FARR Workshop Agenda
Location:
AGU Conference Center
2000 Florida Ave. NW, Washington, D.C. 20009
Wednesday, October 9, 2024
8:00-9:00 am Breakfast/Registration
9:00-10:30 am Opening Plenary - Introduction to FARR
Presenters:
​Moderator:
Christine Kirkpatrick
SDSC, UCSD
Chandi Witharana
University of Connecticut
Satellite Imagery and AI in Action at Pan-Arctic Scale
Vandana Janeja
UMBC
​FAIR in Multi-disciplinary Spaces: Is the Data AI Ready, Shareable and Encourages Reproducibility
Philip Harris
MIT
​Building Cross-Disciplinary Scientific Deep Learning Challenges
10:30-11:00 am AM Break
11:00 am-12:15 pm Fully AI Ready Data
​
​Presenters:
​Moderator:
Christine Kirkpatrick
SDSC, UCSD
Jane Greenberg
Drexel University
The Surging Metadata Wave: Empowering AI with Semantic Systems
Erik Schultes
GO FAIR Foundation
FAIR for AI and AI for FAIR
Anupama (Anu) Gururaj
DAIT, NIAID, NIH
ImmPort@20: Getting ‘AI ready’ for AI
12:15-1:15 pm Lunch
1:15-2:00 pm AI Readiness - repository perspectives
​
​Presenters:
​Moderator:
Karen Stocks
SIO, UCSD
Doug Schuster
NCAR
Supporting ML/AI research through NSF NCAR's Emerging Data Commons Services
Tyler Christensen
NOAA/NESDIS
​AI-Ready Data at NOAA: a repository perspective
Martin Seul
CUAHSI Hydroshare
HydroShare AI Readiness – a (small) repository perspective
Christine Laney
NEON
Generating AI-ready data: perspectives from a continental-scale ecological observatory
2:00-2:45 pm AI Readiness - research perspectives
​
​Presenters:
​Moderator:
Karen Stocks
SIO, UCSD
Sanjib Sharma
Howard University
Advancing Earth Science Education Through Generative Artificial Intelligence
Srija Chakraborty
USRA
Monitoring Greenhouse Gas Emitters at Night with Machine Learning Insights on NASA’s Black Marble
Jazlynn Hall
Cary Institute
Preparing a time series database for applications in forest ecology and wildfire resilience in the Western U.S.
Denys Godwin
Clark University
​Mapping Rooftop Solar across New England
Wenjia Li
University of Idaho
​GeoSymbolNet: Leveraging Data Augmentation to Decipher Geological Map Symbols
Jian Gong
University of Wyoming
​Curating Multi-source Time Series Image Dataset for Tundra Lakes in the Siberian Arctic
2:45-3:00 pm Poster Lightning Talks
Presenters:
See poster session
​Moderator:
Daniel S. Katz
U. of Illinois Urbana-Champaign
3:00-4:00 pm Poster session / PM Break
Presenters:
Yogesh Bhattarai
Howard University
Integrating open-source geospatial data and machine learning for enhanced disaster resilience
Bridget Hass
NEON
NEON Remote Sensing Data in Google Earth Engine to Facilitate FAIR Environmental AI/ML Research
​
Owen Price Skelly
The University of Chicago
Garden: A FAIR Framework for Publishing and Applying AI Models for Translational Research in Science, Engineering, Education, and Industry
Ilya Zaslavsky
SDCS, UCSD
Architecting a data hub for modeling climate change effects on the water-food-energy-health nexus components in arid zones based on FAIR principles
Michael Cecil
University of Maryland
Assessing Smallholder Farmer Planting and Harvest Dates With Geospatial Foundation Models
Reyna Jenkyns
World Data System
Counteracting Concerns of Quality Inputs for AI Applications by Mobilizing Trusted Data Repositories to Demonstrate AI-Readiness
D. Sarkar, S. Lunawat
Global South in AI, UIUC
Unlearning Bias and Mitigating Security Risks in LLMs
​
Geoffrey Fox
University of Virginia
​Curating Multi-source Time Series Image Dataset for Tundra Lakes in the Siberian Arctic
​​​​
Christine Laney
NEON
Expanding heterogenous ecological data use in AI/ML applications
​​
Kirubel Biruk Shiferaw
University Medicine Greifswald
Calibrating reporting guidelines to foster reproducibility in medical AI research
Lydia Fletcher
TACC, U. of Texas at Austin
Leveraging Emerging AI Tools to Reduce the FAIR Workload
​
Josephine Namayanja
U. of Maryland, Baltimore County
A Preliminary Open Science Pipeline to Facilitate AI Reproducibility for Interdisciplinary Communities
​
Jianwu Wang
U of Maryland, Baltimore County
Reproducible and Portable Big Data Analytics in the Cloud
4:00-5:30 pm FAIR & AI Models
​ ​
Presenters:
​Moderator:
Geoffrey Fox
University of Virginia
Daniel S. Katz
U. of Illinois Urbana-Champaign
Introducing the FAIR for Machine Learning (FAIR4ML) RDA interest group
Rajat Shinde
U. of Alabama in Huntsville
​GeoCroissant- A Standardized Metadata Format for Geospatial ML-ready Datasets
Line Pouchard
Sandia National Laboratories​
The role of FAIR in data-intensive, reproducible workflows
​Satrajit Ghosh
MIT
Challenges in Performing FAIR and Reproducible Computation
Thursday, October 10, 2024
8:00-9:00 am Breakfast/Registration
9:00-10:30 am AI Reproducibility
​ ​
Presenters:
​Moderator:
Yuhan (Douglas) Rao
North Carolina State University
Odd Erik Gundersen
Norwegian U. of Sci & Tech
The fundamental principles of reproducibility
Kevin Coakley
SDSC, UCSD
Sources of Irreproducibility in Machine Learning
Elizabeth Campolongo
The Ohio State University
FAIR and Reproducible Data, Models, and Workflows in Imageomics
Roel Janssen
Delft University of Technology
Enabling reproducible, transparent and legally compliant AI in The Netherlands
10:30-11:00 am AM Break
11:00 am-12:30 pm Working session
​Moderator:
Yuhan (Douglas) Rao
North Carolina State University
12:30-1:30 pm Lunch
1:30-3:00 pm Future research directions/gaps
​ ​
Presenters:
​Moderator:
John Towns
U of Illinois Urbana-Champaign
Alejandro Suarez
National Science Foundation
Broadening Access to AI Resources through the National AI Research Resource (NAIRR) Pilot
Chaitan Baru
National Science Foundation
The Open Knowledge Network
Mark Musen
Stanford University
Representing Standards for FAIR Data in a Machine-Actionable Way
​
Christine Kirkpatrick
SDSC, UCSD
Title TBA
Wilbert van Panhuis
NIH/NIAID
Implementing FAIR and AI Ready Data for Biomedical Research: from Principles to Practice