October 14, 2025
AI for Research Administration (AI4RA)
Bridging Worlds Between Research Administration and Artificial Intelligence
Join us for an exclusive free webinar hosted by the NSF-funded Artificial Intelligence for Research Administration (AI4RA) initiative. This session brings together data scientists working at the intersection of AI innovation and research administration (RA), helping institutions design solutions that are secure, accurate, reproducible, and adaptable to diverse environments.
🗓 October 14th 2025
⏰ 12pm Eastern
📍 Online (via Google Meets)
What You'll Learn
- How to distinguish between problems best solved by AI versus traditional data science approaches in RA
- Practical strategies for responsibly integrating AI into research administration workflows
- Lessons from real-world implementations of AI-powered solutions across university systems
- Approaches to ensure data interoperability, reproducibility, and scalability in RA innovation
- How RA professionals can collaborate with data scientists to build sustainable, institution-ready tools
Why Attend?
🔍 Gain clarity on where AI truly delivers value in RA workflows
🤖 Learn from data scientists actively implementing AI solutions in research offices
📊 Discover strategies to ensure solutions are reproducible, secure, and interoperable
🌐 Understand how open-source and enterprise AI can be adapted to diverse institutions
🚀 Walk away with actionable insights for leading innovation in your RA unit
Who Should Attend?
This webinar is designed for:
✅ Research Administrators and Strategy Leaders exploring AI integration
✅ Research Development and Sponsored Programs Professionals
✅ IT and Data Management Teams supporting RA systems
✅ Compliance and Research Integrity Officers evaluating AI adoption
✅ Institutional Leaders and Policy Makers in higher education
✅ Anyone seeking to responsibly introduce AI and data science into RA operations
About Our Hosts
Nathan Wiggins – Data Scientist, Southern Utah University Nathan leads the implementation of AI-powered solutions to streamline RA workflows, designing data integration strategies across multiple university systems and creating scalable, open-source tools for adoption nationwide. He ensures interoperability between internal and external data sources, deploys and fine-tunes large language models for RA use cases, and develops modular software systems for interactive data management, visualization, and dissemination.
Nathan Layman – Data Scientist, University of Idaho Nathan is a computational ecologist specializing in machine learning for complex biological problems. With expertise in Python, R, and C++, his experience spans individual-based modeling, natural language processing, and semantic image segmentation using convolutional neural networks. He is deeply committed to open, reproducible science and applies his broad technical expertise to advance cross-disciplinary, data-driven solutions in research administration.
Reserve your spot today
Don’t miss this opportunity to explore where AI and data science intersect with research administration—and learn how to implement innovation responsibly and effectively.