Title: Collective motion in the mitotic spindle Abstract: Math models of interacting individuals moving as a collective have been profoundly successful in describing physical and social phenomena ranging from swarming insects to human crowds. Especially in molecular biology, recent advances in machine-learning-based automated tracking have led to droves of new data of collective motion. I’ll discuss two […]
We will discuss the sparsity of the solutions to systems of linear Diophantine equations with and without non-negativity constraints. The sparsity of a solution vector is the number of its […]
Title: Collective Behavior in Locust Swarms from Data to Differential Equations Prof. Jasper Weinburd Department of Mathematics Harvey Mudd College Abstract: Locusts are devastating pests that infest and […]
No applied math talk
Title: TBA Abstract: TBA
In this talk we discuss some problems related to finding large induced subgraphs of a given graph G which satisfy some degree-constraints (for example, all degrees are odd, or all […]
Title: A tribute to Professor Ellis Cumberbatch (1934-2021) Abstract: The math colloquium on December 1st will be devoted to remembrances of our beloved CGU colleague Professor Ellis Cumberbatch, a pillar […]
Let d >= 2 be a natural number. We determine the minimum possible size of the difference set A-A in terms of |A| for any sufficiently large finite subset A […]
Title: Where do Putnam problems come from? Speaker: Andrew Bernoff, Department of Mathematics, Harvey Mudd College Abstract: The William Lowell Putnam Exam is the preeminent mathematics competition for undergraduate college students […]
The set of subsets {1, 3}, {1, 3, 4}, {1, 3, 4, 6} is a symmetric chain in the partially ordered set (poset) of subsets of {1,...,6}. It is a […]
Title: Using Stitching for faster sampling Speaker: Mark Huber, Department of Mathematics, Claremont McKenna College Abstract: Point processes are used to model location data, such as the locations of trees in a forest, […]
Archetypal analysis is an unsupervised learning method that uses a convex polytope to summarize multivariate data. For fixed k, the method finds a convex polytope with k vertices, called archetype points, such that the polytope is contained in the convex hull of the data and the mean squared distance between the data and the polytope […]
