My project concerns using complex-valued neural networks to improve the phase estimation of interferometric signals. This essentially means that I'm using a machine learning model that is trained directly on the phase and magnitude of signals (since complex numbers are used to represent the phase and magnitude of signals as a number) and trying to improve how those signals are processed in regards to interferometry, an imaging/measurement technique that uses light waves to create interference patterns to measure surfaces or take precise images. I decided on this project after I learned about interferometry in Astrophysics as it was used in the Michelson-Morley experiments. At the same time, I've always been fascinated by neural networks and their applications. This project combines these two interests into one. With it, I hope I can learn more about machine learning, interferometry, and the process of research.