Polina Golland
Papers
1
Total Citations
33
H-Index
1
About
Polina Golland is a leading figure in computational imaging and biomedical image analysis, with a focus on developing probabilistic models to extract quantitative biological insights from complex microscopy data. Her work bridges computer vision, machine learning, and biology, particularly through shape-based modeling and segmentation. In her highly cited 2010 paper, "Resolving clustered worms via probabilistic shape models," Golland tackled the challenge of automated analysis of *C. elegans* in high-throughput screens, introducing a probabilistic framework to disentangle overlapping worm shapes—a critical step for scalable genetic and drug studies. This contribution, cited over 30 times, exemplifies her impact in enabling robust, automated biological discovery. Beyond this, Golland has made foundational contributions to statistical shape analysis, diffusion MRI, and neuroimaging, with a career spanning over 100 publications and thousands of citations. Her work has been recognized with multiple best paper awards and leadership roles in top conferences like MICCAI. For students and researchers, Golland’s research exemplifies how principled computational models can unlock new biological and clinical insights from imaging data.
Research Focus
Key Achievements
Top Papers
- 1Resolving clustered worms via probabilistic shape models33 citations · 2010