◕ Statistics: Theoretical and Model Innovation

· Statistical Foundations for Explainable Artificial Intelligence (XAI)

· High-Dimensional Statistical Inference and Sparse Modeling

· Statistical Frameworks and Algorithms for Causal Inference

· Bayesian Nonparametric Methods and Probabilistic Programming

· Statistical Modeling for Complex Data (Network, Functional Data)

· Statistical Theory for Online Learning and Sequential Decision-Making

· Robust Statistics and Outlier Detection Theory

· Model Uncertainty Quantification and Trustworthy Inference

· Federated Learning and Distributed Statistical Computing

· Scientific Computing and Physics-Informed Statistical Models

◕ Data Science: From Data to Decision

·Multimodal Data Fusion and Knowledge Discovery

·Automated Machine Learning (AutoML) and Meta-Learning

·Large-Scale Data Governance, Quality, and Privacy-Preserving Computation

·Temporal Data Mining and Spatiotemporal Forecasting Analysis

·Graph Data Mining and Social Network Analysis

·Natural Language Processing and Text Mining

·Computer Vision and Image Understanding

·Recommender Systems and Personalization Techniques

·Decision Intelligence and Operations Optimization

·Data Visualization and Interactive Visual Analytics

◕ Intelligent Computing: Systems and Architectures

·Efficient Training and Inference Systems for Large Models

·Distributed Computing and Federated Learning Frameworks

·AI-Specific Chips and Heterogeneous Computing Architectures

·Edge Intelligence and Internet of Things (IoT) Computing Systems

·High-Performance Scientific Computing and AI for Science Platforms

·Cloud-Edge-End Collaborative Computing and Task Scheduling

·Neuromorphic Computing and Novel Computing Paradigms

·Computational Resource Management for Green and Low-Carbon AI

·Security and Trustworthiness Assurance for Intelligent Computing Systems

·Integration of Quantum Computing and Classical Intelligent Computing