The classical fractional crystallisation scenario for magma ocean solidification on the Moon suggests that its crust formed by flotation of light anorthite minerals on top of a liquid ocean, which has been used to explain the anorthositic composition of the lunar crust. However, this model points to rapid crustal formation over tens of million years and struggles to predict the age range of primitive ferroan anorthosites from 4.5 and 4.3 Ga. 

Here I will present a new paradigm of slushy magma ocean crystallisation in which crystals are suspended throughout the magma ocean, and the lunar crust forms by magmatic processes over several hundreds of thousand years.

We will then focus on the effects of the particular characteristics of this primary crust on the transport and eruption of magma on the Moon.

Snow densification and meltwater refreezing store water in alpine regions and transform snow into ice on the surface of glaciers. Despite their importance in determining snow-water equivalent and glacier-induced sea level rise, we still lack a complete understanding of the physical mechanisms underlying snow compaction and the infiltration of meltwater into snowpacks. Here we (i) analyze snow compaction experiments as a promising direction for determining the rheology of snow though its many stages of densification and (ii) solve for the motion of refreezing fronts and for the temperature increase due to the release of latent heat, which we compare to temperature observations from the Greenland Ice Sheet (Humphrey et al., 2012). In the first part, we derive a mixture theory for compaction and air flow through the porous snow (cf. Hewitt et al. 2016) to compare against laboratory data (Wang and Baker, 2013). We find that a plastic compaction law explains experimental results. Taking standard forms for the permeability and effective pressure as functions of the porosity, we show that this compaction mode persists for a range of densities and overburden loads (Meyer et al., 2020). We motivate the second part of the talk by the observed melting at high elevations on the Greenland Ice Sheet, which causes the refreezing layers that are observed in ice cores. Our analysis shows that as surface temperatures increase, the capacity for meltwater storage in snow decreases and surface runoff increases leading to sea level rise (Meyer and Hewitt, 2017). Together these studies provide a holistic picture for how snow changes through compaction and the role of meltwater percolation in altering the temperature and density structure of surface snow.

We construct a class of Cauchy initial data without (marginally) trapped surfaces whose future evolution is a trapped region bounded by an apparent horizon, i.e., a smooth hypersurface foliated by MOTS. The estimates obtained in the evolution lead to the following conditional statement: if Kerr Stability holds, then this kind of initial data yields a class of scale critical vacuum examples of Weak Cosmic Censorship and the Final State Conjecture. Moreover, owing to estimates for the ADM mass of the data and the area of the MOTS, the construction gives a fully dynamical vacuum setting in which to study the Spacetime Penrose Inequality. We show that the inequality is satisfied for an open region in the Cauchy development of this kind of initial data, which itself is controllable by the initial data. This is joint work with Nikos Athanasiou https://arxiv.org/abs/2009.03704.

I will explain what it means for a manifold to have an affine structure and give an introduction to Benzecri's theorem stating that a closed surface admits an affine structure if and only if its Euler characteristic vanishes. I will also talk about an algebraic-topological generalization, due to Milnor and Wood, that bounds the Euler class of a flat circle bundle. No prior familiarity with the concepts is necessary.

I will discuss the notion of invariably generated groups, its importance, and some intuition. I will then present a construction of an invariably generated group that admits an index two subgroup that is not invariably generated. The construction answers questions of Wiegold and of Kantor-Lubotzky-Shalev. This is a joint work with Nir Lazarovich.

'Quasi-isometric rigidity' in group theory is the slogan for questions of the following nature: let A be some class of groups (e.g. finitely presented groups). Suppose an abstract group H is quasi-isometric to a group in A: does it imply that H is in A? Such statements link the coarse geometry of a group with its algebraic structure. 

Much is known in the case A is some class of lattices in a given Lie group. I will present classical results and outline ideas in their proofs, emphasizing the geometric nature of the proofs. I will focus on one key ingredient, the quasi-flat rigidity, and discuss some geometric objects that come into play, such as neutered spaces, asymptotic cones and buildings. I will end the talk with recent developments and possible generalizations of these results and ideas.

Primitive elements are elements that are part of a basis for a free group. We present the classical Whitehead algorithm for the recognition of such elements, and discuss the ideas behind the proof. We also present a second algorithm, more recent and completely different in the approach.

What do random spanning trees, graph embeddings, random walks, simplices and graph curvature have in common? As you may have guessed from the title, they are indeed all intimately connected to the effective resistance on graphs! While originally invented as a tool to study electrical circuits, the effective resistance has proven time and again to be a graph characteristic with a variety of interesting and often surprising properties. Starting from a number of equivalent but complementary definitions of the effective resistance, we will take a stroll through some classical theorems (Rayleigh monotonicity, Foster's theorem), a few modern results (Klein's metricity, Fiedler's graph-simplex correspondence) and finally discuss number of recent developments (variance on graphs, discrete curvature and graph embeddings).

Topological data analysis has proven to be an effective tool in machine learning, supporting the analysis of neural networks, but also driving the development of new algorithms that make use of topological features. Graph classification is of particular interest here, since graphs are inherently amenable to a topological description in terms of their connected components and cycles. This talk will briefly summarise recent advances in topology-based graph classification, focussing equally on ’shallow’ and ‘deep’ approaches. Starting from an intuitive description of persistent homology, we will discuss how to incorporate topological features into the Weisfeiler–Lehman colour refinement scheme, thus obtaining a simple feature-based graph classification algorithm. We will then build a bridge to graph neural networks and demonstrate a topological variant of ‘readout’ functions, which can be learned in an end-to-end fashion. Care has been taken to make the talk accessible to an audience that might not have been exposed to machine learning or topological data analysis.