Summaries for Day 3 are contributed by Rachael Phillips.

**Jeff Erickson **talked about efficiently hex-meshing things with topology. With a hex mesh, a polyhedra with six quadrilateral facets, there can be a quadrilateral mesh that can be extended to a hexahedral mesh of the interior volume. This can only happen when there are an even amount of quadrilaterals and none of the cycles are odd for the hex mesh. If such a mesh exists, then a polyhedron in 3-dimensional Euclidean space with quadrilateral facets can be constructed in polynomial time. These are extended to domains that have disconnected boundaries and are continued from Thurston, Mitchell, and Eppstein, where the odd cycle criteria is trivial. The idea is to look at a quadrilateral figure and extend that figure to the interior. So, the importance is not in the shape of that figure, since we are not looking at the geometry of this figure, but the topology.

**Jose Perea** talked about Obstructions to Compatible Extensions of Mappings. From Betti numbers in 1994 to Zig-Zag persistence in 2009, there have been several classic invariants in algebraic topology. The basic ones being from a point cloud, constructing a filtration and using that filtration to compute Betti numbers, which tell us about the number of k-dimensional holes within a metric space. Instead of this, it would be useful to come up with new ways of encoding multi-scale information from data. The main goal is to be able to fit our data into a model for the best methods. Using extending sections and the retraction problem, Mumford data is used to fit the model. The question is, how far do you have to go for the model to be good? From local to global, the model tells us the death-like events, where an example would be compatible extensions. The birth-like events where the filtration of each level extends to the next. Once these models are found, it extends compatibility once the model has been extended. The main goal being to extend the previous invariant methods to new invariant methods for data analysis.

**Donald Sheehy** talked about Nested Dissection and (Persistent) Homology. Using nested dissection, this is a way of solving systems of linear equations. This method is an improvement of the naive Gaussian elimination. The reason that it is important to improve Gaussian elimination is because it is a long process that takes a lot of computer memory. It is a method that needs to be improved when using computers to solve it. By building a filtered simplicial complex and computing the persistent homology, we can try to speed up the process of elimination. Normally, Gaussian elimination has a running time of $$O(n^3)$$, or even worse using Strassen it is a running time of $$O(n^{\log_27})$$. Nested Dissection removes a random column in a matrix and separates the graph into two pieces. When a matrix is separated in two pieces, it improves the matrix multiplication and using topology, while doing Gaussian on the boundary. The nested dissection computes the persistent homology of the space of the matrix. Using four methods such as Mesh Filtrations, Nested Dissection, Geometric Separation and Output sensitive persistence algorithm, there is a theorem that improves the asymptotic running time of the persistence algorithm.

**Shmuel Weinberger** talked about Complex and Simple “Topological” invariants. It is common in the study of topological data analysis that the values of topological invariants are discussed. When you calculate homology it does not always give you enough information. The goal is not to look at the dynamics, but learn about dynamics. It is possible to look at the persistent homology of any data set, unfortunately, conditions on the noise, “invariance” with high-probability. It is important is to find examples of Probability Approximately Correct (PAC) computable ideas. Knowing when noise is a problem and when it is not is the main goal of this talk.