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.

Scholar / Twitter / Github / LinkedIn

Research

	μ-Bench: A Vision-Language Benchmark for Microscopy Understanding Alejandro Lozano, Jeffrey Nirschl, James Burgess, Sanket Rajan Gupte, Yuhui Zhang, Alyssa Unell, Serena Yeung-Levy Preprint project page / arXiv / code A Vision-Language Benchmark for Microscopy Understanding.
	Global organelle profiling reveals subcellular localization and remodeling at proteome scale Hein et. al. (including James Burgess) Preprint bioRxiv A proteomics map of human subcellular architecture, led by the Chan-Zuckerberg Biohub.
	Viewpoint Textual Inversion: Discovering Scene Representations and 3D View Control in 2D Diffusion Models James Burgess, Kuan-Chieh Wang, Serena Yeung-Levy ECCV 2024 project page / arXiv / code 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 2024 paper / code 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.