Jeff Calder

Associate Professor of Mathematics
538 Vincent Hall
School of Mathematics
University of Minnesota
Phone: 612-626-1324
Email: jcalder at umn dot edu

Recent Talks

Below is a list of recent talks that I have given, along with the slides and links to videos of the talks, when available. My recent IMA Data Science seminar talk, available here, describes a lot of my research on graph learning over the past few years. For a full list please refer to my CV.
1. Boundary estimation and Hamilton-Jacobi equations on point clouds [Slides]
 
Minisymposium on Graphs, Geometry, PDEs, and Learning, SIAM Conference on Mathematics of Data Science, September 2022.
Minisymposium on Advances in Variational Methods and Applications to Materials and Machine Learning, SIAM Annual Meeting, July 2022.
Mathematical Data Science Seminar, Purdue University, April 2022.
Mathematics Colloquium, University of Utah, April 2022.
Minisymposium on The Geometry of PDEs on Graphs: Analysis and Applications, SIAM Conference on Analysis of PDEs, March 2022.
Hamilton-Jacobi PDEs Reunion Conference I, Institute for Pure and Applied Mathematics, January 2022.
 
2. Uniform convergence rates for Lipschitz learning down to graph connectivity
 
Workshop on Dynamics and Discretization: PDEs, Sampling, and Optimization, Simons Institute for the Theory of Computing, October 2021. [Video]
 
3. Random walks and PDEs in graph-based learning [Slides]
 
Applied Mathematics Seminar, University of Texas at San Antonio, November 2021.
Seminar on the Mathematics of Deep Learning, FAU Erlangen-Nürnberg, May 2021.
Applied Mathematics Seminar, Courant Institute, New York University, April 2021
The Mathematics of Machine Learning, One World Seminar Series, March 2021 [Video]
Computational and Applied Mathematics Seminar, Tufts University, March 2021.
Applied Mathematics Colloquium, New Jersey Institute of Technology, January 2021.
Mathematics Colloquium, University of Toronto, January 2021.
CSE/DTC Machine Learning Seminar, University of Minnesota, Sept 2020 [Video]
School of Mathematics Colloquium, University of Minnesota, Sept 2020.
Mathematics in Imaging, Data and Optimization Seminar, Rensselaer Polytechnic Institute (RPI), Sept 2020.
Workshop on PDE and Inverse Problem Methods in Machine Learning, Institute for Pure and Applied Mathematics, April 2020.
Center for Nonlinear Analysis Seminar, Carnegie Mellon University, February 2020.
 
4. Poisson Learning: A framework for graph-based semi-supervised learning at very low label rates [Slides]
 
Minisymposium on Theory and applications of graph-based learning, SIAM Conference on Computational Science and Engineering, March 2021.
International Conference on Machine Learning (ICML), July 2020
 
5. PDE continuum limits for prediction with expert advice [Slides]
 
ICMS Workshop on Analytic and Geometric Approaches to Machine Learning, University of Bath, July 2021.
Nonlinear Analysis Seminar, Rutgers University, April 2021.
Stochastics and PDEs Seminar, University of Jyväskylä, March 2021.
Applied and Computational Analysis Seminar, University of Cambridge, June 2019.
Workshop on Inverse Problems and Machine Learning, Center de reserches mathematiques, Montreal, May 2019.
 
6. Discrete regularity for graph Laplacians [Slides]
 
Workshop on Stochastic Analysis Related to Hamilton-Jacobi PDEs, Institute for Pure and Applied Mathematics, May 2020. [Video]
 
7. Computation of integral invariants for geometry processing with applications to analysis of broken bone fragments [Slides]
 
Symposium on Computational Modeling and Image Processing of Biomedical Problems, Michigan Technological University, June 2019.
 
8. Nonlinear PDE continuum limits in data science and machine learning [Slides]
 
PDE & Geometric Analysis Seminar, University of Wisconsin Madison, April 2018.
 
9. Introduction to Concentration of Measure with applications to graph-based learning.
 
Long program on High Dimensional Hamilton-Jacobi Equations, Institute for Pure and Applied Mathematics (IPAM). Video: [Part 1][Part 2]