deep-learning

An agent-first platform that deploys bioimage AI models as autoscaling, agent-readable interfaces on cloud or HPC — and powers the “test run” feature on the BioImage Model Zoo. Described in a 2026 preprint.

A community-driven, fully open repository where standardized deep-learning models can be shared, tested in the browser, and deployed across imaging tools.

Cell morphology, learned from image-based spatial proteomics, is shown to predict molecular phenotypes — bridging single-cell imaging and proteomics.

A universal deep-learning approach for accurate FISH spot detection across imaging-based spatial-omics datasets.

A self-driving cell-biology lab where AI agents plan, run and adapt live-cell experiments across a cluster of instruments.

A framework that makes deep-learning pipelines for microscopy flexible, shareable, and reproducible across computing environments.

We’re excited to welcome Nils Mechtel as our second PhD student at AICell Lab. His expertise in computational biology and deep learning will play a crucial role in advancing our human cell simulator project.

Building AI-powered data-driven human whole-cell models

A computational platform for supercharging scalability and interactivity

Building an AI-powered self-driving microscopy system for studying cellular response