• Thanksgiving Week

    Emmy Noether Room, Millikan 1021, Pomona College 610 N. College Ave., Claremont, California

    No applied math talk

  • Odd subgraphs are odd (Asaf Ferber, UC Irvine)

    On Zoom

    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 degrees are j mod k, etc). We survey some classical results, present some interesting and challenging problems, and sketch solutions to some of them. This […]

  • A tribute to Professor Ellis Cumberbatch (1934-2021)

    Zoom

    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 of the Claremont mathematics community, who passed away in September. Three brief talks by his friends and collaborators, Professor John Ockendon (University of Oxford), Dr. […]

  • Difference sets in higher dimensions (David Conlon, Cal Tech)

    On Zoom

    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 of R^d that is not contained in a translate of a hyperplane. By a construction of Stanchescu, this is best possible and thus resolves an […]

  • Where do Putnam problems come from? (Prof. Andrew Bernoff)

    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 in the United States and Canada. I recently finished a three year stint on the competition’s problem committee. This talk is a personal reflection on […]

  • Using Stitching for faster sampling (Prof. Mark Huber)

    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, or cities in a plain.  Repulsive point processes modify the basic model in order to obtain points that are farther apart from each other than would […]

  • APPLIED MATH SEMINAR: Archetypal analysis by Professor Braxton Osting (University of Utah)

    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 […]