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.

My methods work focuses on vision-language models, agent-based systems, and evaluation. I also develop multimodal large language models for biology research.

Scholar / Twitter / Github / LinkedIn

Research

	MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research James Burgess, Jeffrey J Nirschl, Laura Bravo-Sánchez, Alejandro Lozano, Sanket Rajan Gupte, Jesus G. Galaz-Montoya, Yuhui Zhang, Yuchang Su, Disha Bhowmik, Zachary Coman, Sarina M. Hasan, Alexandra Johannesson, William D. Leineweber, Malvika G Nair, Ridhi Yarlagadda, Connor Zuraski, Wah Chiu, Sarah Cohen, Jan N. Hansen, Manuel D Leonetti, Chad Liu, Emma Lundberg, Serena Yeung-Levy Preprint* pdf / benchmark An expert-curated benchmark for research-level reasoning in biological microscopy, with a method for making multiple-choice VQA more challenging.
	Video Action Differencing James Burgess, Xiaohan Wang, Yuhui Zhang, Anita Rau, Alejandro Lozano, Lisa Dunlap, Trevor Darrell, Serena Yeung-Levy Preprint pdf / benchmark We propose Video Action Differencing, a new task for identifying subtle differences between videos of the same action, with a comprehensive benchmark, and an agent-based method.
	Viewpoint Textual Inversion: Discovering Scene Representations and 3D View Control in 2D Diffusion Models James Burgess, Kuan-Chieh Wang, Serena Yeung-Levy ECCV 2024 Outstanding Paper Award at the ECCV Workshop "Emergent Visual Abilities and Limits of Foundation Models" 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'.
	Micro-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.
	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.
	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.

Teaching

Lecturer and teaching assistant, CS286/BIODS276 Advanced Topics in Computer Vision and Biomedicine, Stanford 2024.

Teaching assistant, CS271/BIODS220, Artificial Intelligence in Healthcare, Stanford 2022.

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