Data and Database Systems for AI

More workshops at IEEE International Conference on Data Mining (ICDM) 2026

• AI-ready Data for Science — Focuses on preparing data for AI-driven scientific discovery.
• AI-for-Science Benchmarking — Covers benchmarking AI methods for scientific applications.
• Adaptable Reliable Responsible Learning — Focuses on adaptability, reliability, and responsibility in machine learning.
• Foundation Models for Biology — Explores foundation models for biology and bioinnovation.
• Deep Learning and Clustering — Focuses on deep-learning approaches to clustering and representation learning.
• Data Mining for Ambient Intelligence — Applies data mining to ambient intelligence and secure communications.
• Data Mining in Biomedical Informatics — Applies data mining to biomedical informatics and healthcare data.
• Data Mining for Service — Applies data mining to service systems and service science.
• Evolutionary Data Mining — Covers evolutionary computation approaches to data mining and machine learning.
• Evolving Graph Data Mining — Focuses on mining evolving graphs and continual relational learning.
• GNN-LLM Synergy — Combines graph neural networks with large language models for reasoning over structure.
• Human Dynamics and Mobility Analytics — Focuses on mining human dynamics and mobility in the age of AI agents.
• High Dimensional Data Mining — Focuses on mining and learning from high-dimensional data.
• Incremental and Continual Learning — Covers incremental, continual, and lifelong learning methods.
• Knowledge Discovery in ISAC — Applies knowledge discovery to integrated sensing and communication systems.
• LLMs for Multimodal Data Fusion — Applies large language models to fusing multimodal data.
• LLMs and Tensor Analysis — Explores the interplay of large language models and tensor analysis.
• Large Models for Time Series — Covers large and foundation models applied to time series data mining.
• Medical Time Series Foundation Models — Covers foundation models for medical time series analytics.
• Mental Health on Social Media — Covers detecting mental-health signals in social media data.
• Multimodal Mining for Agriculture — Applies multimodal data mining to sustainable agriculture.
• Machine Learning for Cybersecurity — Applies machine learning to cybersecurity problems.
• Optimization for Data Mining — Applies optimization techniques to emerging data mining problems.
• Open World Anomaly Detection — Covers anomaly detection in open, dynamic, and evolving settings.
• Pattern Mining for Bioinformatics — Applies pattern mining and machine learning to bioinformatics.
• Clustering with Pretrained Models — Explores clustering using pretrained and foundation models.
• Sentiment Analysis — Covers sentiment analysis and opinion mining from text.
• Spatial and Spatiotemporal Data Mining — Covers data mining over spatial and spatiotemporal data.
• Secure Federated Generative AI — Covers security and trust in federated generative AI.
• Trustworthy ML for Decision-Making — Covers fairness, privacy, robustness, and explainability in decision-making ML.
• Trustworthy Federated Learning for Industry — Focuses on trustworthy federated learning for smart industrial systems.
• Trustworthy Recommender Systems — Focuses on trust, privacy, and unlearning in recommender systems.
• Urban Intelligence and Mobility — Covers urban intelligence and multimodal mobility data mining.
• AI for Personalization — Covers AI and data mining methods for personalization and recommendation.
• World Models and Autonomous Intelligence — Explores world models and autonomous intelligence for data mining.