## Idea We're going to have a seminar on applied category theory here at U. C. Riverside, starting in January 2019. We will make it easy to have discussions on the Azimuth Forum and Azimuth Blog. These will work best if you read the papers we're talking about and then join these discussions. We will also try to videotape the talks, to make it easier for you to follow along. Here's how the schedule of talks is shaping up so far. ### January 8, 2019: John Baez - Mathematics in the 21st century ### John Baez wil give an updated synthesized version of these earlier talks of his, so check out these slides and the links: * <a href="http://math.ucr.edu/home/baez/planet/planet_massey.pdf">The mathematics of planet Earth</a>. * <a href="http://math.ucr.edu/home/baez/balsillie/balsillie_what.pdf">What is climate change?</a> * <a href="http://math.ucr.edu/home/baez/ACT2018/">Props in network theory</a>. ### January 15, 2019: Jonathan Lorand - Problems in symplectic linear algebra ### Lorand is visiting U. C. Riverside to work with Baez on applications of symplectic geometry to chemistry. His talk will be about other research of his: > In this talk we will look at various examples of classification problems in symplectic linear algebra: conjugacy classes in the symplectic group and its Lie algebra, linear lagrangian relations up to conjugation, tuples of (co)isotropic subspaces. I will explain how many such problems can be encoded using the theory of symplectic poset representations, and will discuss some general results of this theory. Finally, I will recast this discussion from a broader category-theoretic perspective. ### January 22, 2019: Christina Vasilakopoulou - Wiring diagrams ### Vasilakopoulou, a visiting professor at U.C. Riverside, previously worked with David Spivak. So, we really want to figure out how two frameworks for dealing with networks relate: Brendan Fong's 'decorated cospans', and Spivak's 'monoidal category of wiring diagrams'. Vasilakopoulou will give a talk on systems as algebras for the wiring diagram monoidal category. It will be based on this paper: * Patrick Schultz, David I. Spivak and Christina Vasilakopoulou, <a href="https://arxiv.org/abs/1609.08086"> Dynamical systems and sheaves</a>. but she will focus more on the algebraic description (and conditions for deterministic/total systems) rather than the sheaf theoretic aspect of the input types. This work builds on earlier papers such as these: * David I. Spivak, <a href="https://arxiv.org/abs/1305.0297">The operad of wiring diagrams: formalizing a graphical language for databases, recursion, and plug-and-play circuits</a>. * Dmitry Vagner, David I. Spivak and Eugene Lerman, <a href="https://arxiv.org/abs/1408.1598">Algebras of open dynamical systems on the operad of wiring diagrams</a>. ### January 29, 2019: Daniel Cicala - Dynamical systems on networks ### Cicala will discuss a topic from this paper: * Mason A. Porter and James P. Gleeson, <a href="https://arxiv.org/abs/1403.7663">Dynamical systems on networks: a tutorial</a>. His leading choice is a model for social contagion (e.g. opinions) which is discussed in more detail here: * Duncan J. Watts, <a href="https://www.stat.berkeley.edu/~aldous/260-FMIE/Papers/watts.pdf">A simple model of global cascades on random networks</a>. ### February 5, 2019: Jade Master - Backprop as functor: a compositional perspective on supervised learning ### Here is Jade's abstract: > Fong, Spivak and Tuyéras have found a categorical framework in which gradient descent algorithms can be constructed in a compositional way. To explain this, we first give a brief introduction to backprogation and gradient descent. We then describe their monoidal category $Learn$, where the morphisms are given by abstract learning algorithms. Finally, we show how gradient descent can be realized as a monoidal functor from $Para$, the category of Euclidean spaces with differentiable parameterized functions between them, to $Learn$. Her talk will be based on this paper: &bull; Brendan Fong, David I. Spivak and Rémy Tuyéras <a href="https://arxiv.org/abs/1711.10455" rel="nofollow">Backprop as functor: a compositional perspective on supervised learning</a>. category:courses