isc36 [at] cornell [dot] edu
Bio
Hi! I’m Isabel, and I’m a first-year Information Science PhD student at the Cornell Bowers College of Computing and Information Science. I am interested in using computational social science to understand forms of harm and inequity that are propagated by large scale algorithmic systems. I am fortunate to be advised by Mor Naaman and Allison Koenecke.
Work Experience
Prior to joining Cornell’s Info Sci PhD program, I worked for several years as a data scientist at Spotify. At Spotify I contributed to several to different areas of the company, including algorithmic impact and responsibility and the ML Engagements team (Lex Beattie speaks about ML Engagements here).
I graduated with a B.S. in Statistics and Data Science from Yale, where my senior thesis on the assumptions and ontologies of word embedding bias was advised by Professors Elisa Celis and Issa Kohler-Hausmann. While at Yale I was also a research assistant for projects in the STEM PERL lab and the Human Nature Lab, and interned for education (InsideSchools) and child welfare policy research (Action Research Partners) organizations.
Publications
Most recent publications on Google Scholar.
Beattie, Lex, Isabel Corpus, Lucy H. Lin, and Praveen Ravichandran. “Evaluation Framework for Understanding Sensitive Attribute Association Bias in Latent Factor Recommendation Algorithms.” arXiv preprint arXiv:2310.20061 (2023). https://arxiv.org/abs/2310.20061
Kumar, Navin, Isabel Corpus, Meher Hans, Nikhil Harle, Nan Yang, Curtis McDonald, Shinpei Nakamura Sakai et al. “COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit.” BMC Public Health 22, no. 1 (2022): 446. https://pubmed.ncbi.nlm.nih.gov/35255881/
CV
Contact Me
You can reach me by email at isc36 [at] cornell [dot] edu. I am also on LinkedIn, and as of recently, Bluesky!