The project Probabilistic tools for high-dimensional geometric inference, topological data analysis and large-scale networks aims to explore the power of randomization in developing new algorithms, as well as structural understanding of large, high-dimensional datasets and networks, which often arise in modern applications, for example, in artificial intelligence, machine learning, data mining etc. This includes e.g. designing geometric algorithms with reduced dependence on ambient dimension, dimensionality reduction for topological data analysis, sample compression for systems of bounded VC dimension, study of random simplicial complexes, etc. More information regarding the aims and objectives of the project can be found here.