1. Data-Centric Learning and Analytics

• Data quality, data curation, and dataset engineering

• Data augmentation, synthesis, and labeling strategies

• Bias detection, fairness, and robustness in data

• Streaming, spatiotemporal, and large-scale data analytics

• Data visualization and human-in-the-loop data analysis​

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2. Machine Learning for Data-Centric AI

• Learning with noisy, sparse, or imbalanced data

• Weakly supervised, self-supervised, and semi-supervised learning、

• Foundation models and data-efficient learning

• Multimodal and heterogeneous data learning

• Explainable and trustworthy learning from data​

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3. Data-Driven Algorithms and Optimization

• Optimization methods for data selection and sampling

• Active learning and data acquisition strategies

• Scalable and distributed learning algorithms

• Graph, relational, and structured data algorithms

• Automated data pipelines and AutoML for data optimization

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4. Data Systems and Infrastructure for AI

• Big data architectures for AI workloads

• Cloud, edge, and distributed data-centric AI systems

• Data governance, privacy, and security in AI systems

• Federated learning and decentralized data intelligence

• Benchmarking, evaluation, and reproducibility of data-centric systems​

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5.  Data-Centric AI Applications

• Data-driven AI in healthcare, finance, and bioinformatics

• Smart cities, urban analytics, and digital governance

• Learning analytics and educational data intelligence

• Environmental, sustainable, and green AI

• NLP, computer vision, and speech systems from a data-centric perspective

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6.  Responsible and Future Data-Centric AI

• Ethical issues in data collection and usage

• Human–AI collaboration in data design and interpretation

• Data standards, governance frameworks, and policy

• Future directions and challenges in data-centric intelligence