Hello! I am a Visiting Assistant Professor of Computer Science at Occidental College. In the fall of 2022, I’ll be teaching Data Structures and Computer Organization. My research centers around machine learning practices in astronomy; I’m interested in scientific software and computational infrastructure, and tools developed in industry that are adapted in academic environments. I collaborate with Dr. Tuan Do, Assistant Professor at UCLA in the Physics and Astronomy Department, and our Machine Learning group, the UCLA Astrophysics Data Lab. Contact me at boscoe at oxy.edu.
I’m teaching in Spring ’22: Computer Organization, Databases, and a special topics course– a computational data science course geared for CS majors. In March 2022 I took a group of Oxy students to Stanford University to Women in Data Science Conference (WiDS 2022), my first in-person conference in two years. We had a fantastic time.
This week (Dec 2021) our ML UCLA group presents our paper & poster at Machine Learning for the Physical Sciences at NeurIPS, yes, the remote slog continues: https://ml4physicalsciences.github.io/2021/ . If you are stumbling in here from the world wide web, these days I’m interested in processes for curating ML datasets for astro, as well as model preservation in machine learning. And of course all the cool projects my students are doing at Oxy. Everyone continue to stay safe out there.
Presenting at ADASS this week (in Capetown, South Africa) on October 27: https://www.adass2021.ac.za/
I am now a visiting assistant professor at Occidental College in the Computer Science Department. Looking forward to being at Oxy’s beautiful campus in Eagle Rock, CA with a fantastic set of students. Currently teaching: Operating Systems and Computer Organization
I was previously a postdoctoral researcher at the University of California, Los Angeles, in the Physics and Astronomy Department, with Dr. Tuan Do as my advisor. Our Alfred P. Sloan-funded Machine Learning in Astronomy project’s aims are twofold: to expand upon an existing training sets of galaxy data and hopefully make then ‘machine learning ready’ a term we attempt to define, as well as exploring an understanding of tools and workflows necessary to incorporate machine learning practices derived from industry and computer 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 (CSUN), and undergraduate degrees in fine arts (Pratt Institute) and computer science.
Astronomically-sized thank you to the Alfred P. Sloan Foundation for funding the Machine Learning in Astronomy research project, grant # G2020-14032