##Statistics Seminar##\\ Department of Mathematics and Statistics ^ **DATE:**|Thursday, October 5, 2023 | ^ **TIME:**|1:15pm -- 2:15pm | ^ **LOCATION:**|WH 100E | ^ **SPEAKER:**|Jingze Liu, Binghamton University | ^ **TITLE:**|High-dimensional Integration and Sampling with Normalizing Flows| \\ **Abstract** In many fields of science, high-dimensional integration is required. Numerical methods have been developed to evaluate these complex integrals. We introduce the code i-flow, a Python package that performs high-dimensional numerical integration utilizing normalizing flows. Normalizing flows are machine-learned, bijective mappings between two distributions. i-flow can also be used to sample random points according to complicated distributions in high dimensions. We compare i-flow to other algorithms for high-dimensional numerical integration and show that i-flow outperforms them for high dimensional correlated integrals.