Conference Information

AI4Science 2026: International Conference on Artificial Intelligence for Science

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Submission Date:
2026-07-15
Notification Date:
2026-08-20
Conference Date:
2026-10-23
Location:
Shenzhen, China
Viewed: 1272   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

44.2 / 100
Ranked #3,124 of 5,682 conferences · Top 55%

#371 of 739 in Artificial Intelligence & Machine Learning

Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
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%) No data - scored at the neutral baseline of 50
Community attention (10%)
15
Public record completeness (15%)
35

Inputs used: Researchers who opened this page in the past 24 months: 6

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 25% - 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 2026-09-15

Call For Papers

AI4Science 2026 (International Conference on Artificial Intelligence for Science) is an academic conference held in Shenzhen, China on 2026-10-23. The paper submission deadline is 2026-07-15. Acceptance notifications are sent on 2026-08-20.

The topics of interest for submission include, but are not limited to: Track 1: AI Foundations for Scientific Discovery Machine Learning Physics-informed AI Scientific Foundation Models Scientific Reasoning Causal Learning Scientific Knowledge Graphs AI-based Modeling Scientific Optimization Explainable Scientific AI Trustworthy AI Track 2: Computer Vision and Intelligent Perception Computational Imaging Image Reconstruction Computer Vision Multimodal Perception Vision Foundation Models Intelligent Sensing 3D Vision Biomedical Imaging Remote Sensing Intelligence Video Understanding Track 3: Data Intelligence and AI Systems Data Mining Data Analytics Distributed AI Parallel Computing High-performance Computing Cloud-Edge Intelligence Scientific Workflows AI Computing Systems Data-driven Modeling Intelligent Decision Systems Track 4: AI for Scientific Discovery Foundation Models for Science Autonomous Scientific Agents AI-driven Scientific Discovery AI for Materials Discovery AI for Molecular Modeling AI for Computational Biology AI for Protein Structure Prediction AI for Scientific Experimentation AI for Climate Science AI-assisted Scientific Research
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