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--- seminars:stat:oct52023 [2023/10/01 13:12] – qyu
+++ seminars:stat:oct52023 [2023/10/01 13:13] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>
+<WRAP 70% center>
+^  **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|
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+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.
+</WRAP>