David Smith Selected for NASA NIAC Fellowship
Award will support the pursuit of robotically assembled metamaterials in space for radar systems enabling long-range situational awareness.
Data Science, Advanced Computing & Intelligent Systems
Duke Engineering researchers are developing and deploying the power of computing to design autonomous systems, improve communications, glean useful insights from masses of data, detect disease and improve health, and enhance security in our world and cyberspace.
Major Centers and Initiatives
AI & Machine Learning
- AI/Machine Learning Group—Department of Electrical & Computer Engineering
- Computation & AI Group—Thomas Lord Department of Mechanical Engineering & Materials Science
- NSF AI Institute for Edge Computing Leveraging Next-Generation Networks (Athena)
Big Data & Data Analytics
Quantum Computing
- Duke Quantum Center
- Quantum Computing Group
- Error-corrected Universal Reconfigurable Ion-trap Quantum Archetype (Euriqa)
- Spectator Qubit Multidisciplinary University Research Initiative (MURI)
- Software-Tailored Architecture for Quantum (STAQ)
Computer Systems & Architecture
- NSF AI Institute for Edge Computing Leveraging Next-Generation Networks (Athena)
- Center for Alternative Sustainable and Intelligent Computing (ASIC)
Information Processing
Computational Design
- Computational Design:
- Center for Autonomous Materials Design
- Computational Modeling of Biological Systems Group
- Aerodynamics & Aeroelasticity Group
- Sensing & Imaging Group
- GUIde Consortium for Aeroelasticity
Trustworthy Computing
AI Risk in 2026: What Happens When Engineers Don’t Understand It
Every day, AI tools get more capable, and deployment is outpacing our understanding of what they get wrong. Duke Engineering Dean Jerry Lynch connects with three Duke University experts to break down where that gap is already showing up: in cybersecurity, in the everyday AI tools people use to get advice, and in healthcare.
Thwarting Hidden Resume Hacks Targeting AI Hiring Tools
An academic-industry collaboration identifies 1% of resumes in a 200,000 sample across multiple sectors as containing concealed instructions meant to exploit AI systems.