Machine Learning for Reviewer-Proposal Matching in ALMA Distributed Peer Review
Автор: NSF-Simons AI Institute for Cosmic Origins
Загружено: 2025-12-01
Просмотров: 9
Presenter: John Carpenter (ALMA Observatory Scientist)
Part 1 Abstract We 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, we 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.
John Carpenter obtained his Bachelor’s degree in Astronomy from the University of Wisconsin–Madison and his PhD from the University of Massachusetts–Amherst. He was a JCMT Fellow at the University of Hawai‘i before joining Caltech’s Owens Valley Radio Observatory, where he contributed to the formation of the CARMA interferometer and eventually served as Executive Director. Since 2015, he has been the Observatory Scientist at the Joint ALMA Observatory in Chile, overseeing the proposal review process. His research centers on the formation and evolution of protoplanetary disks, particularly through submillimeter observations with ALMA.
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