Welcome: I am a Visiting Assistant Professor of Computer Science at Occidental College in Los Angeles, California. As a computer and information scientist, I have deep expertise in building and maintaining technological infrastructures, from hardware to software. I currently research the incorporation of machine learning into astronomy data pipelines. I collaborate with Dr. Tuan Do, Assistant Professor at UCLA in the Physics and Astronomy Department, and the UCLA Astrophysics Data Lab. During Spring 2023, I am teaching Data Structures, and Computer Organization, using Oxy’s Bletchley cluster. Say hello at boscoe ~at~ oxy.edu.

Update 11.29.22

Our NeurIPS paper is here: https://arxiv.org/abs/2211.14401 . TLDR: In this work, we look at what properties an effective machine learning dataset should possess to be useful in astronomy. To create effective ML datasets, the process entails a combination of methods to obtain data from astronomy data archives, plus the addition of tools and techniques specifically for ML tool ingestion. This is a time-consuming task, requiring knowledge and expertise in both traditional astronomy data tools and contemporary machine learning data methods.

Update 10.22.22

Excited to announce our paper has been accepted to the Machine Learning in Physical Sciences workshop held at NeurIPS in December in New Orleans. Thanks to all the reviewers, and see everyone soon!

Update 05.08.22

Oxy Fountain in the spring

Update 03.17.22

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.

WiDS 2022.

Update 12.12.21

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.

Update 10/2021

Presenting at ADASS this week (in Capetown, South Africa) on October 27: https://www.adass2021.ac.za/

Update 08/2021

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

(Update 10.6.2020. Amazing. Congratulations to Andrea Ghez for the Nobel Prize 2020 in Physics [shared with Genzel and Penrose]. I’m just gasping for air here, 3rd from right.)

 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 set of galaxy data, as well as exploring an understanding of tools and workflows necessary to incorporate machine learning practices derived from industry and computer science 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