Artificial Intelligence

Advancing the future of Artificial Intelligence (AI) through responsible innovation, interdisciplinary research, and forward-thinking education.

Our researchers lead in a rapidly evolving AI field by combining technical depth with real-world problem solving, applied coursework, and industry collaboration. Faculty and students apply their expertise and education to technical and societal challenges, including detecting human trafficking hotspots, improving bruise detection across diverse skin tones, monitoring ecological systems, and strengthening cybersecurity against AI-driven scams.

Capability Areas

Machine Learning
Machine learning (ML) focuses on training algorithms to learn patterns from data and make predictions or decisions without explicit programming. It supports recommendation systems, fraud detection, and predictive maintenance applications.
Natural Language Processing
Natural language processing (NLP) allows computers to interpret and generate human language to support chatbots, translation systems, sentiment analysis, and text summarization.
Computer Vision
Computer vision (CV) enables machines to interpret visual information from images and video. Research areas include object detection, facial recognition, and medical image analysis.
Deep Learning
Deep learning (DL) uses multi-layered neural networks to model complex data relationships. It enables advanced capabilities in areas such as image recognition, speech processing, and natural language understanding.
Two students in a technology lab review AI data center software on a large display while standing next to server equipment and a workstation, demonstrating hands-on engineering and computing research.
Virginia AI Data Center Research Lab
Learn more about the research lab

The Virginia AI Data Center Research Lab focuses on sustainable digital infrastructure and AI-driven innovation through clean energy integration, grid resilience, and data center operations optimization.

Photo credit:
Ron Aira

AI in the News

Ethical AI Use in Academia

George Mason’s commitment to responsible AI in teaching, research, and institutional operations strengthens its academic programs by ensuring that innovation is paired with clear governance and security frameworks.

The university’s AI guidelines provide role-based guidance for students, faculty, and researchers that emphasize transparency, data privacy, academic integrity, and accountability in the development and use of AI technologies. George Mason prepares students not only to build advanced AI systems, but also to design and deploy them responsibly in real-world environments. enabling them to lead in industries where trust, compliance, and ethical AI development are increasingly essential.

Two students work with robotics equipment in a lab, with one student using a computer while another observes, demonstrating hands-on artificial intelligence and robotics research
Mason Autonomy and Robotics Center
Explore the MARC

Researchers at the Mason Autonomy and Robotics Center (MARC) develop autonomous systems and robotics technologies, and study human-AI collaboration and deployment of intelligent systems.

Photo credit:
Evan Cantwell
Academics and Education in AI
MS in Artificial Intelligence
Virginia’s first standalone MS in AI provides advanced technical and ethical training in AI policy, ethics, and society; advanced AI; scalable and secure AI infrastructures; and use-inspired AI.
Graduate Certificate in Responsible Artificial Intelligence
The graduate certificate in Responsible AI emphasizes ethical design, policy considerations, safety and risk theory, and responsible deployment of AI systems.
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Curriculum Integration

AI is embedded across engineering and computing curricula through courses that combine technical depth, hands-on development, and ethical analysis.

Foundational offerings such as Foundations of Applied AI introduce machine learning, intelligent systems, and human-centered AI concepts, while applied courses like AI Application Development and AI-Driven Big Data Essentials focus on building and deploying real-world AI solutions. Undergraduate and graduate electives, including applied generative AI, big data analytics, and ethical AI, ensure that technical skills are paired with policy awareness and practical experience, preparing graduates for interdisciplinary AI careers.

Degree Concentrations

Established engineering and computing degrees offer targeted concentrations that integrate AI expertise into their core disciplines.

  • Applied Computer Science, BS – Concentration in Artificial Intelligence (AI)
  • Operations Research, MS – Concentration in Artificial Intelligence (AI)
  • Applied Information Technology, MS – Concentration in Machine Learning Engineering (MLE)
  • Computer Engineering, MS – Concentration in Machine Learning and Intelligent Computing Architectures (MLIC)
  • Electrical Engineering, MS – Concentration in Machine Learning in Electrical Engineering (MLEE)
  • Cyber Security Engineering, MS – Concentration in Cyber Secure Artificial Intelligence Systems (CSAI)

Academic Departments

Information Sciences and Technology
Computer Science
Electrical and Computer Engineering
Systems Engineering and Operations Research