Author name cluster

James Burgess

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICML Conference 2025 Conference Paper

CellFlux: Simulating Cellular Morphology Changes via Flow Matching

Yuhui Zhang
Yuchang Su
Chenyu Wang 0003
Tianhong Li
Zoe Wefers
Jeffrey J. Nirschl
James Burgess
Daisy Ding

Building a virtual cell capable of accurately simulating cellular behaviors in silico has long been a dream in computational biology. We introduce CellFlux, an image-generative model that simulates cellular morphology changes induced by chemical and genetic perturbations using flow matching. Unlike prior methods, CellFlux models distribution-wise transformations from unperturbed to perturbed cell states, effectively distinguishing actual perturbation effects from experimental artifacts such as batch effects—a major challenge in biological data. Evaluated on chemical (BBBC021), genetic (RxRx1), and combined perturbation (JUMP) datasets, CellFlux generates biologically meaningful cell images that faithfully capture perturbation-specific morphological changes, achieving a 35% improvement in FID scores and a 12% increase in mode-of-action prediction accuracy over existing methods. Additionally, CellFlux enables continuous interpolation between cellular states, providing a potential tool for studying perturbation dynamics. These capabilities mark a significant step toward realizing virtual cell modeling for biomedical research. Project page: https: //yuhui-zh15. github. io/CellFlux/.

Details

ICLR Conference 2025 Conference Paper

Video Action Differencing

James Burgess
Xiaohan Wang
Yuhui Zhang
Anita Rau
Alejandro Lozano
Lisa Dunlap
Trevor Darrell
Serena Yeung

How do two individuals differ when performing the same action? In this work, we introduce Video Action Differencing (VidDiff), the novel task of identifying subtle differences between videos of the same action, which has numerous applications, such as coaching and skill learning. To enable development on this new task, we first create VidDiffBench, a benchmark dataset containing 549 video pairs, with human annotations of 4,469 fine-grained action differences and 2,075 timestamps indicating where these differences occur. Our experiments demonstrate that VidDiffBench poses a significant challenge for state-of-the-art large multimodal models (LMMs), such as GPT-4o and Qwen2-VL. By analyzing the failure cases of LMMs on VidDiffBench, we highlight two key challenges for this task: localizing relevant sub-actions over two videos and fine-grained frame comparison. To overcome these, we propose the VidDiff method, an agentic workflow that breaks the task into three stages: action difference proposal, keyframe localization, and frame differencing, each stage utilizing specialized foundation models. To encourage future research in this new task, we release the benchmark and code.

Details

NeurIPS Conference 2024 Conference Paper

Micro-Bench: A Microscopy Benchmark for Vision-Language Understanding

Alejandro Lozano
Jeffrey Nirschl
James Burgess
Sanket R. Gupte
Yuhui Zhang
Alyssa Unell
Serena Yeung-Levy

Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research. Vision-language models (VLMs) offer a promising solution for large-scale biological image analysis, enhancing researchers’ efficiency, identifying new image biomarkers, and accelerating hypothesis generation and scientific discovery. However, there is a lack of standardized, diverse, and large-scale vision-language benchmarks to evaluate VLMs’ perception and cognition capabilities in biological image understanding. To address this gap, we introduce Micro-Bench, an expert-curated benchmark encompassing 24 biomedical tasks across various scientific disciplines (biology, pathology), microscopy modalities (electron, fluorescence, light), scales (subcellular, cellular, tissue), and organisms in both normal and abnormal states. We evaluate state-of-the-art biomedical, pathology, and general VLMs on Micro-Bench and find that: i) current models struggle on all categories, even for basic tasks such as distinguishing microscopy modalities; ii) current specialist models fine-tuned on biomedical data often perform worse than generalist models; iii) fine-tuning in specific microscopy domains can cause catastrophic forgetting, eroding prior biomedical knowledge encoded in their base model. iv) weight interpolation between fine-tuned and pre-trained models offers one solution to forgetting and improves general performance across biomedical tasks. We release Micro-Bench under a permissive license to accelerate the research and development of microscopy foundation models.

PDF Details DOI

ICRA Conference 2003 Conference Paper

Experiments in nonsmooth control of distributed manipulation

Todd D. Murphey
Joel W. Burdick
James Burgess
Andrew Homyk

This paper describes an experimental modular distributed manipulation system upon which one can implement a variety of control schemes. We have shown elsewhere that when one includes the nonsmooth effects of friction into a model of distributed manipulation, nonsmooth feedback laws must generally be used to control distributed manipulators. We summarize results obtained with this experimental system that confirm the validity of control schemes proposed by the authors in recent papers. We describe the control algorithms in some detail and include specifics of the experimental set-up and experimental results.

Details