Projects per year
Personal profile
Research interests
My research involves interactions between partial differential equations (PDE), numerical schemes, 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. A continuing theme in my current research is finding continuum limits for discrete combinatorial problems. I am particularly interested when these continuum limits involve solving nonlinear PDEs, and have applications in science and engineering.
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The Fingerprint is created by mining the titles and abstracts of the person's research outputs and projects/funding awards to create an index of weighted terms from discipline-specific thesauri.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Math-DT: Advancing the Mathematical Foundations for Dynamic Digital Twinning of Next-Generation Mobile Wireless Networks
Calder, J. (PI) & Lu, Y. (CoI)
THE NATIONAL SCIENCE FOUNDATION
1/1/25 → 12/31/27
Project: Research project
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MoDL: Analytical Foundations for Deep Learning and Inference over Graphs
Calder, J. (PI)
THE NATIONAL SCIENCE FOUNDATION
7/1/22 → 6/30/26
Project: Research project
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CAREER: Harnessing the continuum for big data: Partial differential equations, calculus of variations and machine learning
Calder, J. (PI)
THE NATIONAL SCIENCE FOUNDATION
7/1/20 → 6/30/26
Project: Research project
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Geometric Analysis for Classification and Reassembly of Broken Bones
Olver, P. J. (PI) & Calder, J. (CoI)
THE NATIONAL SCIENCE FOUNDATION
9/1/18 → 8/31/21
Project: Research project
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Numerical solution of a PDE arising from prediction with expert advice
Calder, J., Drenska, N. & Mosaphir, D., 2025, (Accepted/In press) In: European Journal of Applied Mathematics.Research output: Contribution to journal › Article › peer-review
Open Access -
Bayesian Active Learning for Sample Efficient 5G Radio Map Reconstruction
Polyzos, K. D., Sadeghi, A., Ye, W., Sleder, S., Houssou, K., Calder, J., Zhang, Z. L. & Giannakis, G. B., 2024, In: IEEE Transactions on Wireless Communications. 23, 12, p. 19382-19396 15 p.Research output: Contribution to journal › Article › peer-review
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Consistency of Semi-supervised Learning, Stochastic Tug-of-War Games, and the p-Laplacian
Calder, J. & Drenska, N., 2024, Modeling and Simulation in Science, Engineering and Technology. Birkhauser, p. 1-53 53 p. (Modeling and Simulation in Science, Engineering and Technology; vol. Part F3944).Research output: Chapter in Book/Report/Conference proceeding › Chapter
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Monotone discretizations of levelset convex geometric PDEs
Calder, J. & Lee, W., Dec 2024, In: Numerische Mathematik. 156, 6, p. 1987-2029 43 p.Research output: Contribution to journal › Article › peer-review
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RATIO CONVERGENCE RATES FOR EUCLIDEAN FIRST-PASSAGE PERCOLATION: APPLICATIONS TO THE GRAPH INFINITY LAPLACIAN
Bungert, L., Calder, J. & Roith, T., Aug 2024, In: Annals of Applied Probability. 34, 4, p. 3870-3910 41 p.Research output: Contribution to journal › Article › peer-review
Open Access2 Scopus citations
Datasets
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GraphLearning Python Package
Calder, J., ZENODO, 2022
DOI: 10.5281/zenodo.5850940, https://zenodo.org/record/5850940
Dataset