To set up students, faculty and staff for success in an increasingly AI-driven society, NC State’s College of Engineering is integrating AI into its full breadth of curricula and research.
This past fall, Louis Martin-Vega Dean of Engineering Jim Pfaendtner launched the College of Engineering’s Applied AI Initiative. With a cross-disciplinary approach, the initiative aims to ensure that all NC State engineering and computer science faculty, staff and students understand AI’s potential for contributing to the advancement of their fields. The initiative builds on the college’s thematic research strengths, including fundamental AI algorithms and theories, robotics and automation, smart systems, sensors and materials and macromolecules.
“We want to equip our students with a foundational understanding of how to most effectively apply AI to problem-solving so that they leave NC State fully prepared as leaders in a rapidly changing workforce,” Pfaendtner said.
Applied AI Symposium Launches Vision for Future
In September, the college brought together industry leaders and faculty, staff and students to discuss where the college is headed with using AI in classrooms, research and work.
An applied AI committee composed of faculty members from all of the college’s departments is helping to advise curriculum changes, faculty hiring and planning for future labs and classrooms.
Several common themes emerged from the talks and discussions, including data-driven engineering, communication and collaboration, scaling up infrastructure and personalized learning.
On the Cutting Edge
Graduate students presented 90 posters demonstrating their work applying AI to a wide range of disciplines. Posters mentioned guide dogs, agriculture, robotic limbs, historical Arabic documents, yams, autonomous vehicles, materials discovery and more.
Improving Efficiency of DNA-Based Storage
Gunavaran Brihadiswaran, a third-year electrical engineering Ph.D. student, talked about his research to use machine learning algorithms to attempt to predict DNA bindings in a large-scale system.
“Let’s say you want to store one exabyte of data, which is [one billion gigabytes],” he said. “We need two conventional data centers’ worth of space with the current technology, but with DNA, we only need one cubic inch.”
While more work needs to be done, Brihadiswaran and his fellow researchers found the convolutional neural network (CNN) classifier, which is often used for image classification, to be a promising option.
Predicting Guide Dog Aptitude
Guide dogs help thousands of people with visual disabilities in their daily lives, but training them is a costly and time-consuming process. Researchers at NC State are using a smart collar to collect behavioral and physiological data to study gait patterns from puppies, and AI can help speed up and improve data analysis.
Yifan Wu, a fourth-year Ph.D. student in electrical engineering, has a very close research partner for this work: his dog, Happy.
“She’s in almost all of our alpha testing phases,” he said.
Working in collaboration with Guiding Eyes for the Blind, this ongoing study has assessed more than 500 candidate puppies.
Dean’s Distinguished Seminar Series: Applied AI Futures
The Dean’s Distinguished Seminar series is bringing AI leaders to campus for talks on relevant areas of applied AI, including process control, supply chain and manufacturing, agriculture and food safety, and health and biomedical applications.
On November 4, 2024, the college welcomed Markus J. Buehler, Professor of Civil and Environmental Engineering at MIT, as the keynote speaker for the inaugural Applied AI Futures Seminar Series. His seminar focused on the exciting ways applied AI can enhance discovery and solve complex, multiscale problems in advanced materials. In one example, he talked about using AI to find common structural similarities between music and materials science.
Upcoming Seminars
March 18, 2025: Maria Gini
• Distinguished Professor of Computer Science and Engineering, University of Minnesota
April 21, 2025: Sergei Kalinin
• Weston Fulton Professor of Materials Science and Engineering, University of Tennessee, Knoxville
• Chief Scientist, AI/ML for Physical Sciences, Pacific Northwest National Laboratory
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