Katherine Xu

I'm a first year Computer Science PhD student in the GRASP Lab at the University of Pennsylvania, and I am currently working with Prof. Jianbo Shi. My research interests in computer vision and machine learning include image segmentation, generative models, and 3D scene understanding. I also enjoy leveraging AI in the medical, climate, and robotics domains.

Previously, I spent 4.5 wonderful years at MIT, where I completed my bachelor's degree in Computer Science with a minor in Mathematics and my master's degree in Computer Science with a concentration in AI. I am grateful for the opportunities to conduct research at MIT and intern twice at Meta, including Meta AI (FAIR).

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I developed a model that estimates the extreme heat risk of an urban area at the census tract level. To construct this model, I fine-tuned a vision transformer to segment risk factors from aerial images. I also incorporated heat hazard and vulnerability factors from land surface temperature, building, and socioeconomic datasets. This research focuses on developing a heat risk model for Boston, which experiences intense urban heat islands.

During my internship at Meta AI, I worked with the Open Catalyst team, which uses AI to discover catalysts for renewable energy storage. Large graph neural networks (GNNs) have shown good progress on the Open Catalyst 2020 (OC20) dataset to predict the forces and energies of atoms and systems, but we have little understanding of how or why these models work. Hence, we present perturbation analyses of GNN predictions on OC20, and we observed evidence that aligns with chemical intuition.

Template credit to Jon Barron.