About Daniel Fredin

I am a physics graduate student at the University of Washington, with a focus on machine learning applications in gravitational wave detection and astrophysical signal processing. My current graduate research is conducted in collaboration with the Gravitational Wave Gramian Angular Summation Field (GWGASF) project, where I develop deep learning models for analyzing noisy time-series data from astrophysical sources.

In addition to my academic work, I bring hands-on engineering experience from roles in both the aerospace and space technology sectors. I previously worked at Boeing as a Materials, Process and Physics Engineer, specializing in nondestructive evaluation (NDE) methods including ultrasonic testing and radiographic inspection for composite aerospace structures. I currently serve as a Project Technician at Amazon’s Project Kuiper, supporting the development and validation of satellite systems.

My broader interests lie at the intersection of physics, artificial intelligence, and high-performance computing, with a focus on solving complex problems in fundamental science and engineering. This website highlights my research, technical projects, and professional experience.

For inquiries regarding research collaborations or professional opportunities, please feel free to reach out.