Call for Contributions: Multi-Analyst Study & Hackathon on Interpretable Machine Learning

We invite researchers to participate in an upcoming Multi-Analyst Study and in-person Hackathon on Interpretable Machine Learning for Longitudinal and Clustered Data, hosted in Dortmund.

The initiative focuses on applying and comparing interpretable machine learning approaches for dependent data structures, such as longitudinal and clustered designs. Participants will work with two provided datasets to evaluate methods collaboratively and contribute to the development of methodological guidelines for ML in such contexts.

Interested researchers are encouraged to submit a brief outline (max. 300 words) describing their proposed approach and preferred dataset(s). The submission deadline is April 20, 2026. Accepted participants will be invited to an in-person workshop on July 2, 2026 in Dortmund.

Further details, including information on datasets, aims, and the full timeline, can be found in the complete call for contributions: t3://file?uid=208638

We welcome broad participation and encourage colleagues to share this opportunity within their networks.