General Information
Details
Dr. Roy’s research lies at the intersection of artificial intelligence, computing education, and data-driven modeling of real-world systems. Her current work explores how generative AI and machine learning can enhance computing education, including curriculum design, assessment, and decision-making at scale, while addressing challenges such as over-reliance, bias, and hallucinations. She also maintains interdisciplinary research interests in AI applications for infrastructure resilience, building on her doctoral training in computational science and engineering. Across these areas, her research emphasizes practical impact, sustainability, and the thoughtful deployment of AI in educational and societal contexts.
Dr. Roy’s teaching interests focus on software engineering, machine learning, natural language processing, and capstone design, with an emphasis on preparing students for real-world, large-scale computing practice. She teaches undergraduate and graduate courses across in-person, hybrid, and online formats, including offerings in Georgia Tech’s OMSA program. A central theme of her teaching is the responsible integration of generative AI into the software development lifecycle, where students learn to use AI tools for design, implementation, and testing while maintaining strong engineering judgment, code quality, and ethical awareness. Her courses emphasize design principles, sustainability considerations, teamwork, and scalable feedback mechanisms, with a particular interest in effective pedagogy for large enrollment and project-based courses.
Recent Publications
- Roy, N., Horielko, O., & Olufisayo, O. (2026, February). Benchmarking AI Tools for Software Engineering Education: Insights into Design, Implementation, and Testing. Accepted in SIGCSE 2026.
- Roy, N., Horielko, O., & Omojokun, O. (2025, October). Integrating AI Tools in Advanced Computer Science Curricula: A Case Study of Course Redesign. In Proceedings of the ACM Global on Computing Education Conference 2025 Vol 1 (pp. 92-98). https://doi.org/10.1145/3736181.374713
- Roy, N., Borela, R., & Omojokun, O. (2025, October). Integrating Sustainability into Software Engineering Education: A Course Redesign Initiative. In Proceedings of the ACM Global on Computing Education Conference 2025 Vol 1 (pp. 176-182). https://doi.org/10.1145/3736181.3747135
- Borela, R., Soylu, M. Y., Lee, J., & Roy, N.(2025, August).What Computing Faculty Want: Designing AI Tools for High-Enrollment Courses Beyond CS1. In Proceedings of ACM Conference on International Computing Education Research https://doi.org/10.1145/3702653.3744327
- Roy, N., Horielko, O., & Olufisayo, O. (2025, August).Benchmarking of Generative AI Tools in Software Engineering Education: Formative Insights for Curriculum Integration. In Proceedings of ACM Conference on International Computing Education Research https://doi.org/10.1145/3702653.3744328
- Alavala, N., Roy, N., McDaniel, M., Roozbahani, M. M., Borela, R., & Babolhavaeji, P. (2025, June). Beyond Buzzwords: Making Sustainability a Pillar of the Computing Curriculum. In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 389-395). https://doi.org/10.1145/3724363.3729034
- Liding, Z. C., Roy, N., & Borela, R. (2025, June). Tracking the Progression of Errors Across Successive CS1 Code Submissions. In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2 (pp. 801-801). https://doi.org/10.1145/3724389.3730809
- Roy, N., Olufisayo, O., & Horielko, O. (2025, February). Empowering Future Software Engineers: Integrating AI Tools into Advanced CS Curriculum. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1747-1747). https://doi.org/10.1145/3641555.3705074
- Roy, N., Olufisayo, O., & Tu, H. (2025, February). Scaling Academic Decision-Making with NLP: Automating Transfer Credit Evaluations. In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2 (pp. 1603-1604). https://doi.org/10.1145/3641555.3705245
- Zhang, D., Roy, N., Wang, R., & Frost, J. D. (2025). Predicting tornado-induced building damage: A comparative study of tree-based models and graph neural networks. International Journal of Disaster Risk Reduction, 105525. https://doi.org/10.1016/j.ijdrr.2025.105525
- Zhang, D., Huang, H., Smith, N. S., Roy, N., & Frost, J. D. (2025). From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images. arXiv preprint arXiv:2507.02781. https://doi.org/10.48550/arXiv.2507.02781
- Huang, H., Zhang, D., Masalava, A., Roozbahani, M. M., Roy, N., & Frost, J. D. (2025). Enhancing the fidelity of social media image data sets in earthquake damage assessment. Earthquake Spectra. https://doi.org/10.1177/87552930251335649
Creative & Professional Development Activities
- Participant and contributor, ACM SIGCSE, ITiCSE, ICER, and CompEd conferences (2023–2026), with focus on generative AI in computing education, curriculum redesign, and scalable assessment.
- Design and implementation of AI-integrated curricula and assessment frameworks for undergraduate and graduate computing courses, including capstone and large-enrollment software engineering offerings.
- Development of AI-supported instructional tools and workflows for curriculum design, transfer credit evaluation, and academic decision-making at scale.
- Leadership of online and hybrid instructional initiatives, including course development and delivery for large-scale programs.