(The following topics include but are not limited to)
Track 1: Applied Statistics
High-dimensional statistical inference, hypothesis testing and confidence interval construction
Bayesian statistics, non-parametric statistics and semi-parametric statistical methods
Time series analysis, spatial statistics and panel data modeling
Survival analysis, reliability statistics and failure prediction models
Experimental design, sampling survey and causal inference (instrumental variables, difference-in-differences, piecewise regression)
Robust statistics, extreme value analysis and anomaly detection
Statistical computing, MCMC methods and variational inference
Statistical learning theory, ensemble learning and model selection
Track 2: Mathematical Modeling
Mathematical inverse problems in physics
Stochastic theory
Combinatorics
Complex dynamics
Difference and differential equations
Orbit and iteration theory
Symmetry and integrable systems
Special functions and orthogonal polynomials
Applications of computer mathematics
Data-driven modeling and simulation optimization
Predictive modeling and time series analysis
Uncertainty modeling and risk analysis
Statistical modeling and inference methods
Process modeling, simulation and control
Object-oriented modeling and simulation systems
Complex system modeling and multi-scale simulation
Real-time modeling, dynamic simulation and federated simulation
Knowledge-based modeling and intelligent simulation
Track 3: Data Analysis Data mining
Knowledge discovery
Data analysis methods
Process optimization
Predictive modeling
High-frequency data analysis
Time series
Complex data analysis
Unstructured data
Data visualization
Interactive analysis
Qualitative and quantitative analysis
Data analysis algorithms
System architecture design
Graph data analysis
Graph neural networks
Data-driven decision support ……