About Me

Hello! I’m a second year PhD student at Columbia University in the Department of Biomedical Informatics (DBMI), advised by Professor Matthew McDermott. I work on machine learning for healthcare, mostly on foundation models over EHR event streams and how to evaluate them.

Before Columbia, I earned a B.S. in Computer Science with a minor in Physics from the University of Michigan. There I worked with Professor Jenna Wiens, in the Michigan AI Lab on machine learning for healthcare, first as an undergraduate researcher and then for a year as a pre-doctoral researcher. Here is my CV.

Outside of research I enjoy cycling, keeping up with the news, music, and cooking.

Email: gbk2114 (at) cumc (dot) columbia (dot) edu

News

7/26: EveryQuery was accepted to the Structured Data for Health workshop at ICML 2026.
9/25: Excited to begin my PhD at Columbia University in NYC, joining the McDermott Health AI Lab and the Department of Biomedical Informatics.
11/24: I wrote a blog post on DEPICT for the Michigan AI lab blog. Check it out here.
7/24: DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks accepted to ECCV 2024!
6/24: Started as a full time research assistant with Jenna Wiens’s MLD3 group at Michigan.
5/24: Graduated with a B.S. in Computer Science and minor in Physics from Michigan with distinction.
6/23: Started as a undergraduate research assistant with Jenna Wiens’s MLD3 group at Michigan.

Publications

DEPICT: Diffusion Enabled Permutation Importance for Image Classification Tasks
Sarah Jabbour, Gregory Kondas, Ella Kazerooni, Michael W. Sjoding, David Fouhey*, Jenna Wiens*. ECCV 2024. [paper][code]
depict_thumbnail Permutation importance has been used to provide feature importance explanations for tabular-based models. Leveraging text-conditioned diffusion, we extend this framework to image-based models and facilitate dataset-level model explanations.

Teaching

Teaching Assistant - PHYSICS 250: Physics for the Life Sciences II, Spring 2021 - Fall 2023