The problem of peer review is at the center of how science is formalized and propagated. It affects knowledge generation via its impact on dissemination, credit assignment, promotions, funding etc. Yet scientists don’t view the review process as something that should be engineered. The match between reviewers and manuscripts has rarely been analyzed and has never been optimized to minimize bias and variance. Kording and his team will analyze a dataset of manuscripts, reviewers, and editor ratings for the over-8000 neuroscience papers submitted to a major journal (PLOS One). Their goal is to quantify bias and variance, offer solutions to how to minimize them, and to optimize the review process with respect to other criteria like research novelty and target audience. Ultimately, improving the process of reviewer assignment could make knowledge generation more efficient.