03-18, 18:30–18:40 (US/Eastern), CGIS Concourse
Integrating interdisciplinary collaboration in AI courses enables students to apply AI to real-world problems across diverse fields. In my AI classes, students partner with faculty from various departments, using OpenOnDemand for GPU-based computations to develop AI solutions. This hands-on approach includes projects like rainfall prediction, wildlife imagery classification, and healthcare trend analysis. OpenOnDemand supports these projects enabling students to tackle data-intensive challenges. This framework builds technical skills and highlights AI’s impact across disciplines, preparing students to innovate beyond traditional boundaries. This poster showcases the work done with OpenOnDemand across 7 different disciplines to show the interdisciplinary power of OpenOnDemand.
This presentation examines integrating interdisciplinary collaboration into AI courses, using OpenOnDemand for GPU-based computations to support data-intensive, real-world projects. In my AI classes, students collaborate with faculty from different departments, applying machine learning and data analysis techniques to solve complex problems while connecting their work to other disciplines. OpenOnDemand provides students with high-performance computing access, enabling them to handle large datasets and train models efficiently, making interdisciplinary AI projects feasible and scalable.
Key projects include:
- Earth and Ocean Sciences: Predicting rainfall in the western USA, classifying wildlife in imagery, and assessing water quality using satellite data.
- Environmental Sciences: Examining the impact of concentrated animal feeding operations (CAFOs) on communities in North Carolina.
- Exercise Science: Analyzing factors affecting divers’ balance using force plate data.
- Healthcare Administration: Predicting access points in electronic health records and deriving insights from American Hospital Association survey data.
- Nursing: Identifying depression patterns among parents of children with special healthcare needs.
- Physics and Physical Oceanography: Detecting building damage through satellite imagery.
- English: Analyzing literary depictions of the Wilmington Massacre of 1898 with natural language processing.
- History: Studying the long-term effects of the 1898 Wilmington Massacre on Black communities in Wilmington.
Through these projects, students gain hands-on experience in developing AI solutions tailored to specific domains. This interdisciplinary approach expands their understanding of AI’s applications beyond computer science, helping them to see how AI can drive innovation across a variety of contexts. By applying AI to real-world data, students enhance their technical expertise and develop an appreciation for cross-disciplinary research, equipping them to tackle diverse challenges with AI solutions.
This model prepares students to approach AI with a holistic perspective, fostering skills that allow them to innovate at the intersection of AI and other fields, addressing complex societal and academic challenges.
Gulustan Dogan is an assistant professor at University of North Carolina Wilmington in Computer Science department. She worked at Yildiz Technical University, Istanbul, Turkey as an Associate Professor. She worked at NetApp and Intel as a software engineer in Silicon Valley. She received her PhD degree in Computer Science from City University of New York. She received her B.Sc degree in Computer Engineering from Middle East Technical University, Turkey. She is one of the founding members of Turkish Women in Computing (TWIC), a Systers community affiliated with Anita Borg Institute. She also serves as Wilmington Ambassador of Women In Data Science Stanford.