Call for Research Papers
Exploring the interplay between AI/ML and System Engineering
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.
Important Dates
Timeline To Be Decided
Dates will be announced soon.
Submission Guidelines
- Page Limit: 6 pages (main content) + unlimited references.
- Format: Double-column ACM Standard Format.
- Review: Double-blind review process.