Conference Information

AIMLSystems 2026: International Conference on AI-ML Systems

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Submission Date:
2026-06-08
Notification Date:
2026-07-30
Conference Date:
2026-10-06
Location:
Lecco, Italy
Years:
6
Viewed: 13966   Tracked: 3   Attend: 0

Conference Partner Index (CP-I)

46.1 / 100
Ranked #2,495 of 5,682 conferences · Top 44%
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
52
Community attention (10%)
29
Public record completeness (15%)
35

Inputs used: Editions on record: 6 · Researchers following it here: 3 · Researchers who opened this page in the past 24 months: 7

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-17

Call For Papers

AIMLSystems 2026 (International Conference on AI-ML Systems) is an academic conference held in Lecco, Italy on 2026-10-06. The paper submission deadline is 2026-06-08. Acceptance notifications are sent on 2026-07-30.

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.
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