Jeffrey Calder

Jeffrey Calder is a Professor at the School of Mathematics, University of Minnesota, since 2025. His work bridges partial differential equations (PDEs), numerical analysis, applied probability, and computer science to develop theoretically grounded algorithms for machine learning and data science. He focuses on continuum limits of discrete problems to understand algorithm behavior and improve efficiency.

• Education:
• Ph.D. in Applied and Interdisciplinary Mathematics (2014), University of Michigan, advised by Selim Esedoglu and Alfred Hero
• Morrey Assistant Professor at UC Berkeley (2014-2016), mentored by Lawrence C. Evans and James Sethian

Research Interests: Calder’s research centers on using PDEs and the calculus of variations to analyze foundational problems in machine learning and data science. He emphasizes proving large-sample-size continuum limits to rigorously explain algorithm performance and derive new methods with guaranteed efficiency. Key areas include graph-based learning, optimization, and numerical analysis.

Scientific Awards:

• NSF Career Award (2020)
• Alfred P. Sloan Research Fellowship (2020)
• McKnight Presidential Fellowship (2021)
• Guillermo E. Borja Award (2021)
• Albert and Dorothy Marden Professorship (2023-2028)

Advising and Grants: Calder has supervised 5 Ph.D. students, 4 postdoctoral scholars, and numerous undergraduates and high school students. His research is funded by the National Science Foundation, Alfred P. Sloan Foundation, and McKnight Foundation. He co-founded the AMAAZE consortium for mathematics and anthropology collaborations.