4th International Conference on AI, Data Mining and Data Science (AIDD 2026)
October 30 ~ 31, 2026, Virtual Conference Scope & Topics 4th International Conference on AI, Data Mining and Data Science (AIDD 2026) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Data mining, and Data Science. The Conference looks for significant contributions to all major fields of the AI, Data mining, and Data Science in theoretical and practical aspects.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to:
Topics of interest include, but are not limited to, the following Artificial Intelligence
- Foundation Models, Generative AI and Diffusion Models
- LLM Systems, Optimization, Compression and Efficient Inference
- Agentic AI, Autonomous AI Agents and Multi Step Reasoning
- Machine Learning, Deep Learning and Representation Learning
- Reinforcement Learning, Bandits and Sequential Decision Making
- Probabilistic Modeling, Bayesian Learning and Causal Inference
- Causal Representation Learning and Counterfactual Reasoning
- AutoML, Neural Architecture Search and Automated ML Pipelines
- Trustworthy, Robust, Safe and Explainable AI
- Adversarial Machine Learning and Model Security
- Multimodal AI (Vision–Language–Audio–Graph)
- Computer Vision, 3D Vision and Scene Understanding
- Speech, Audio and Natural Language Processing
- Graph Neural Networks, Graph Transformers and Graph Foundation Models
- Neuro Symbolic AI and Knowledge Driven Reasoning
- AI for Code, Program Synthesis and Software Intelligence
- Robotics, Embodied AI and Autonomous Systems
- Edge AI, TinyML and Pervasive Intelligence
Data Science
- Data Science Theory, Methods and Pipelines
- Data Centric AI, Data Quality Optimization and Data Curation
- Big Data Architectures, Distributed Systems and Scalable ML
- AI Infrastructure, GPU/TPU Optimization and Model Parallelism
- Data Lakes, Warehouses and Lakehouse Architectures
- Data Integration, Cleaning, Wrangling and Feature Engineering
- Data Provenance, Lineage and Trustworthy Data Pipelines
- Vector Databases, Embedding Search and Neural Retrieval
- Information Retrieval, RAG 2.0 and Hybrid Search
- Cloud, Edge and Federated Data Science
- MLOps, Model Deployment, Monitoring and DataOps
- Privacy Preserving Data Science (DP, MPC, HE)
- Synthetic Data Generation, Evaluation and Simulation
- Visual Analytics, Dashboards and Interactive Exploration
Data mining
- Foundations of Data Mining and Knowledge Discovery
- Classification, Regression, Clustering and Association Analysis
- Dimensionality Reduction, Manifold Learning and Feature Selection
- Outlier, Anomaly and Novelty Detection
- Time Series, Forecasting and Sequential Pattern Mining
- Graph Mining, Network Mining and Knowledge Graph Reasoning
- Text, Web, Video and Multimedia Data Mining
- Spatial, Spatio Temporal and Trajectory Data Mining
- Data Stream Mining and Online Learning
- Human Centered and Human in the Loop Data Mining
- Constraint Based and Knowledge Guided KDD
- Parallel, Distributed and Scalable Data Mining Algorithms
- Mining from Noisy, Incomplete and Low Quality Data
- Security, Privacy and Information Hiding in Data Mining
- LLM Data Mining, Dataset Curation and Training Data Analysis
- Explainable Data Mining and Transparent KDD
- AutoKDD and Automated Feature Engineering
Applied AI, Data Mining and Data Science
- Educational Data Mining and Learning Analytics
- Medical, Healthcare and Bioinformatics Data Mining
- AI for Drug Discovery, Protein Design and Molecular ML
- Enterprise, Finance and Business Analytics
- IoT, Cyber Physical Systems and Sensor Data Analytics
- Disaster Prediction, Risk Modeling and Crisis Analytics
- Operations Research, Optimization and Decision Support
- Semantic Web, Knowledge Graphs and Web Intelligence
- AI for Climate, Sustainability and Environmental Modeling
- AI for Cybersecurity and Threat Detection
- AI for Science (Physics, Chemistry, Materials)
- AI for Social Good and Public Policy
Theory, Foundations and Emerging Topics
- Computational Learning Theory and Generalization
- Statistical Methods for Intelligent Data Analysis
- Mechanistic Interpretability and Model Internals
- Optimization for Deep Learning at Scale
- Self Supervised Learning and Foundation Model Pretraining
- Reasoning, Planning and Sequential Decision Processes
- Knowledge Representation, Reasoning and Learning
- Responsible AI, Data Ethics and AI Governance
- Mining Trends, Opportunities, Risks and Future Directions
Paper Submission Authors are invited to submit papers through the conference Submission System by September 19, 2026. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI) series (Confirmed). Selected papers from AIDD 2026, after further revisions, will be published in the special issue of the following journals. Important Dates | Submission Deadline | : | September 19, 2026 | | Authors Notification | : | October 19, 2026 | | Final Manuscript Due | : | October 24, 2026 |
Co - Located Event ***** The invited talk proposals can be submitted to aidd@aidd2026.org
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