Jeffrey Calder

College of Science & Engineering
Twin Cities

Award Year
University Award
McKnight Presidential Fellow Award
Research Title
Continuum limits in machine learning and data science

No Monument Location

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.