 - Computer Architecture
  • ESE 547 - Digital Signal Processing
  • ESE 554 - Computational Models for Computer Engineers
  • ESE 555 - Advanced VLSI Systems Design
  • ESE 566 - Hardware-Software Co-Design of Embedded Systems
  • ESE 587 - Hardware Architectures for Deep Learning

Thesis Option

• 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.
• 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 597, ESE 599, and ESE 698 and up to 3 credits of ESE 597 may be used.
• Only 3 credits of ESE 698 may be used.
• Any non-ESE course will need prior approval given by the Graduate Program Director before a student can register.

CORE Courses

• Minimum of six (6) regular courses.
• Of these six, at least five (5) regular courses must be taken in the department.
• Three of these five regular courses must be selected from the following CORE Courses:
  • ESE 502 - Linear Systems
  • ESE 503 - Stochastic Systems
  • ESE 511 - Solid-State Electronics
  • ESE 538 - Nanoelectronics
  • ESE 516 - Integrated Electronic Devices and Circuits I
  • ESE 520 - Applied Electromagnetics
  • ESE 528 - Communication Systems
  • ESE 532 - Theory of Digital Communication
  • ESE 505 - Wireless Communications
  • ESE 545 - Computer Architecture
  • ESE 547 - Digital Signal Processing
  • ESE 554 - Computational Models for Computer Engineers
  • ESE 555 - Advanced VLSI Systems Design
  • ESE 566 - Hardware-Software Co-Design of Embedded Systems
  • ESE 587 - Hardware Architectures for Deep Learning

Certificates

Networking & Wireless Communications Certificate

• Matriculated students only.
• Networking and wireless communications are key technologies in today’s technological world.
• 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.

Requirements

• 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 Communication
  • 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 Communications 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

Engineering Machine Learning Systems Certificate

• 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.

Requirements

• Foundations (1 required):
  • ESE 503 - Stochastic Systems
• Fundamental Methods (2 required):
  • ESE 588 - Fundamentals of Machine Learning
  • ESE 589 - Learning Systems for Engineering Applications
• Applications (1 out of three electives):
  • ESE 568 - Computer and Robot Vision
  • ESE 587 - Hardware Architectures for Deep Learning
  • ESE 590 - Practical Machine Learning and Artificial Intelligence
  • BMI 511 - Translational Bioinformatics
  • ESE 569 - Translational Bioinformatics

Engineering the Internet of Things Certificate

• 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.

Requirements

• Foundations (1 required):
  • ESE 566 - Hardware-Software Co-Design of Embedded Systems
• Basic Skills and Knowledge (2 required):
  • ESE 506 - Wireless Network
  • ESE 525 - Moden Sensors in Artificial Intelligence Applications
• Cloud and Security (1 out of two electives):
  • ESE 543 - Mobile Cloud Computing
  • ESE 544 - Network Security Engineering