James Burgess

I am a Stanford PhD student working on computer vision and machine learning. I'm fortunate to be advised by Serena Yeung-Levy and to be supported by the Quad Fellowship.

In vision and ML, I'm interested in generative modeling, diffusion models, and 3D vision. I'm also very excited by applications of ML to cell biology, especially with representation learning and computer vision for microscopy.

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Global organelle profiling reveals subcellular localization and remodeling at proteome scale
Hein et. al. (including James Burgess)

A proteomics map of human subcellular architecture, led by the Chan-Zuckerberg Biohub.

Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models
James Burgess, Kuan-Chieh Wang, Serena Yeung
project page / arXiv

Image diffusion models encode 3D world knowledge in their latent space, which our method, ViewNeTI, leverages to do novel view synthesis from few input views.

Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles
James Burgess, Jeffrey J. Nirschl, Maria-Clara Zanellati, Alejandro Lozano, Sarah Cohen, Serena Yeung-Levy
Nature Communications
code / paper

Unsupervised shape representations of cells and organelles are erroneously sensitive to image orientation, which we mitigate with equivariant convolutional network encoders in our method, O2VAE.

I stole this website template from Jon Barron who published his source code here.