Christine Kirkpatrick

San Diego Supercomputer Center, University of California San Diego & GO FAIR US

Conference theme:

Pending

Abstract:

This session will discuss lessons learned and emerging community practices as they relate to AI Readiness (of data), AI Reproducibility, and the relationship between the FAIR Principles and Machine Learning (ML). Insights into ways to leverage Gen-AI and LLMs for improving data cleaning and organization for ML will be shared, as well as open research questions, and gaps in practices at the intersection of AI and data. This will include an update from the FAIR in Machine Learning, AI Readiness, (AI) Reproducibility Research Coordination Network (FARR).

Speaker biography:

Christine Kirkpatrick leads the Research Data Services division at the San Diego Supercomputer Center, supporting large-scale research infrastructure. Her work focuses on data-centric AI, aiming to improve efficiency and reduce power use and time to discovery. She is PI of the NSF-funded FAIR in ML RCN, advancing best practices and reproducibility in AI. Kirkpatrick also leads the NIAID FAIRification project, enhancing biomedical data metadata quality. She founded the GO FAIR US Office, serves on the Open Storage Network Executive Committee, and is Co-PI of the NSF-funded GRANDE-U project, supporting groundwater research in the Baltic states.