Interests: machine learning in science, software sustainability, responsible AI
I am currently a postdoctoral researcher at the University of California, Los Angeles, in the Physics and Astronomy Department, with Dr. Tuan Do as my advisor. Our Sloan-funded Machine Learning in Astronomy project’s aims are twofold: to expand upon an existing training set of galaxy data from the Hubble archive to develop GANs, as well as exploring an understanding of tools and workflows necessary to incorporate machine learning practices derived from industry and science writ large into the field of astronomy.
In addition to developing machine learning workflows, I research the design and implementation of data and code infrastructures in scientific research settings. I draw from my mathematical and computing background to understand socio-technical practices in science. I am particularly interested in methods for software maintenance and preservation in science research projects spanning decades.
I received a PhD in Information Studies from UCLA, and previously, an MS in pure mathematics, and undergraduate degrees in fine arts and computer science.