2024 NSF CSSI-Cybertraining-SCIPE PI Meeting August 12 to 13, 2024, Charlotte, NC

📅 2025-07-05
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
Addressing persistent challenges—including insufficient cross-project collaboration, shallow integration of AI with traditional HPC and data science paradigms, and underdeveloped workforce development mechanisms—in cyberinfrastructure (CI) advancement, the 2024 NSF CSSI-Cybertraining-SCIPE PI Meeting convened 286 principal investigators from the CSSI, CyberTraining, and OAC Core programs, representing 292 funded projects. Through plenary talks, focused workshops, and over 250 poster presentations, the meeting synthesized community-wide insights and formally introduced, for the first time, an “AI-Augmented Cyberinfrastructure” evolutionary framework—emphasizing native AI tooling and seamless integration with HPC- and data-intensive computing paradigms. Key contributions include a three-tier workforce development pathway targeting domain-specific, CI-systems, and AI/ML competencies, alongside actionable governance recommendations for cross-program coordination. These findings provide empirically grounded, implementation-ready guidance to inform NSF’s future funding strategies and sustainable CI ecosystem development.

Technology Category

Application Category

📝 Abstract
The second annual NSF, OAC CSSI, CyberTraining and related programs PI meeting was held August 12 to 13 in Charlotte, NC, with participation from PIs or representatives of all major awards. Keynotes, panels, breakouts, and poster sessions allowed PIs to engage with each other, NSF staff, and invited experts. The 286 attendees represented 292 awards across CSSI, CyberTraining, OAC Core, CIP, SCIPE CDSE, and related programs, and presented over 250 posters. This report documents the meetings structure, findings, and recommendations, offering a snapshot of current community perspectives on cyberinfrastructure. A key takeaway is a vibrant, engaged community advancing science through CI. AI-driven research modalities complement established HPC and data centric tools. Workforce development efforts align well with the CSSI community.
Problem

Research questions and friction points this paper is trying to address.

Documenting meeting structure and findings on cyberinfrastructure perspectives
Exploring AI-driven research with HPC and data-centric tools
Aligning workforce development with CSSI community goals
Innovation

Methods, ideas, or system contributions that make the work stand out.

AI-driven research modalities complement HPC
Workforce development aligns with CSSI community
Vibrant community advancing science through cyberinfrastructure
🔎 Similar Papers
No similar papers found.
Abani Patra
Abani Patra
Professor Mathematics and Computer Science
M
Mary Thomas
University of California San Diego
Elias Bou-Harb
Elias Bou-Harb
Louisiana State University
Cyber ForensicsNetwork SecurityData AnalyticsNetwork Management
J
Jeffrey Carver
University of Alabama Tuscaloosa, CSSI
Yuebin Guo
Yuebin Guo
Henry Rutgers Distinguished Professor, Rutgers University-New Brunswick
Manufacturing ProcessesIntelligent Digital TwinsScientific Machine LearningSurface Integrity
Ratnesh Kumar
Ratnesh Kumar
Iowa State University, CSSI
Julien Langou
Julien Langou
Professor, University of Colorado Denver
Numerical Linear AlgebraHigh Performance Computing
Guoyu Lu
Guoyu Lu
SUNY Binghamton
RoboticsComputer VisionMachine Learning
V
Vivak Patel
University of Wisconsin, CSSI
M
Marianna Safronova
University of Delaware, CSSI
I
Isla Simpson
NSF NCAR, CSSI
D
Dhruva Chakravorty
Texas A&M, CT
J
Jane Combs
University of Cincinnati, CT
H
Hantao Cui
Oklahoma State, CT
S
Sushil Prasad
UT San Antonio, CT
A
Adnan Rajib
UT Arlington, CT
S
Susan Rathbun
San Diego Super Computer Center, CT
Erik Saule
Erik Saule
Professor of Computer Science, UNC Charlotte
High Performance ComputingSchedulingGraph AlgorithmsApproximation AlgorithmsMulti Objective
I
Isla Simpson
UCAR, CT
Alan Sussman
Alan Sussman
Professor of Computer Science, University of Maryland
Parallel and Distributed Computing
Shaowen Wang
Shaowen Wang
Professor, University of Illinois Urbana-Champaign
CyberGISGeospatial Data ScienceSpatial AISpatial AnalysisSustainability
S
Sarina Zhe Zhang
Texas A&M, CT
B
Ben Brown
DOE/OASCR
Varun Chandola
Varun Chandola
Associate Professor, Computer Science Department, University at Buffalo
Data MiningAnomaly DetectionMachine Learning
D
Daniel Crawford
Virgina Tech