(The following topics include but are not limited to)
Track 1: Applied Statistics
Theoretical and Methodologies of Mathematical Statistics
Statistical Modeling and Inference
Non-parametric Statistics and Semi-parametric Statistics
Bayesian Statistics and Computational Statistics
Time Series Analysis and Prediction
Spatial Statistics and Spatio-temporal Modeling
Multivariate Statistical Analysis and Dimension Reduction Techniques
Survival Analysis and Reliability Statistics
High-dimensional Data Analysis and Variable Selection
Statistical Computing and Monte Carlo Methods
Experimental Design and Response Surface Methodology
Statistical Quality Control and Six Sigma
Biostatistics and Clinical Trial Design
Environmental Statistics and Ecological Statistics
Economic Statistics and Financial Econometrics
Social Statistics and Market Research
Statistical Software and Applications
Statistical Learning and Foundations of Data Science
Track 2: Big Data
Big Data Storage and Management Technologies
Big Data Processing Frameworks (Hadoop, Spark, etc.)
Distributed Computing and Data Parallel Processing
Stream Data Processing and Real-time Computing
Graph Data Processing and Graph Computing
Spatial Big Data and Trajectory Analysis
Text Mining and Natural Language Processing
Multimodal Data Processing and Fusion
Big Data Visualization and Interactive Analysis
Big Data Privacy Protection and Security
Data Governance and Data Quality
Big Data Applications in Social Networks
Big Data Applications in Medical Health
Big Data Applications in Financial Risk Control
Big Data Applications in Urban Management
Internet of Things and Sensor Data Stream Analysis
Track 3: Machine Learning
Supervised Learning Algorithms and Theories
Unsupervised Learning and Semi-supervised Learning
Reinforcement Learning and Deep Reinforcement Learning
Deep Learning Architectures and Optimization
Generative Models and Diffusion Models
Transfer Learning and Domain Adaptation
Federated Learning and Privacy-Preserving Machine Learning
Small Sample Learning and Zero-Shot Learning
Multi-task Learning and Meta-Learning
Interpretable Machine Learning and Causal Inference
Graph Neural Networks and Graph Representation Learning
Self-supervised Learning and Contrastive Learning
Large-scale Machine Learning and Distributed Training
Neural Network Architecture Search
Adversarial Machine Learning and Robustness
Probabilistic Graphical Models and Inference
Kernel Methods and Support Vector Machines
Ensemble Learning and Boosting Methods
Machine Learning in Interdisciplinary Fields
Fine-tuning of Large Models and Efficient Training Techniques
Multimodal Machine Learning and Cross-modal Understanding
AI Agents and Autonomous Decision Systems
...