Scope of Submission
We welcome submissions from a broad range of disciplines, including but not limited to:
- Stochastic methods in machine learning
- Statistical learning for dynamic systems
- Stochastic modeling in data science
- Applications of stochastic methods in finance
- Statistical learning in time series forecasting
- Stochastic methods for optimization problems
- Statistical learning in computer vision
- Stochastic modeling in healthcare systems
- Applications of stochastic methods in engineering
- Statistical learning for anomaly detection
- Stochastic processes in artificial intelligence
- Statistical learning in social sciences
- Stochastic methods for risk management
- Statistical learning in environmental modeling
- Stochastic modeling in telecommunications
- Applications of stochastic methods in logistics
- Statistical learning for user behavior analysis
- Stochastic methods in operations research
- Statistical learning in energy systems
- Stochastic modeling in supply chain optimization
All papers must be original and not previously published or submitted elsewhere.
