Welcome to Mathematics

The Mathematics Department is committed to the development, teaching, and learning of mathematics at all levels. Our faculty are actively engaged in research in many areas of pure and applied mathematics. We offer undergraduate and graduate degrees that will prepare students for jobs in industry, education, or mathematical research. We also teach a wide range of introductory mathematics courses for students in science, engineering, business, and the liberal arts, and support those students with our Math Tutorial Lab.

  • Adam Wood - 2020 Bor-Luh Lin Thesis Award Winner
  • Sayan Das (left), Yeongseong Jo (right), and Shijie Zhu (middle)
  • Mathematics Graduate student writing on the board
  • The Mathematics Tutorial Lab inside the Department of Mathematics at the University of Iowa
  • A student studying mathematics
  • A graduate student in the Department of Mathematics
  • Thomas Kindred instructing students in mathematics
  • A student studying mathematics
  • A graduate student in the Department of Mathematics
  • Math Platoon - Free Math tutoring for Veterans and Military-connected students.

Noteworthy

  • Women in Math Colloquium Series - Carina Curto

    Thursday November 5th

    3:15–3:30 (Virtual Tea)

    3:30–4:30 Talk by Carina Curto (Penn State)

    Zoom ID: https://uiowa.zoom.us/j/97356340747

    Title: Graphs, network motifs, and threshold-linear algebra in the brain

    Abstract: Threshold-linear networks (TLNs) are commonly-used rate models for modeling neural networks in the brain. Although the nonlinearity is quite simple, it leads to rich dynamics that can capture a variety of phenomena observed in neural activity: persistent activity, multistability, sequences, oscillations, etc. Here we study competitive threshold-linear networks, which exhibit both static and dynamic attractors. These networks have corresponding hyperplane arrangements whose oriented matroids encode important features of the dynamics. We will show how the graph associated to such a network yields constraints on the set of (stable and unstable) fixed points, and how these constraints affect the dynamics. In the special case of combinatorial threshold-linear networks (CTLNs), we find an even stronger set of "graph rules" that allow us to predict emergent sequences and to engineer networks with prescribed dynamic attractors.

    (This event is supported by the NSF (DMS-1844267).)

Mathematics Calendar

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