Past

We are surrounded by systems that involve many elements and the interactions between them: the air we breathe, the galaxies we watch, herds of animals roaming the African planes and even us – trying to decide on whom to vote for.

As common as such systems are, their mathematical investigation is far from simple. Motivated by the realisation that in most cases we are not truly interested in the individual behaviour of each and every element of the system but in the average behaviour of the ensemble and its elements, a new approach emerged in the late 1950s - the so-called mean field limits approach. The idea behind this approach is fairly intuitive: most systems we encounter in real life have some underlying pattern – a correlation relation between its elements. Giving a mathematical interpretation to a given phenomenon and its emerging pattern with an appropriate master/Liouville equation, together with such correlation relation, and taking into account the large number of elements in the system results in a limit equation that describes the behaviour of an average limit element of the system. With such equation, one hopes, we could try and understand better the original ensemble.

In our talk we will give the background to the formation of the ideas governing the mean field limit approach and focus on one of the original models that motivated the birth of the field – Kac’s particle system. We intend to introduce Kac’s model and its associated (asymptotic) correlation relation, chaos, and explore attempts to infer information from it to its mean field limit – The Boltzmann-Kac equation.

Conformal blocks appear in several areas of mathematical physics from random geometry to black hole physics. A probabilistic notion of conformal blocks using gaussian multiplicative chaos measures was recently formulated by Promit Ghosal, Guillaume Remy, Xin Sun, Yi Sun (arxiv:2003.03802). In this talk, I will show that the semiclassical limit of the probabilistic conformal blocks recovers a special case of the elliptic form of Painlevé VI equation, thereby proving a conjecture by Zamolodchikov. This talk is based on an upcoming paper with Promit Ghosal and Andrei Prokhorov.

Since their introduction by Gromov in the 80s, a wealth of tools have been developed to study hyperbolic groups. Thus, when studying a class of groups, a characterisation of those that are hyperbolic can be very useful. In this talk, we will turn to the class of one-relator groups. In previous work, we showed that a one-relator group not containing any Baumslag--Solitar subgroups is hyperbolic, provided it has a Magnus hierarchy in which no one-relator group with a so called `exceptional intersection' appears. I will define one-relator groups with exceptional intersection, discuss the aforementioned result and will then provide a characterisation of the hyperbolic one-relator groups with exceptional intersection. Finally, I will then discuss how this characterisation can be used to establish properties for all one-relator groups.

Rational functions are able to approximate functions containing branch point singularities with a root-exponential convergence rate. These appear for example in the solution of boundary value problems on domains containing corners or edges. Results from Newman in 1964 indicate that the poles of the optimal rational approximant are exponentially clustered near the branch point singularities. Trefethen and collaborators use this knowledge to linearize the approximation problem by fixing the poles in advance, giving rise to the Lightning approximation. The resulting approximation set is however highly ill-conditioned, which raises the question of stability. We show that augmenting the approximation set with polynomial terms greatly improves stability. This observation leads to a  decoupling of the approximation problem into two regimes, related to the singular and the smooth behaviour of the function. In addition, adding polynomial terms to the approximation set can result in a significant increase in convergence speed. The convergence rate is however very sensitive to the speed at which the clustered poles approach the singularity.

A Latin square of order n is an n by n grid filled with n different symbols so that every symbol occurs exactly once in each row and each column, while a transversal in a Latin square is a collection of cells which share no row, column or symbol. The Ryser-Brualdi-Stein conjecture states that every Latin square of order n should have a transversal with n-1 elements, and one with n elements if n is odd. In 2020, Keevash, Pokrovskiy, Sudakov and Yepremyan improved the long-standing best known bounds on this conjecture by showing that a transversal with n-O(log n/loglog n) elements exists in any Latin square of order n. In this talk, I will discuss how to show, for large n, that a transversal with n-1 elements always exists.

We outline Dirac cohomology for Lie algebras and Vogan’s conjecture. We then cover some basic material on Schur-Weyl duality and Arakawa-Suzuki functors. Finishing with current efforts and results on generalising Vogan’s conjecture to a Schur-Weyl duality setting. This would relate the centre of a Lie algebra with the centre of the relevant tantaliser algebra. We finish by considering a unitary module X and giving a bound on the action of the tantalizer algebra.

Most algorithms for computing a matrix function $f(A)$ are based on finding a rational (or polynomial) approximant $r(A)≈f(A)$ to the scalar function on the spectrum of $A$. These functions are often in a composite form, that is, $f(z)≈r(z)=r_k(⋯r_2(r_1(z)))$ (where $k$ is the number of compositions, which is often the iteration count, and proportional to the computational cost); this way $r$ is a rational function whose degree grows exponentially in $k$. I will review algorithms that fall into this category and highlight the remarkable power of composite (rational) functions.

Standard twistor theory involves a complex projective
3-space PT which naturally divides into two halves PT+
and PT–, joined by their common 5-real-dimensional
boundary PN. However, this splitting has two quite
different basic physical interpretations, namely
positive/negative helicity and positive/negative
frequency, which ought not to be confused in the
formalism, and the notion of “bi-twistors” is introduced
to resolve this issue. It is found that quantized bi-
twistors have a previously unnoticed G2* structure,
which enables the split-octonion algebra to be directly
formulated in terms of quantized bi-twistors, once the
appropriate complex structure is incorporated.

So-called Schauder estimates are a standard tool in the analysis of linear elliptic and parabolic PDE. They have been originally obtained by Hopf (1929, interior case), and by Schauder and Caccioppoli (1934, global estimates). The nonlinear case is a more recent achievement from the ’80s (Giaquinta & Giusti, Ivert, Lieberman, Manfredi). All these classical results hold in the uniformly elliptic framework. I will present the solution to the longstanding problem, open since the ‘70s, of proving estimates of such kind in the nonuniformly elliptic setting. I will also cover the case of nondifferentiable functionals and provide a complete regularity theory for a new double phase model. From joint work with Giuseppe Mingione (University of Parma).

In this talk, I will discuss the (non-)construction of the focusing Gibbs measures and the associated dynamical problems. This study was initiated by Lebowitz, Rose, and Speer (1988) and continued by Bourgain (1994), Brydges-Slade (1996), and Carlen-Fröhlich-Lebowitz (2016). In the one-dimensional setting, we consider the mass-critical case, where a critical mass threshold is given by the mass of the ground state on the real line. In this case, I will show that the Gibbs measure is indeed normalizable at the optimal mass threshold, thus answering an open question posed by Lebowitz, Rose, and Speer (1988).

In the three dimensional-setting, I will first discuss the construction of the $\Phi^3_3$-measure with a cubic interaction potential. This problem turns out to be critical, exhibiting a phase transition:normalizability in the weakly nonlinear regime and non-normalizability in the strongly nonlinear regime. Then, I will discuss the dynamical problem for the canonical stochastic quantization of the $\Phi^3_3$-measure, namely, the three-dimensional stochastic damped nonlinear wave equation with a quadratic nonlinearity forced by an additive space-time white noise (= the hyperbolic $\Phi^3_3$-model). As for the local theory, I will describe the paracontrolled approach to study stochastic nonlinear wave equations, introduced in my work with Gubinelli and Koch (2018). In the globalization part, I introduce a new, conceptually simple and straightforward approach, where we directly work with the (truncated) Gibbs measure, using the variational formula and ideas from theory of optimal transport.

The first part of the talk is based on a joint work with Philippe Sosoe (Cornell) and Leonardo Tolomeo (Bonn/Edinburgh), while the second part is based on a joint work with Mamoru Okamoto (Osaka) and Leonardo Tolomeo (Bonn/Edinburgh).

Ideal triangulations were introduced by Thurston as a tool for studying hyperbolic three-manifolds.  Taut ideal triangulations were introduced by Lackenby as a tool for studying "optimal" representatives of second homology classes.

After these applications in geometry and topology, it is time for dynamics. Veering triangulations (taut ideal triangulations with certain decorations) were introduced by Agol to study the mapping tori of pseudo-Anosov homeomorphisms.  Gueritaud gave an alternative construction, and then Agol and Gueritaud generalised it to find veering triangulations of three-manifolds admitting pseudo-Anosov flows (without perfect fits).

We prove the converse of their result: that is, from any veering triangulation we produce a canonical dynamic pair of branched surfaces (in the sense of Mosher).  These give flows on appropriate Dehn fillings of the original manifold.  Furthermore, our construction and that of Agol--Gueritaud are inverses.  This then gives a "perfect" combinatorialisation of pseudo-Anosov flow (without perfect fits).

This is joint work with Henry Segerman.

For a family of finite sets whose cardinalities are naturally called class numbers, the Gauss problem asks to determine the subfamily in which every member has class number one. We study the Siegel moduli space of abelian varieties in characteristic $p$ and solve the Gauss problem for the family of central leaves, which are the loci consisting of points whose associated $p$-divisible groups are isomorphic. Our solution involves mass formulae, computations of automorphism groups, and a careful analysis of Ekedahl-Oort strata in genus $4$. This geometric Gauss problem is closely related to an arithmetic Gauss problem for genera of positive-definite quaternion Hermitian lattices, which we also solve.

I will talk about homological enumerative invariants for vector bundles on algebraic curves. These invariants were defined by Joyce, and encode rich information about the moduli space of semistable vector bundles, such as its volume and intersection numbers, which were computed by Witten, Jeffrey and Kirwan in previous work. I will define a notion of regularization of divergent infinite sums, and I will express the invariants explicitly as such a divergent sum in a vertex algebra.

While there have been substantial successes of actor-critic methods in continuous control, simpler critic-only methods such as Q-learning often remain intractable in the associated high-dimensional action spaces. However, most actor-critic methods come at the cost of added complexity: heuristics for stabilisation, compute requirements as well as wider hyperparameter search spaces. To address this limitation, we demonstrate in two stages how a simple variant of Deep Q Learning matches state-of-the-art continuous actor-critic methods when learning from simpler features or even directly from raw pixels. First, we take inspiration from control theory and shift from continuous control with policy distributions whose support covers the entire action space to pure bang-bang control via Bernoulli distributions. And second, we combine this approach with naive value decomposition, framing single-agent control as cooperative multi-agent reinforcement learning (MARL). We finally add illustrative examples from control theory as well as classical bandit examples from cooperative MARL to provide intuition for 1) when action extrema are sufficient and 2) how decoupled value functions leverage state information to coordinate joint optimization.

Argyres—Douglas (AD) theories are 4d N=2 SCFTs which have some unusual features, and until recently, explicit holographic duals of these theories were unknown. We will consider a concrete class of these theories obtained by wrapping the 6d N=(2,0) ADE theories on a (twice) punctured sphere: one irregular and one regular puncture, and construct their holographic duals. The novel aspects of these solutions require a relaxation of the regularity conditions of the usual Gaiotto—Maldacena framework and to allow for brane singularities. We show how to construct the dictionary between the AdS(5) solutions and the field theory and match observables between the two. If time allows, I will comment on some on-going work about further compactifying the AD theories on spindles, or the 6d theories on four-dimensional orbifolds. 

What should we be thinking about when we're making a diagram for a paper? How do we help it to express the right things? Or make it engaging? What kind of colour palette is appropriate? What software should we use? And how do we make this process as painless as possible? Join Joshua Bull and Christoph Dorn for a lively Fridays@4 session on illustrating mathematics, as they share tips, tricks, and their own personal experiences in bringing mathematics to life via illustrations.

Models of animal brains are increasingly common and mapped in increasing detail. To simplify analysis of their function, we consider subregions and show that they perform well as classifiers of overall activity, with only a fraction of the neurons. The uniqueness of such ''reliable'' regions seems to be related to the types of connections that pairs of neurons form in them. By focusing on topologically significant structures and reciprocally connected neurons we find even stronger classification results. This is ongoing work across several institutions, including EPFL, the Blue Brain Project, and the University of Aberdeen.

Isostasy is one of the earliest quantitative geophysical theories still in current use. It explains why observed gravity anomalies are generally much weaker than what is inferred from visible topography, and why planetary crusts can support large mountains without breaking up. At large scale, most topography (including bathymetry) is in isostatic equilibrium, meaning that surface loads are buoyantly supported by crustal thickness variations or density variations within the crust and lithosphere, in such a way that deeper layers are hydrostatic. On Earth, examples of isostasy are the average depth of the oceans, the elevation of the Himalayas, and the subsidence of ocean floor away from mid-ocean ridges, which are respectively attributed to the crust-ocean thickness difference, to crustal thickening under mountain belts, and to the density increase due to plate cooling. Outside Earth, isostasy is useful to constrain the crustal thickness of terrestrial planets and the shell thickness of icy moons with subsurface oceans.

Given the apparent simplicity of the isostatic concept – buoyant support of mountains by iceberg-like roots – it is surprising that a debate has been going on for over a century about its various implementations. Classical isostasy is indeed not self-consistent, neglects internal stresses and geoid contributions to topographical support, and yields ambiguous predictions of geoid anomalies at the planetary scale. In the last few years, these problems have attracted renewed attention when applying isostasy to planetary bodies with an unbroken crustal shell. In this talk I will discuss isostatic models based on the minimization of stress, on time-dependent viscous evolution, and on stationary viscous flow. I will show that these new isostatic approaches are mostly equivalent and discuss their implications for the structure of icy moons.

As one of the largest retailers in the world, Tesco relies on automated forecasting to help with decision making. A common issue with forecasts is that of the cold start problem; that we must make forecasts for new products that have no history to learn from. Lack of historical data becomes a real problem as it prevents us from knowing how products react to events, and if their sales react to the time of year. We might consider using similar products as a way to produce a starting forecast, but how should we define what ‘similar’ means, and how should we evolve this model as we start getting real live data? We’ll present some examples to hopefully start a fruitful discussion.

European option payoffs can be generated by combinations of hockeystick payoffs or of monomials. Interestingly, path dependent options can be generated by combinations of signatures, which are the building blocks of path dependence. We focus on the case of 1 asset together with time, typically the evolution of the price x as a function of the time t. The signature of a path for a given word with letters in the alphabet {t,x} (sometimes called augmented signature of dimension 1) is an iterated Stratonovich integral with respect to the letters of the word and it plays the role of a monomial in a Taylor expansion. For a given time horizon T the signature elements associated to short words are contained in the linear space generated by the signature elements associated to longer words and we construct an incremental basis of signature elements. It allows writing a smooth path dependent payoff as a converging series of signature elements, a result stronger than the density property of signature elements from the Stone-Weierstrass theorem. We recall the main concepts of the Functional Itô Calculus, a natural framework to model path dependence and draw links between two approximation results, the Taylor expansion and the Wiener chaos decomposition. The Taylor expansion is obtained by iterating the functional Stratonovich formula whilst the Wiener chaos decomposition is obtained by iterating the functional Itô formula applied to a conditional expectation. We also establish the pathwise Intrinsic Expansion and link it to the Functional Taylor Expansion.

It is known that the Brauer group of a smooth, projective surface
defined by an equality of two homogeneous polynomials in characteristic 0, is
finite up to constants. I will report on new methods to determine these Brauer
groups, or at least their algebraic parts, as long as the coefficients are in a
certain sense generic. This generalises previous results obtained over the
years by Colliot-Thélène--Kanevsky--Sansuc, Bright, Uematsu and Santens.
(Joint work with A. N. Skorobogatov.)

Automated Market Makers (AMMs) are a new prototype of 
trading venues which are revolutionising the way market participants 
interact. At present, the majority of AMMs are Constant Function 
Market Makers (CFMMs) where a deterministic trading function 
determines how markets are cleared. A distinctive characteristic of 
CFMMs is that execution costs for liquidity takers, and revenue for 
liquidity providers, are given by closed-form functions of price, 
liquidity, and transaction size. This gives rise to a new class of 
trading problems. We focus on Constant Product Market Makers with 
Concentrated Liquidity and show how to optimally take and make 
liquidity. We use Uniswap v3 data to study price and liquidity 
dynamics and to motivate the models.

For liquidity taking, we describe how to optimally trade a large 
position in an asset and how to execute statistical arbitrages based 
on market signals. For liquidity provision, we show how the wealth 
decomposes into a fee and an asset component. Finally, we perform 
consecutive runs of in-sample estimation of model parameters and 
out-of-sample trading to showcase the performance of the strategies.

I will explain how the model-theoretic algebraic closure in Zilber’s pseudo-exponential field can be described in terms of the self-sufficient closure. I will sketch a proof and show how the Mordell-Lang conjecture for algebraic tori comes into play. If time permits, I’ll also talk about the characterisation of strongly minimal sets and their geometries. This is joint work (still in progress) with Jonathan Kirby.

Solving sparse linear systems is omnipresent in scientific computing. Direct approaches based on matrix factorization are very robust, and since they can be used as a black-box, it is easy for other software to use them. However, the memory requirement of direct approaches scales poorly with the problem size, and the algorithms underpinning sparse direct solvers software are poorly suited to parallel computation. Multilevel Domain decomposition (MDD) methods are among the most efficient iterative methods for solving sparse linear systems. One of the main technical difficulties in using efficient MDD methods (and most other efficient preconditioners) is that they require information from the underlying problem which prohibits them from being used as a black-box. This was the motivation to develop the widely used algebraic multigrid for example. I will present a series of recently developed robust and fully algebraic MDD methods, i.e., that can be constructed given only the coefficient matrix and guarantee a priori prescribed convergence rate. The series consists of preconditioners for sparse least-squares problems, sparse SPD matrices, general sparse matrices, and saddle-point systems. Numerical experiments illustrate the effectiveness, wide applicability, scalability of the proposed preconditioners. A comparison of each one against state-of-the-art preconditioners is also presented.

The classification of anti de Sitter black holes is an open problem of central importance in holography. In this talk, I will present new advances in classification of supersymmetric solutions to five-dimensional minimal gauged supergravity. In particular, we prove a black hole uniqueness theorem within a ‘Calabi-type’ subclass of solutions with biaxial symmetry. This subclass includes all currently known black hole solutions within this theory.

The applied, numerical and asymptotic analysis of acoustic, electromagnetic and elastic scattering by smooth scatterers (e.g. a cylinder or a sphere) is a classical topic in applied mathematics. However, many real-world applications involve highly non-smooth scatterers with geometric structure on multiple length scales. Examples include acoustic scattering by trees and other vegetation in the modelling of urban noise propagation, electromagnetic scattering by snowflakes and ice crystal aggregates in climate modelling and weather prediction, and elastic scattering by cracks and other interfaces in seismic imaging and hydrocarbon exploration. In such situations it may be more appropriate to model the scatterer not by a smooth surface but by a fractal, a geometric object with self-similarity properties and detail on every length scale. Well-known examples include the Cantor set, Sierpinski triangle and the Koch snowflake. In this talk I will give an overview of our recent research into acoustic scattering by such fractal structures. So far our work has focussed on establishing well-posedness of the scattering problem and integral equation reformulations of it, and developing and analysing numerical methods for obtaining approximate solutions. However, there remain interesting open questions about the high frequency (short wavelength) asymptotic behaviour of solutions, and whether the self-similarity of the scatterer can be exploited to derive more efficient approximation techniques.

Separability is an algebraic property enjoyed by certain subsets of groups. In the world of non-positively curved groups, it has a not-too-well-understood link to geometric properties such as convexity. We explore this connection in the setting of relatively hyperbolic groups and discuss a recent joint work in this area involving products of quasiconvex subgroups.

In this talk, I will present a noncommutative analogue of Margulis’ factor theorem for higher rank lattices. More precisely, I will give a complete description of all intermediate von Neumann subalgebras sitting between the von Neumann algebra of the lattice and the von Neumann algebra of the action of the lattice on the Furstenberg-Poisson boundary. As an application, we infer that the rank of the semisimple Lie group is an invariant of the pair of von Neumann algebras. I will explain the relevance of this result regarding Connes’ rigidity conjecture.

I will present recent results on the growth of entanglement between two adjacent regions in a tripartite, one-dimensional many-body system after a quantum quench. Combining a replica trick with a space-time duality transformation a universal relation between the entanglement negativity and Renyi-1/2 mutual information can be derived, which holds at times shorter than the sizes of all subsystems. The proof is directly applicable to any local quantum circuit, i.e., any lattice system in discrete time characterised by local interactions, irrespective of the nature of its dynamics. The derivation indicates that such a relation can be directly extended to any system where information spreads with a finite maximal velocity. The talk is based on Phys. Rev. Lett. 129, 140503 (2022).

Various graphs associated to surfaces have proved to be important tools for studying the large scale geometry of mapping class groups of surfaces, among other applications. A seminal paper of Masur and Minsky proved that perhaps the most well known example, the curve graph, is Gromov hyperbolic. However, this is not the case for every naturally defined graph associated to a surface. We will present joint work with Jacob Russell classifying a wide family of graphs associated to surfaces according to whether the graph is Gromov hyperbolic, relatively hyperbolic or not relatively hyperbolic.
 

Many networks exhibit some community structure. There exists a wide variety of approaches to detect communities in networks, each offering different interpretations and associated algorithms. For large networks, there is the additional requirement of speed. In this context, the so-called label propagation algorithm (LPA) was proposed, which runs in near linear time. In partitions uncovered by LPA, each node is ensured to have most links to its assigned community. We here propose a fast variant of LPA (FLPA) that is based on processing a queue of nodes whose neighbourhood recently changed. We test FLPA exhaustively on benchmark networks and empirical networks, finding that it runs up to 700 times faster than LPA. In partitions found by FLPA, we prove that each node is again guaranteed to have most links to its assigned community. Our results show that FLPA is generally preferable to LPA.

A random $d$-regular graph is just a $d$-regular simple graph on $[n]=\{1,2,\ldots,n\}$ chosen uniformly at random from all such graphs. This model, with $d=d(n)$, is one of the most natural random graph models, but is quite tricky to work with/reason about, since actually generating such a graph is not so easy. For $d$ constant, Bollobás's configuration model works well; for larger $d$ one can combine this with switching arguments pioneered by McKay and Wormald. I will discuss recent progress with Nick Wormald, pushing linear-time generation up to $d=o(\sqrt{n})$. One ingredient is reciprocal rejection sampling, a trick for 'accepting' a certain graph with a probability proportional to $1/N(G)$, where $N(G)$ is the number of certain configurations in $G$. The trick allows us to do this without calculating $N(G)$, which would take too long.

HiGHS is open-source optimization software for linear programming, mixed-integer programming, and quadratic programming. Created initially from research solvers written by Edinburgh PhD students, HiGHS attracted industrial funding that allowed further development, and saw it contribute to a REF 2021 Impact Case Study. Having been identified as a game-changer by the open-source energy systems planning community, the resulting crowdfunding campaign has received large donations that will allow the HiGHS project to expand and create further Impact.

This talk will give an insight into the state-of-the-art techniques underlying the linear programming solvers in HiGHS, with a particular focus on the challenge of solving sequences of linear systems of equations with remarkable properties. The means by which "gradware" created by PhD students has been transformed into software, generating income and Impact, will also be described. Independent benchmark results will be given to demonstrate that HiGHS is the world’s best open-source linear optimization software.

 

A finite W-algebra is a gadget associated to each nilpotent orbit in a complex semisimple Lie algebra g. There is a functor from W-modules to a full subcategory of g-modules, known as Skryabin’s equivalence, and every primitive ideals of the enveloping algebra U(g) as the annihilator of a module obtained in this way. This gives a convenient way of organising together primitive ideals in terms of nilpotent orbits, and this approach has led to a resurgence of interest in some hard open problems which lay dormant for some 20 years. The primitive ideals of U(g) which come from one-dimensional representations of W-algebras are especially nice, and we shall call them Losev—Premet ideals. The goal of this talk is to explain my recent work which seeks to: (1) describe the structure of the space of the dimensional representations of a finite W-algebra and (2) classify the Losev—Premet ideals.

Residual networks (ResNets) have displayed impressive results in pattern recognition and, recently, have garnered considerable theoretical interest due to a perceived link with neural ordinary differential equations (neural ODEs). This link relies on the convergence of network weights to a smooth function as the number of layers increases. We investigate the properties of weights trained by stochastic gradient descent and their scaling with network depth through detailed numerical experiments. We observe the existence of scaling regimes markedly different from those assumed in neural ODE literature. Depending on certain features of the network architecture, such as the smoothness of the activation function, one may obtain an alternative ODE limit, a stochastic differential equation (SDE) or neither of these. Furthermore, we are able to formally prove the linear convergence of gradient descent to a global optimum for the training of deep residual networks with constant layer width and smooth activation function. We further prove that if the trained weights, as a function of the layer index, admit a scaling limit as the depth increases, then the limit has finite 2-variation.

In this talk, I will first give a brief introduction to the Landau-Ginzburg -- Conformal Field Theory (LG-CFT) correspondence, a prediction from physics. This prediction links aspects of Landau-Ginzburg models, described by matrix factorisations for a polynomial known as the potential, with Conformal Field Theories, described by for example vertex operator algebras. While both sides of the correspondence have good mathematical descriptions, it is an open problem to give a mathematical formulation of the correspondence. 

After this introduction, I will discuss the only known realisation of this correspondence, for the potential $x^d$. For even $d$ this is a recent result, and I will give a sketch of the proof which uses the tools of module tensor categories

 I will not assume prior knowledge of matrix factorisations, CFTs, or module tensor categories. This talk is based on joint work with Ana Ros Camacho.

In this talk, I will present several results on the Anderson Hamiltonian with white noise potential in dimension 1. This operator formally writes « - Laplacian + white noise ». It arises as the scaling limit of various discrete models and its explicit potential allows for a detailed description of its spectrum. We will discuss localization of its eigenfunctions as well as the behavior of the local statistics of its eigenvalues. Around large energies, we will see that the eigenfunctions are localized and follow a universal shape given by the exponential of a Brownian motion plus a drift, a behavior already observed by Rifkind and Virag in tridiagonal matrix models. Based on joint works with Cyril Labbé.

Shrinking Ricci solitons are Ricci flow solutions that self-similarly shrink under the flow. Their significance comes from the fact that finite-time Ricci flow singularities are typically modeled on gradient shrinking Ricci solitons. Here, we shall address a certain converse question, namely, “Given a complete, noncompact gradient shrinking Ricci soliton, does there exist a Ricci flow on a closed manifold that forms a finite-time singularity modeled on the given soliton?” We’ll discuss work that shows the answer is yes when the soliton is asymptotically conical. No symmetry or Kahler assumption is required, and so the proof involves an analysis of the Ricci flow as a nonlinear degenerate parabolic PDE system in its full complexity. We’ll also discuss applications to the (non-)uniqueness of weak Ricci flows through singularities.

Numerical optimization is an indispensable tool of modern data analysis, and there are many optimization problems where it is difficult or impossible to compute the full gradient of the objective function. The field of derivative free optimization (DFO) addresses these cases by using only function evaluations, and has wide-ranging applications from hyper-parameter tuning in machine learning to PDE-constrained optimization.

We present two projects that attempt to scale DFO techniques to higher dimensions.  The first method converges slowly but works in very high dimensions, while the second method converges quickly but doesn't scale quite as well with dimension.  The first-method is a family of algorithms called "stochastic subspace descent" that uses a few directional derivatives at every step (i.e. projections of the gradient onto a random subspace). In special cases it is related to Spall's SPSA, Gaussian smoothing of Nesterov, and block-coordinate descent. We provide convergence analysis and discuss Johnson-Lindenstrauss style concentration.  The second method uses conventional interpolation-based trust region methods which require large ill-conditioned linear algebra operations.  We use randomized linear algebra techniques to ameliorate the issues and scale to larger dimensions; we also use a matrix-free approach that reduces memory issues.  These projects are in collaboration with David Kozak, Luis Tenorio, Alireza Doostan, Kevin Doherty and Katya Scheinberg.

I will describe a procedure, known as holomorphic twist, to isolate protected quantities in supersymmetric quantum field theories. The resulting theories are holomorphic, interacting and have infinite dimensional symmetries, analogous to the holomorphic half of a 2D CFT. I will explain how to study quantum corrections to these symmetries and other  higher operations.
As a surprise, we find a novel UV manifestation of
confinement, dubbed "holomorphic confinement," in the example of pure
SU(N) super Yang-Mills.

Ilia Smilga
Margulis spacetimes and crooked planes

We are interested in the following problem: which groups can act 
properly on R^n by affine transformations, or in other terms, can occur 
as a symmetry group of a "regular affine tiling"? If we additionally 
require that they preserve a Euclidean metric (i.e. act by affine 
isometries), then these groups are well-known: they all contain a 
finite-index abelian subgroup. If we remove this requirement, a 
surprising result due to Margulis is that the free group can act 
properly on R^3. I shall explain how to construct such an action.

Charles Parker
Unexpected Behavior in Finite Elements for Linear Elasticity
One of the first problems that finite elements were designed to approximate is the small deformations of a linear elastic body; i.e. the 2D/3D version of Hooke's law for springs from elementary physics. However, for nearly incompressible materials, such as rubber, certain finite elements seemingly lose their approximation power. After briefly reviewing the equations of linear elasticity and the basics of finite element methods, we will spend most of the time looking at a few examples that highlight this unexpected behavior. We conclude with a theoretical result that (mostly) explains these findings.

The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). In this talk, we present SINATRA: a machine learning pipeline for analyzing collections of shapes without requiring any correspondences. Our method takes in two classes of shapes and highlights the physical features that best describe the variation between them. We develop a rigorous simulation framework to assess our approach, which themselves are a novel contribution to 3D image and shape analyses. Lastly, as case studies with real data, we use SINATRA to (1) analyze mandibular molars from four different suborders of primates and (2) facilitate the visual identification of structural signatures differentiating between the trajectories of two protein ensembles resulting from molecular dynamics simulations. Together, these results highlight a promising future for interpretable machine learning to facilitate the non-trivial task of pattern recognition in evolutionary and structural biology with substantially increased resolution.

2:30 -3.00 Will Turner (CDT Student, Imperial College London)

Topologies on unparameterised path space

The signature of a path is a non-commutative exponential introduced by K.T. Chen in the 1950s, and appears as a central object in the theory of rough paths developed by T. Lyons in the 1990s. For continuous paths of bounded variation, the signature may be realised as a sequence of iterated integrals, which provides a succinct summary for multimodal, irregularly sampled, time-ordered data. The terms in the signature act as an analogue to monomials for finite dimensional data: linear functionals on the signature uniformly approximate any compactly supported continuous function on unparameterised path space (Levin, Lyons, Ni 2013). Selection of a suitable topology on the space of unparameterised paths is then key to the practical use of this approximation theory. We present new results on the properties of several candidate topologies for this space. If time permits, we will relate these results to two classical models: the fixed-time solution of a controlled differential equation, and the expected signature model of Levin, Lyons, and Ni. This is joint work with Thomas Cass.

3.05 -3.35 Ross Zhang (CDT Student, University of Oxford)

Random vortex dynamics via functional stochastic differential equations

The talk focuses on the representation of the three-dimensional (3D) Navier-Stokes equations by a random vortex system. This new system could give us new numerical schemes to efficiently approximate the 3D incompressible fluid flows by Monte Carlo simulations. Compared with the 2D Navier-Stokes equation, the difficulty of the 3D Navier-Stokes equation lies in the stretching of vorticity. To handle the stretching term, a system of stochastic differential equations is coupled with a functional ordinary differential equation in the 3D random vortex system. Two main tools are developed to derive the new system: the first is the investigation of pinned diffusion measure, which describes the conditional distribution of a time reversal diffusion, and the second is a forward-type Feynman Kac formula for nonlinear PDEs, which utilizes the pinned diffusion measure to delicately overcome the time reversal issue in PDE. Although the main focus of the research is the Navier-stokes equation, the tools developed in this research are quite general. They could be applied to other nonlinear PDEs as well, thereby providing respective numerical schemes.

3.40 - 4.25pm Dr Cris Salvi (Imperial College London)

Signature kernel methods

Kernel methods provide a rich and elegant framework for a variety of learning tasks including supervised learning, hypothesis testing, Bayesian inference, generative modelling and scientific computing. Sequentially ordered information often arrives in the form of complex streams taking values in non-trivial ambient spaces (e.g. a video is a sequence of images). In these situations, the design of appropriate kernels is a notably challenging task. In this talk, I will outline how rough path theory, a modern mathematical framework for describing complex evolving systems, allows to construct a family of characteristic kernels on pathspace known as signature kernels. I will then present how signature kernels can be used to develop a variety of algorithms such as two-sample hypothesis and (conditional) independence tests for stochastic processes, generative models for time series and numerical methods for path-dependent PDEs.

4.30 Refreshments

Building computationally capable biological systems has long been an aim of synthetic biology. The potential utility of bio-computing devices ranges from biosafety and environmental applications to diagnosis and personalised medicine. Here we present work on the design of bacterial computers which use spatial patterning to process information. A computer is composed of a number of bacterial colonies which, inspired by patterning in embryo development, communicate using diffusible morphogen-like signals. A computation is programmed into the overall physical arrangement of the system by arranging colonies such that the resulting diffusion field encodes the desired function, and the output is represented in the spatial pattern displayed by the colonies. We first mathematically demonstrate the simple digital logic capability of single bacterial colonies and show how additional structure is required to build complex functions. Secondly, inspired by electronic design automation, an algorithm for designing optimal spatial circuits computing two-level digital logic functions is presented, extending the capability of our system to complex digital functions without significantly increasing the biological complexity. We implement experimentally a proof-of-principle system using engineered Escherichia coli interpreting diffusion fields formed from droplets of an inducer molecule. Our approach will open up new ways to perform biological computation, with applications in synthetic biology, bioengineering and biosensing. Ultimately, these computational bacterial communities will help us explore information processing in natural biological systems.

The canonical dimension is a fundamental integer-valued invariant that is attached to mod p representations of p-adic Lie groups. I will explain why it is both an asymptotic measure of growth, and an algebraic quantity strongly related to Krull dimension. We will survey algebraic tools that can be applied in its calculation, and describe results spanning the last twenty years. I'll present a new theorem and suggest its possible significance for the mod p local Langlands programme.