Jeff Calder

Albert and Dorothy Marden Professor
School of Mathematics
University of Minnesota
538 Vincent Hall
Phone: 612-626-1324
Email: jwcalder at umn dot edu

News

  • My book Linear Algebra, Data Science and Machine Learning, co-authored with Peter Olver, will be published by Springer in August, 2025. The book is for an advanced undergraduate course on the mathematics of machine learning, and is used for Math 5465/5466 at the University of Minnesota. The book includes a set of companion Python notebooks to help students explore applications. Please see the [Official Website], [Our Website], and the [Github Website]. If you are interested in adopting the texbook, please reach out with any questions.
  • You can find my Python package GraphLearning on [GitHub] [PyPI] with full documentation.
  • Here are the slides (Clustering, PageRank, t-SNE, Poisson Learning) from my lectures in the June 2021 Summer School on Random Structures in Optimizations and Related Applications at the University of Minnesota.
  • Here are the Slides and Code from my BYU lecture on Graph-based learning.
  • Here are some notes I have written on Mathematics of Image and Data Analysis for a senior level undergraduate course (Math 5467).
  • If you are looking for an advisor for a senior capstone project, see here.
  • I am a co-founder of the AMAAZE consortium for mathematics and anthropology.
  • Here are some notes I have written on Viscosity Solutions and the Calculus of Variations.
  • Slides from recent talks can be found on my Talks page.
  • Code for all papers can be found on my Publications page.
  • Here are the slides and lecture notes from my minicourse on PDEs for Data Peeling given at UC Berkeley on May 22--25, 2017 and at the GSSI in L'Aquila, Italy on Sept 6-8, 2017.

About Me

I am an Associate Professor of Mathematics at the University of Minnesota. My research involves interactions between partial differential equations (PDE), numerical analysis, applied probability, and computer science. I am interested in both the rigorous analysis of PDE, and the development and implementation of algorithms. It is exciting when mathematical analysis and insights can lead to more efficient algorithms. My research is supported by the National Science Foundation, the Alfred P. Sloan Foundation, and the McKnight Foundation. You can read more on my research page.