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

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. Partial Differential Equations and graph-based learning [Slides]: Applied Mathematics Colloquium, Columbia University, April 2023.; Center for Nonlinear Analysis Seminar, Carnegie Mellon University, January 2023.; Scientific Computing and Numerics (SCAN) Seminar, Cornell University, October, 2022.
2. Discrete to continuum convergence rates in graph-based learning at percolation length scales [Slides]: Special Session on Data Science at the Crossroads of Analysis, Geometry, and Topology , Joint Mathematics Meeting, January 2023
3. Boundary estimation and Hamilton-Jacobi equations on point clouds [Slides]: IPAM Reunion Workshop on High Dimensional Hamilton-Jacobi Equations, December 2022.; 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.
4. 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]
5. 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.
6. 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
7. 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.
8. Discrete regularity for graph Laplacians [Slides]: Workshop on Stochastic Analysis Related to Hamilton-Jacobi PDEs, Institute for Pure and Applied Mathematics, May 2020. [Video]
9. 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.
10. Nonlinear PDE continuum limits in data science and machine learning [Slides]: PDE & Geometric Analysis Seminar, University of Wisconsin Madison, April 2018.
11. 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]