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
Title TBA
Vandana Janeja
UMBC
Title TBA
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
Title TBA
Erik Schultes
GO FAIR Foundation
Title TBA
Anupama (Anu) Gururaj
DAIT, NIAID, NIH
Title TBA
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 Chrisensen
NOAA
Title TBA
Martin Seul
CUAHSI Hydroshare
Title TBA
Christine Laney
NEON
Title TBA
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
2:45-4:00 pm Poster session / PM Break
Presenters:
Yogesh Bhattarai
Howard University
Integrating open-source geospatial data and machine learning for enhanced disaster resilience
Geoffrey Fox
University of Virginia
Curating Multi-source Time Series Image Dataset for Tundra Lakes in the Siberian Arctic
Josephine Namayanja
U. of Maryland, Baltimore County
A Preliminary Open Science Pipeline to Facilitate AI Reproducibility for Interdisciplinary Communities
Michael Cecil
Virginia Tech
Assessing Smallholder Farmer Planting and Harvest Dates With Geospatial Foundation Models
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
S. Debika, L. Sarkar
Global South in AI, UIUC
Unlearning Bias and Mitigating Security Risks in LLMs
Reyna Jenkyns
World Data System
Counteracting Concerns of Quality Inputs for AI Applications by Mobilizing Trusted Data Repositories to Demonstrate AI-Readiness
Jianwu Wang
U of Maryland, Baltimore County
Reproducible and Portable Big Data Analytics in the Cloud
Lydia Fletcher
TACC, U. of Texas at Austin
Leveraging Emerging AI Tools to Reduce the FAIR Workload
Christine Laney
NEON
Expanding heterogenous ecological data use in AI/ML applications
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
4:00-5:30 pm FAIR & AI Models
Presenters:
Moderator:
Geoffrey Fox
University of Virginia
Daniel S. Katz
U. of Illinois Urbana-Champaign
FAIR in ML - RDA IG
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
Jessica Forde
Brown University
Title TBA
Odd Erik Gundersen
Norwegian U. of Sci & Tech
Title TBA
Roel Janssen
Delft University of Technology
Enabling reproducible, transparent and legally compliant AI in The Netherlands
Elizabeth Campolongo
The Ohio State University
FAIR and Reproducible Data, Models, and Workflows in Imageomics
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
Katie Antypas
NSF
Broadening Access to AI Resources through the National AI Research Resource (NAIRR) Pilot
Mark Musen
Stanford University
Title TBA
Wilbert van Panhuis
NIH/NIAID
Implementing FAIR and AI Ready Data for Biomedical Research: from Principles to Practice
TBA
TBA
Title TBA