Learn how we're thinking about benchmarking!

At CZI, we are interested in providing the infrastructure and methods support to advance biology-centric benchmarking, therefore bridging the gap between model developers and model users and advancing AI in Biology. Read about a few of our initial collaborations and projects in this domain!

Insights from the CZI-hosted Benchmarking and Evaluation Workshop

Learn about insights from a recent CZI workshop on building robust benchmarking for AI in biology

Designing a ML Competition for CryoET Data with Limited Annotations

Take a behind-the-scenes look at Chan Zuckerberg Imaging Institute's efforts in the development and hosting of an ML competition to boost cryo-electron tomography particle detection.

Introducing the Biological Bias Assessment Guide for AI in Biology

A compilation of resources that offer a framework for identifying and addressing biases in AI models for biology.