arXiv subjects

Stephen M. Fiore

Publications and source records attributed to Stephen M. Fiore.

8 recordsLinked to original sources

arXiv2026

Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, the second in a three-year series focused on strengthening scientific computing ecosystems through socio-technical co-design. Workshop discussions identified four interdependent strategic themes: software ecosystems for AI-enabled scientific discovery; trust, validation, and traceability; human-AI teaming and paradigm shifts; and workforce, pedagogy, and governance. The report translates these themes into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and sustainability, and evaluation of scientific value. Together, these priorities outline directions for building scientific computing ecosystems that remain trustworthy, sustainable, innovative, and resilient as AI assumes a growing role in scientific work.

cs.CE↗

arXiv2026

Speed and impact of team science during urgent societal events

Urgent societal events demand scientific responses that are both rapid and impactful. Through an adversarial collaboration, we connected bibliometric databases to evaluate the speed and impact of over 2 million scientific publications in the three years following 48 urgent societal events. A pilot analysis of three cases -- the 2022 release of ChatGPT, the 2019 COVID-19 pandemic, and the 2001 World Trade Center attacks -- yielded unexpected patterns: larger teams were not only more impactful but also quicker to publish. More precisely, increases in team size were associated with (a) initial increases, but eventual diminishing returns in academic citations, (b) curvilinear returns in news and policy document citations, and (c) curvilinear returns in terms of how quickly papers were published. In other words, there are points where further increases in team sizes are either marginally helpful (diminishing returns) or counterproductive (curvilinear returns). To evaluate robustness, we pre-registered a broader test covering 45 additional events spanning two decades.

cs.DL↗

arXiv2025

Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collaborative scientific software ecosystems. To address urgent challenges at the intersection of high-performance computing (HPC), AI, and scientific software, participants envisioned agile, robust ecosystems built through socio-technical co-design--the intentional integration of social and technical components as interdependent parts of a unified strategy. This approach combines advances in AI, HPC, and software with new models for cross-disciplinary collaboration, training, and workforce development. Key recommendations include building modular, trustworthy AI-enabled scientific software systems; enabling scientific teams to integrate AI systems into their workflows while preserving human creativity, trust, and scientific rigor; and creating innovative training pipelines that keep pace with rapid technological change. Pilot projects were identified as near-term catalysts, with initial priorities focused on hybrid AI/HPC infrastructure, cross-disciplinary collaboration and pedagogy, responsible AI guidelines, and prototyping of public-private partnerships. This report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven to drive discovery, expand access, strengthen the workforce, and accelerate scientific progress.

cs.CE↗

arXiv2021

Metrics and Mechanisms: Measuring the Unmeasurable in the Science of Science

What science does, what science could do, and how to make science work? If we want to know the answers to these questions, we need to be able to uncover the mechanisms of science, going beyond metrics that are easily collectible and quantifiable. In this perspective piece, we link metrics to mechanisms by demonstrating how emerging metrics of science not only offer complementaries to existing ones, but also shed light on the hidden structure and mechanisms of science. Based on fundamental properties of science, we classify existing theories and findings into: hot and cold science referring to attention shift between scientific fields, fast and slow science reflecting productivity of scientists and teams, soft and hard science revealing reproducibility of scientific research. We suggest that interest about mechanisms of science since Derek J. de Solla Price, Robert K. Merton, Eugene Garfield, and many others complement the zeitgeist in pursuing new, complex metrics without understanding the underlying processes. We propose that understanding and modeling the mechanisms of science condition effective development and application of metrics.

physics.soc-ph↗

arXiv2021

Influence Dynamics Among Narratives: A Case Study of the Venezuelan Presidential Crisis

It is widely understood that diffusion of and simultaneous interactions between narratives -- defined here as persistent point-of-view messaging -- significantly contributes to the shaping of political discourse and public opinion. In this work, we propose a methodology based on Multi-Variate Hawkes Processes and our newly-introduced Process Influence Measures for quantifying and assessing how such narratives influence (Granger-cause) each other. Such an approach may aid social scientists enhance their understanding of socio-geopolitical phenomena as they manifest themselves and evolve in the realm of social media. In order to show its merits, we apply our methodology on Twitter narratives during the 2019 Venezuelan presidential crisis. Our analysis indicates a nuanced, evolving influence structure between 8 distinct narratives, part of which could be explained by landmark historical events.

cs.SI↗

arXiv2021

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps in understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

cs.SI↗

arXiv2020

Analyzing the Productivity of GitHub Teams based on Formation Phase Activity

Our goal is to understand the characteristics of high-performing teams on GitHub. Towards this end, we collect data from software repositories and evaluate teams by examining differences in productivity. Our study focuses on the team formation phase, the first six months after repository creation. To better understand team activity, we clustered repositories based on the proportion of their work activities and discovered three work styles in teams: toilers, communicators, and collaborators. Based on our results, we contend that early activities in software development repositories on GitHub establish coordination processes that enable effective collaborations over time.

cs.SE↗

arXiv2018

Towards a computational model of social norms

We describe a computational model of social norms based on identifying values that a certain culture finds desirable such as dignity, generosity and politeness. The model quantifies these values in the form of Culture-Sanctioned Social Metrics (CSSMs) and treats social norms as the requirement to maximize these metrics from the perspective of the self, peers and public. This model can be used to create realistic social simulations, to explain or predict human behavior in specific scenarios, or as a component of robots or agents that need to interact with humans in specific social-cultural settings. We validate the model by using it to represent a complex deception scenario and showing that it can yield non-trivial insights such as the explanation of apparently irrational human behavior.

cs.CY↗