Ibrahem Alshybani

The CI-PIVOT project, a collaboration between Indiana University, Purdue, and Mississippi State, addresses the gap between complex HPC/AI resources and domain researchers by transforming science experts into embedded Cyberinfrastructure Professionals (CIPs). Through an 18-month cohort model, this project establishes a scalable framework for professionalizing the CIP role, providing insights into training that can be generalized across the broader research ecosystem.

Cyberinfrastructure Professionals (CIPs) play a critical role in enabling scalable, data-intensive, and collaborative science by bridging domain research and advanced computational systems. However, pathways into CIP roles remain poorly defined, particularly for domain scientists transitioning into research computing careers. This study presents a conceptual framework and empirical insights from the NSF-funded CI-PIVOT initiative under the Strengthening the Cyberinfrastructure Professionals Ecosystem (SCIPE) program. We articulate a staged pathway for CIP development, describe the structure and outcomes of an 18-month cohort-based training model, and synthesize key design principles for building sustainable CI workforce pipelines. These findings contribute toward formalizing CIP career trajectories and informing institutional strategies for workforce development in cyberinfrastructure-enabled science.

Cao et al. introduce PANDA, an AI model designed for the early detection of pancreatic ductal adenocarcinoma (PDAC) using non-contrast CT scans. While the model shows great promise, it faces several challenges. Notably, its training predominantly on East Asian datasets raises concerns about generalizability across diverse populations. Additionally, PANDA’s ability to detect rare lesions, such as pancreatic neuroendocrine tumors (PNETs), could be improved by integrating other imaging modalities. High specificity is a strength, but it also poses risks of false positives, which may lead to unnecessary procedures and increased healthcare costs. Implementing a tiered diagnostic approach and expanding training data to include a wider demographic are essential steps for enhancing PANDA’s clinical utility and ensuring its successful global implementation, ultimately shifting the focus from late diagnosis to proactive early detection.