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--- meetings_spring_2026 [2026/03/07 16:45] – asjensen
+++ meetings_spring_2026 [2026/04/07 13:58] (current) – asjensen
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 | February 25  | Base Modulus for Matroid Truncation, Strength, and Fractional Arboricity | Huy Truong | Kansas State University | [[#february_25|Abstract]] |
 | March 4      | Modulus of Families of Lipschitz Chains with Arbitrary Dimension and Codimension | Andrew Jensen | Kansas State University | [[#march_4|Abstract]] |
-| March 11     |  |  |  |  |
+| March 11     | **NO MEETING** |  |  |  |
-| March 18     | **NO MEETING** | //Spring Break// | | |
+| March 18     | **NO MEETING** | | | |
-| March 25     |  |  | | |
+| March 25     | **NO MEETING** |  | | |
-| April 1      |  |  | | |
+| April 1      | **NO MEETING** |  | | |
-| April 8      |  |  | | |
+| April 8      | Hamilton-Jacobi Equations on Graphs with Applications to Semi-Supervised Learning and Data Depth  | Andrew Jensen | Kansas State University | [[#april_8_|Abstract]] |
 | April 15     |  |  | | |
 | April 22     |  |  | | |
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 **"Modulus of Families of Lipschitz Chains with Arbitrary Dimension and Codimension", Andrew Jensen**\\
 Recently, Lohvansuu (2023) introduced the p-modulus for families of k-dimensional Lipschitz chains and their dual families of (n-k)-dimensional chains. While he established an upper bound for the duality of these families on Lipschitz cubes, the corresponding lower bound remained an open question. Subsequently, Kangasniemi and Prywes (2025) developed dMod, a related notion of modulus based on differential forms, and successfully established a full duality result. In this talk, I will explore the implications of these developments and discuss related open problems.
+==== April 8 ====
+**"Hamilton-Jacobi Equations on Graphs with Applications to Semi-Supervised Learning and Data Depth", Andrew Jensen**\\
+We will be reading through a paper by Jeff Calder and Mahmood Ettehad discussing how the p-Eikonal Equation on graphs allows one to recover distances on graphs, and in particular p -> infinity recovers shortest-path graph distance. The authors then apply the finding in machine learning contexts. I will briefly share an overview of the paper's results, then the remaining portion of the meeting will be a group discussion on the paper. You can find the paper at https://arxiv.org/abs/2202.08789.