(The following topics include but are not limited to)
Track 1: Application of Statistical Theory and Methods
High-dimensional Statistical Inference
Bayesian Statistics and Computation
Nonparametric and Semi-parametric Statistics
Time Series Analysis and Prediction
Spatial Statistics and Geostatistical Modeling
Survival Analysis and Reliability Statistics
Sampling Survey and Experimental Design
Statistical Hypothesis Testing and Large Sample Theory
Multivariate Statistics Analysis
Extreme Value Statistics and Risk Statistics
Longitudinal Data and Panel Data Statistical Methods
Statistical Processing of Missing Data
Statistical Causal Inference
Track 2: Data Modeling and Data Analysis
Complex System Data Modeling
Small Sample and Unbalanced Data Modeling
Time Series Data Modeling and Mining
Graph Data and Network Data Modeling
Text, Image Multimodal Data Modeling
Financial and Economic Data Modeling
Biomedical Data Modeling
Industrial Process Data Modeling
Causal Modeling and Explainable Modeling
Data Dimension Reduction and Feature Engineering
Anomaly Detection and Outlier Modeling
Data Modeling under Transfer Learning
Big Data Preprocessing and Data Quality Assessment
Track 3: Intelligent Computing and Statistical Artificial Intelligence
Machine Learning Algorithms and Statistical Learning Theory
Deep Learning and Neural Network Modeling
Reinforcement Learning and Intelligent Optimization Algorithms
Fuzzy Systems and Rough Set Theory
Evolutionary Computation and Swarm Intelligence
Intelligent Prediction and Decision Models
Explainable Artificial Intelligence (XAI)
Federated Learning and Privacy Computing
Intelligent Optimization and Operations Research Computing
Statistical Intelligent Methods in Computer Vision
Statistical Modeling in Natural Language Processing
Application of Intelligent Computing in Risk Warning
Statistical-driven Large Model Methods
...