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Team

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Seungmin "Sam" Hwang Principal Investigator Karen Zhu Senior Research Scientist Suji Kim Postdoctoral Associate Danny Papini Research Scientist Chukwudumebi "Dumebi" Okonkwo Research Associate Kyoungwha "Ellen" Pang Visiting Scholar Xiaoyu "Alina" Yang Postdoctoral Associate

Collaborate with the Imaging Platform

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There are a number of different ways to work with us, depending on the scale of the help/collaboration. We welcome academic and industry projects involving image analysis, CellPainting, Optical Pooled Screening, and image data science. Don't see a path that matches your needs? We're happy to get...

Hongming Shang

Person: Research Technician, Dana Farber Cancer Institute

Hongming Shang

Runxi Shen

Person: Postdoctoral Associate

I focus on developing advanced deep-learning methods to create robust cell representations from cell painting assays. My research aims to understand how various genetic variants alter cellular states and explore potential treatments for diseases associated with these variants.

Christina Papini

Person:

To be filled up

Kyla Jacobs

she/her

Person: Research Associate I

Press

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Articles and other press 2024: Beth Cimini is interviewed in special episode on Mental Health on Peter O'Toole's podcast series "The Microscopists" [ Link to video] 2024. Beth Cimini is interviewed in "Enhancing Global Access: interview with CZI grantee Beth Cimini" (Focal Plane/ The Company of...

Adapting systems biology to address the complexity of human disease in the single-cell era

Publication: Journal Article

Fischer, David S., Martin A. Villanueva, Peter S. Winter, and Alex K. Shalek. 2025. “Adapting Systems Biology to Address the Complexity of Human Disease in the Single-Cell Era”. Nature Review Genetics.

Consequences of training data composition for deep learning models in single-cell biology

Publication: Journal Article

Nadig, Ajay, Akshaya Thoutam, Madeline Hughes, Anay Gupta, Andrew W. Navia, Nicolo Fusi, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, and Lorin Crawford. 2025. “Consequences of Training Data Composition for Deep Learning Models in Single-Cell Biology”. BioRxiv.

Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models

Publication: Journal Article

Cultrera_di_Montesano, Sebastiano, Davide D’Ascenzo, Srivatsan Raghavan, Ava P. Amini, Peter S. Winter, and Lorin Crawford. 2025. “Hierarchical Cross-Entropy Loss Improves Atlas-Scale Single-Cell Annotation Models”. BioRxiv.