ACM KDD 2026
KDD is the premier Data Science and AI conference, hosting both a Research and an Applied Data Science Track. The conference will take place from August 9 to 13, 2026, in Jeju, Korea. KDD has two submission cycles per year. This call details the CFP for the first cycle and invites submissions for both the Research and the Applied Data Science Track. An additional Datasets and Benchmarks track call will only be issued in the second cycle along with the other tracks.
所属领域：数据库／数据挖掘／内容检索
CCF推荐：A
录用率：20%（2024年）
时间地点：2026年8月9日-济州·韩国
大会征文
Foundations of Knowledge Discovery and Data Science. Submissions are invited to discuss core models, algorithms, and theoretical insights for knowledge discovery. Topics may include data-driven learning and structured knowledge extraction, including supervised, unsupervised, semi-supervised, and self-supervised learning, classification, regression, clustering, and dimensionality reduction; model selection and optimization; probabilistic and statistical methods (e.g., Bayesian inference, graphical models); matrix and tensor methods; structured and relational learning from data.
Modern AI and Big Data. Submissions are invited to elaborate on the intersection of AI and massive data repositories. Topics may include deep representation learning, meta-learning, in-context learning, prompt engineering, continual learning, few-shot adaptation, reinforcement learning, generation, and reasoning, including generative models (e.g., GANs, VAEs), large language models (LLMs), and multimodal foundation and frontier models operating on big data; AI’s role in emergent reasoning, automated insight generation, and scientific discovery, including knowledge graph construction, hypothesis generation, neural-symbolic integration, and deriving novel concepts from large complex data.
Trustworthy and Responsible Data Science. Submissions are invited to feature techniques and frameworks that ensure responsible data use, management, and analysis. Topics may include data security, data privacy, data transparency, accountability in data-driven systems, privacy-preserving learning, adversarial robustness, interpretability and explainability of models, decision support visualization, fairness in data mining, ethical data processing, algorithmic auditing, and frameworks for responsible AI development and deployment.
Systems for Data Science and Scalable AI. Submissions that detail new architectures, systems, and infrastructures for large-scale data analysis and machine learning (e.g., distributed computing, federated learning, cloud-based systems) are invited. Topics may include efficient approaches to support high-volume data analysis, streaming, sampling, and summarization, data integration, transformation, and cleaning at scale, and data mining and machine learning for systems—machine learning for database management, learning device placement, orchestration, and scheduling of computational and data workflows.
Data Science Applications. Submissions are invited for innovative data science and artificial intelligence (AI) applications. Topics may include methods for analyzing scientific, social science, medical, and legal data, as well as time series, text, graphs, Internet of Things (IoT) data, and more. We also welcome contributions on recommender systems and bioinformatics. New directions that push the boundaries of data science applications are of particular interest, such as quantum data science, including algorithms and information-theoretic approaches for quantum machine learning and data processing in quantum systems.