top of page
Image by Maddison McMurrin

FAIR in ML, AI Readiness, & Reproducibility (FARR) Workshop

october workshop_edited.jpg

The FAIR in ML, AI Readiness, & Reproducibility Research Coordination Network (FARR RCN) computer scientists, geoscientists, research data practitioners, geosciences data and tool repositories / providers, and computing infrastructure providers and research tool builders to participate in FARR's in-person workshop on October 9-10, 2024 at the AGU Conference Center in Washington DC. Communities outside of geosciences with similar challenges, as well as industry, government, and non-profits with a stake in these topics are also encouraged to attend.

​

Who should attend?

  • Are you interested in learning about or working towards AI readiness?

  • Do you want to share AI models, or use others' models?

  • Are you concerned about reproducibility of work that uses AI models?

  • Do you have experiences in these areas that you think others would benefit from?

If you are interested in those questions, this workshop will be a good opportunity to connect with others.

 

Purpose:

The "FARR Workshop” aims to make advancement in the areas of AI Readiness, AI Reproducibility, and the intersection of the FAIR Principles and ML through

  • Spurring new or deepened collaborations

  • Sharing best practices and lessons learned

  • Exploring research gaps and priorities 

​​

​

 

Participate

 

Call For Posters - Abstracts are due by September 18, 2024

The FARR RCN seeks posters focused on AI Readiness, AI Reproducibility and FAIR Principles & ML, especially in the geosciences, including but not limited to topics such as:

  • Foundation models (FM), especially as they relate to building FMs, methods for benchmarking, maintaining, extending, data formats, and related considerations

  • Data-centric AI, especially as it relates to research priorities and signals for data repositories and resource providers

  • Using ML and Knowledge Engineering to add context or structure to data 

  • Applying the FAIR principles to data, workflows, and models for AI/ML, and techniques for automation and validation

  • What AI Readiness means for geoscience repositories and related providers: challenges, success stories, and lessons learned

  • Community approaches to AI reproducibility

  • AI reproducibility and refactoring for LLMs and Gen-AI

​

​

Logistics

Draft Agenda

 

Attendance at the meeting will be in-person only. There is no registration fee for this meeting. Breakfast and lunch will be provided. Travel support will be made available for a limited number of early career researchers. 

​​​

Accommodations: reservations due by September 18, 2024, while supplies last

​

Registration

There is no registration fee, but participants should register by September 18, 2024 to allow for planning logistics such as space, food and name badges. Register HERE

 

Venue

AGU Conference Center

2000 Florida Ave. NW, Washington, D.C. 20009

Directions to the AGU Conference Center

Closest Metro station: Dupont Circle

​

bottom of page