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 focus on vision-language models. 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 NeurIPS Datasets & Benchmarks 2024 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) Cell 2024 bioRxiv / code 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 ECCV Workshop "Emergent Visual Abilities and Limits of Foundation Models" - Outstanding Paper Award project page / arXiv / code We show that 2D diffusion models like StableDiffusion have 3D control in their text input space which we call '3D view tokens'.
	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.