I will introduce asymptotic safety, which is a quantum field theoretic
paradigm providing a predictive ultraviolet completion for quantum field
theories. I will show examples of asymptotically safe theories and then
discuss the search for asymptotically safe models that include quantum
gravity.
In particular, I will explain how asymptotic safety corresponds to a new
symmetry principle - quantum scale symmetry - that has a high predictive
power. In the examples I will discuss, asymptotic safety with gravity could
enable a first-principles calculation of Yukawa couplings, e.g., in the
quark sector of the Standard Model, as well as in dark matter models.

The path signature is a characterization of paths that originated in Chen's iterated integral cochain model for path spaces and loop spaces. More recently, it has been used to form the foundations of rough paths in stochastic analysis, and provides an effective feature map for sequential data in machine learning. In this talk, we return to the topological foundations in Chen's construction to develop generalizations of the signature.

In this talk, I'll present inequalities bounding the number of critical cells in a filtered cell complex on the one hand, and the entries of the Betti tables of the multi-parameter persistence modules of such filtrations on the other hand. Using the Mayer-Vietoris spectral sequence we first obtain strong and weak Morse inequalities involving the above quantities, and then we improve the weak inequalities achieving a sharp lower bound for the number of critical cells. Furthermore, we prove a sharp upper bound for the minimal number of critical cells, expressed again in terms of the entries of Betti tables. This is joint work with Andrea Guidolin (KTH, Stockholm). The full paper is posted online as arxiv:2108.11427.

What do we use metrics on persistent modules for? Is it only to asure  stability of some constructions? 

In my talk I will describe why I care about such metrics, show how to construct a rich space of them and illustrate how  to use

them for analysis. 

We will consider modeling shapes and fields via topological and lifted-topological transforms. 

Specifically, we show how the Euler Characteristic Transform and the Lifted Euler Characteristic Transform can be used in practice for statistical analysis of shape and field data. The Lifted Euler Characteristic is an alternative to the. Euler calculus developed by Ghrist and Baryshnikov for real valued functions. We also state a moduli space of shapes for which we can provide a complexity metric for the shapes. We also provide a sheaf theoretic construction of shape space that does not require diffeomorphisms or correspondence. A direct result of this sheaf theoretic construction is that in three dimensions for meshes, 0-dimensional homology is enough to characterize the shape.