Cosmological observations provide a unique window into the fundamental physics of the primordial universe. This project, led by a team at the University of Wisconsin, Madison, will make use of two major upcoming experiments, the Simons Observatory for the Cosmic Microwave Background and the Rubin Observatory for the distribution of galaxies, and combine them in a novel way. The team will cross-correlate these two data sets using an innovative method that illuminates how matter moves in the universe. This measurement can uncover the fundamental physics of cosmological inflation, the earliest known epoch in the evolution of the universe. To make this analysis possible, the team will generate very fast simulations, which are required to test the data analysis pipeline to the required precision. The project also explores the use of machine learning methods to enhance the sensitivity of the proposed analysis. As part of this project, the team will provide research experiences and develop research-based curricula for high school students and undergraduate students. The goal of this project is to develop a data analysis pipeline for kinetic Sunyaev-Zeldovich (kSZ) velocity reconstruction and apply it to Simons Observatory (SO) and Vera C. Rubin data. Before SO data becomes available, the team will apply their pipeline to existing Stage-3 data from ACT and the photometric DESI Legacy Survey. KSZ velocity reconstruction has the exciting property that it can be used to infer a map o