We welcome submissions presenting original research that explores the interplay between AI/ML and system engineering. Our focus includes (but is not limited to) the following pivotal topics:

Scalable & Efficient AI-ML

Focuses on agentic AI systems, AIOps efficiency, and distributed, federated, or decentralized learning. Topics include high-performance, robust, secure, and energy-efficient systems, as well as root-cause analysis and auto-scaling for deployments on-premise, in the cloud, or at the edge.

System Architectures

Advanced hardware platforms (CPU, GPU, accelerators, edge devices) enabling improved cost, performance, and power efficiency; high-performance computing for AI workloads; custom hardware co-design; and data-intensive infrastructures.

Socio-Economic & Decision Systems

Vertical and domain-adapted foundation models, agentic workflows, and emerging AI techniques with significant system-level implications for decision making and socio-economic modeling.

Domain-Specific Solutions

Advanced AI-ML methods for real-world systems including healthcare, education, governance, finance, communication, security, and computer vision. Emphasis is placed on scalability, robustness, and meeting strict operational constraints.

Safe & Responsible AI

Safe system design, detection of out-of-distribution data and hallucinations, and rigorous verification and testing of AI systems. Includes the analysis of risks and opportunities arising from real-world deployment.