Advisor: Josh Bloom
Office: Campbell 202 C
Hi! I’m Jackie, a 3rd year graduate student and NSF Fellow in the Astronomy Department working with Professor Josh Bloom. I am currently using neural likelihood-free inference to infer parameters of eclipsing binary stars so that we can learn more about stellar evolution, measure distances within the Milky Way as well as to distant galaxies, and gain a better understanding of the population of eclipsing binaries overall.
I grew up in eastern Iowa and fell in love with astronomy when I read Stephen Hawking’s A Brief History of Time for my summer reading list in high school. I earned my Bachelor’s degrees in Physics and Computer Science with minors in Astronomy and Mathematics at Iowa State University, from which I graduated summa cum laude with Honors in May 2020. Through my research as an undergraduate, I discovered my passion for developing novel techniques and methodologies, especially those involving machine learning, for solving a variety of problems in astronomy. I have continued to follow that passion here at Berkeley. After earning my PhD, I hope to use the skills I have learned in grad school to target climate-related problems here on Earth through a career in industry as an environmental data scientist.
In my spare time, I enjoy doing a variety of outdoor activities (especially backpacking in the mountains!), practicing and performing aerial arts, knitting, and cooking. I also enjoy tutoring high school and undergraduate students in physics, astronomy, and math – feel free to reach out if you are looking for some help in any of your classes!
My main interest is in machine learning (ML) applications. Currently, I am focusing on applying ML to eclipsing binary stars to infer their fundamental stellar and orbital parameters. In the past I have worked on finding strong gravitational lens candidates in galaxy images, distinguishing Young Stellar Objects from more evolved stars, developing a maximum likelihood method for studying VERITAS extended gamma-ray sources. and detecting faint short transients in Rubin images.