Jeff Calder

Jeff Calder is an Associate Professor in the School of Mathematics at the University of Minnesota. He specializes in interactions between partial differential equations (PDE), numerical analysis, applied probability, and computer science, with applications to machine learning and data analysis. His research has been supported by the National Science Foundation, Alfred P. Sloan Foundation, and McKnight Foundation.

• Ph.D. in Applied and Interdisciplinary Mathematics (2014, University of Michigan)
• Morrey Assistant Professor (2014-2016, UC Berkeley)

His research interests include:

• Rigorous analysis of PDEs
• Algorithm development for machine learning
• Variational methods in data science
• Graph-based learning techniques
• Computational biomedical imaging
• Interdisciplinary applications in anthropology

Recent publications focus on t-SNE improvements, medical imaging segmentation, and variational PDEs for inversion problems. His work spans computer science, applied mathematics, and biomedical engineering.

Scientific awards include:

• NSF Career Award (2020)
• Sloan Research Fellowship (2020)
• McKnight Presidential Fellowship (2021)
• Guillermo E. Borja Award (2021)
• Albert and Dorothy Marden Professorship (2023-2025)

He supervises graduate and undergraduate research projects and co-founded the AMAAZE consortium for mathematics-anthropology collaboration. His GraphLearning Python package provides open-source tools for graph-based machine learning.