M.S. in AI Engineering

Program Overview

The Master of Science in Artificial Intelligence (AI) Engineering program addresses the critical need for experts who understand AI fundamentals, can apply AI to solve physical-AI engineering problems, and have the skills to advance AI concepts, hardware and systems technologies.

Capitalizing on our unique strengths and interdisciplinary educational opportunities within the School of Engineering and Applied Science (SEAS) and the broader George Washington University (GW) communities, the M.S. in AI Engineering at GW builds on a strong foundation of hardware computing, physical engineering, and intelligence systems.

The Challenges:

While AI has already significantly impacted daily life, its progress is bottlenecked by unsustainably growing power, high-speed memory, and massive computational infrastructure needs. AI engineers capable of innovatively engendering better systems, from hardware chips to Data Centers, are needed.

While AI has been deployed to benefit consumer and business applications, industrial and physical applications of AI are growing rapidly and require a new breed of professionals with AI and Engineering strengths.

The Washington, D.C. Advantage

Due to the program's strategic location in the Washington, D.C. metropolitan area, graduates enjoy a distinct competitive edge. The curriculum aligns directly with the hiring needs of massive regional data center hubs, Fortune 500 defense and high-tech enterprises, and premier federal research entities. GW is located in the physical proximity of such organizations including the Naval Research Laboratory (NRL) and NASA Goddard Space Flight Center (GSFC), helping graduates prepare for long-term career opportunities.

Career Outcomes

By balancing career relevance, industry perspective, academic rigor and strong technical training, this curriculum prepares graduates to succeed in an evolving tech landscape. Rather than focusing solely on software algorithms, the program’s emphasis on Fundamentals, Physical AI and hardware-software co-design creates flexibility across AI and engineering career paths and provide the foundations for life-long learning.

Graduates are uniquely positioned to secure high-impact roles across the engineering, physics, and computer science sectors, including:

AI Infrastructure Engineer: Building and improving the computing systems and data centers that support large-scale AI applications.
Hardware / VLSI Design Engineer: Creating faster, more energy-efficient computer hardware designed to power AI technologies.
Edge AI & Connected Systems Specialist: Bringing AI capabilities to devices like smartphones, sensors, robotics, and other connected technologies.
Sustainable Energy & Smart Grid Specialist: Using AI to improve energy systems, support renewable power, and modernize critical infrastructure.
Systems / Security Engineer: Protecting AI systems and networks from cybersecurity threats while helping them run safely and reliably.

Program Requirements:

Total Credit Hours: 30
Program Structure: Core Courses (12 credits) + Area of Focus (12 credits) + Thesis courses OR 2 additional elective courses (6 credits)
Duration: Two years (full-time graduate student enrollment) or three years (part-time graduate student enrollment)

Required Courses (Select 4 out of 5 courses)

ECE 6005: Computer Architecture and Design

ECE 6105: Introduction to High-Performance Computing

ECE 6210: Machine Intelligence

ECE 6850: Pattern Recognition and Machine Learning

ECE 6882: Reinforcement Learning

Areas of Focus (Select 1 track from the list of areas of focus below)

AI Computing and Systems

Focuses on the computer hardware, computing systems, and technologies that make modern AI applications possible and efficient.

ECE 6125: Parallel Computer Architecture
ECE 6130: Big Data and Cloud Computing
ECE 6150: Design of Interconnection Networks for Parallel Computer Architectures
ECE 6217: Neural Networks and Hardware Implementations

Smart Grids and Sustainable AI

Explores how AI can support energy systems, power infrastructure, and sustainability efforts for the future of computing.

ECE 6070: Electrical Power Systems
ECE 6669: Smart Power Grids
ECE 6690: Power Systems Economics
ECE 6699: Energy and Sustainability

Cloud/Edge Intelligence & Security

Focuses on delivering AI applications across cloud, mobile, and connected systems while helping keep networks and data secure.

ECE 6035: Introduction to Computer Networks
ECE 6130: Big Data and Cloud Computing
ECE 6160: Secure Computing Systems
ECE 6565: Network Security

Thesis & Non-Option: Thesis and non-thesis options are available. Students who choose to complete a thesis take 24 credit hours of course work and 6 credit hours for thesis research. These 6 credit hours must be taken over two semesters.

Students who choose the non-thesis option complete two elective courses subject to faculty advisor approval for a total of 30 credit hours of coursework.

Admissions & Enrollment Policies:

At this time, Fall 2026 applications are being accepted only from students who do not require an F-1 visa to study in the United States.

Preferred bachelor's degree in biomedical engineering, electrical engineering, computer engineering, or computer science. An undergraduate background in stochastic processes, linear algebra, and computer organization is highly desirable.
May be admitted to the program with a bachelor's in a different field with a condition of taking deficiency courses.
Minimum of 3.0 GPA (on a 4.0 scale) or equivalent achieved at the time of bachelor's degree completion.
Successful submission of online application form, exam scores and other documents as outlined in the admissions requirements.