THEORY

Section 1. Machine Learning in Fundamental Physics

• Machine learning methods in particle astrophysics and high energy physics.
• Fast event generators based on machine learning for simulation of physics phenomena.
• Multi-messenger data analysis of experimental data.
• Application machine learning for data analysis in megascience facilities.

Section 2. Machine Learning for Environmental Sciences

• Climate analysis, retrospective analysis and projection
• Statistical modeling of the ocean and atmosphere on various temporal and spatial scales
• Environmental monitoring: remote sensing, instrumental monitoring, observations, monitoring networks

Section 3. Machine Learning in Natural Sciences

• Biology and bioinformatics.
• Engineering sciences.
• Modern Machine Learning Methods

The conference will feature:

• invited presentations – 30 minutes,
• regular presentations – 15 minutes,