Dr. Carpenter and his team developed a machine learning framework to improve reviewer-proposal assignments in ALMA’s distributed peer review system. By using topic models trained on past proposals, they can represent both proposals and reviewer expertise in the same space, measure their similarity, and optimize assignments with the PeerReview4All algorithm.

This approach has led to better matches, more reviewers identifying themselves as experts, and the removal of manual reassignments. In this talk, I will outline the method, highlight performance results, and discuss possible next steps.