Jeffrey Calder

Jeff Calder is a mathematician whose research involves using continuum mathematical analysis, including partial differential equations and the calculus of variations, to study discrete problems at the foundations of machine learning and data science. The goals of his research are to further our understanding of existing algorithms, and to develop new, more efficient algorithms founded on strong theoretical principles with provable performance guarantees. He has made foundational contributions to many problems, including the sorting of multivariate data with data peeling algorithms, and graph-based semi-supervised learning with limited data.