Conveners
Artificial Intelligence (AI) - II
- Daniele Bonacorsi (University of Bologna)
Description
During the last decade, Artificial Intelligence (AI) and statistical learning techniques have started to become pervasive in scientific applications, exploring the adoption of novel algorithms, modifying the design principles of application workflows, and impacting the way in which grid and cloud computing services are used by a diverse set of scientific communities. This track aims at discussing problems, solutions and application examples related to this area of research, ranging from R&D activities to production-ready solutions. Topics of interests in this track include: AI-enabled scientific workflows; novel approaches in scientific applications adopting machine learning (ML) and deep learning (DL) techniques; cloud-integrated statistical learning as-a-service solutions; anomaly detection techniques; predictive and prescriptive maintenance; experience with MLOps practices; AI-enabled adaptive simulations; experience on ML/DL models training and inference on different hardware resources for scientific applications.
Modern Earth Observation (EO) platforms integrate diverse distributed components and scientific workflows across heterogeneous cloud environments. Ensuring software security, maintainability and rapid delivery within such complex systems represents a major operational challenge. To address this, we developed an AI-assisted DevSecOps framework that augments continuous integration and deployment...
Modern scientific workflows increasingly rely on Machine Learning (ML) models whose development, deployment, and validation must meet high standards of reliability, transparency, and reproducibility. Yet many scientific ML pipelines still lack robust engineering practices, making experiments difficult to track, compare, and replicate. In this contribution, we present a structured MLOps...
With the widespread adoption of large language models (LLMs) like ChatGPT, AI has emerged as a transformative productivity tool across human industries. LLM-based agents—capable of autonomous task planning, tool utilization, and result explanation—have consequently become a hot point of recent research.
High-energy physics analysis presents a compelling AI4Science scenario. It features...
2025 is widely recognized as the Year of the AI Agent. Large language models have moved beyond conversational interfaces to become callable tools that boost productivity—evident in the rapid adoption of systems like Manus, Claude-Code, and Cursor. AI Agent technologies are also increasingly being applied in scientific research to assist in data analysis and literature exploration, as...
