Key publications

Wei Ouyang*, Fynn Beuttenmueller*, Estibaliz Gómez-de-Mariscal*, Constantin Pape*, Tom Burke, Carlos Garcia-López-de-Haro, Craig Russell, Lucı́a Moya-Sans, Cristina de-la-Torre-Gutiérrez, Deborah Schmidt, others.
BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis.
bioRxiv, 2022.

Wei Ouyang, Richard W Bowman, Haoran Wang, Kaspar E Bumke, Joel T Collins, Ola Spjuth, Jordi Carreras-Puigvert, Benedict Diederich.
An Open-Source Modular Framework for Automated Pipetting and Imaging Applications.
Advanced biology, 2022.

Wei Ouyang*, Jiachuan Bai*, Manish Kumar Singh, Christophe Leterrier, Paul Barthelemy, Samuel FH Barnett, Teresa Klein, Markus Sauer, Pakorn Kanchanawong, Nicolas Bourg, others.
ShareLoc-an open platform for sharing localization microscopy data.
Nature Methods, 2022.

Wei Ouyang, Andrey Aristov, Mickaël Lelek, Xian Hao, Christophe Zimmer.
Deep learning massively accelerates super-resolution localization microscopy.
Nature biotechnology, 2018.

Wei Ouyang, Casper F Winsnes, Martin Hjelmare, Anthony J Cesnik, Lovisa Åkesson, Hao Xu, Devin P Sullivan, Shubin Dai, Jun Lan, Park Jinmo, others.
Analysis of the human protein atlas image classification competition.
Nature methods, 2019.

Wei Ouyang, Florian Mueller, Martin Hjelmare, Emma Lundberg, Christophe Zimmer.
ImJoy: an open-source computational platform for the deep learning era.
Nature methods, 2019.

Wei Ouyang

The AICell Lab ( is a multidisciplinary group which focuses on building AI systems for data-driven cell and molecular biology. More specifically, we would like to take the grand challenge of modeling the human cell and building human cell simulators using powerful AI models. It is an ambitious goal that requires profound innovations in not only data analysis and modeling, but also in data generation. We believe it is crucial for us to build autonomous systems to acquire massive amounts of high quality data that are suitable to train AI models. To this front, we would like to build a fully automated imaging farm which consists of multiple microscopes, robotic arms, liquid handling robots and automatic incubators. Importantly, we run AI models in real-time to augment the microscopy views, generating artificial labels and annotations. It also allows generating feedback signals to or example, control the cell growth, differentiation, and drive the microscope to change field-of-views, illumination power and other experimental conditions in order to optimize the phototoxicity and capture rare events in live cells.

Overall, the long-term goal of the group is to create large-scale whole human cell models trained on existing multi-omics datasets and new data generated by the imaging farm. We envision the human cell models have a great potential in in-silico cell experimentation, drug discovery and contributing to a holistic and systematic understanding of the human cell.

Group Members

We are hiring PhDs and Postdocs, please reach out!

Last updated: 2024-03-25

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