Mathematical Insitute, Oxford

Topological data analysis is a growing area of research where topology and geometry meets data analysis. Many data science problems have a geometric flavor, and thus computational tools like persistent homology and Mapper were often found to be useful. Domains of applications include cosmology, material science, diabetes and cancer research. We will discuss some main tools of the field and some prominent applications.

Diffusion processes are widely used to model the evolution of random values over time. In many applications, the diffusion process is constrained to a finite domain. We consider the estimation problem of a diffusion process constrained by a polytope, i.e. intersection of finitely many (hyper-)planes, given a discretely observed time series data. Since the boundary behaviours of a diffusion process are characterised by its drift and diffusion functions, we derive sufficient conditions on the drift and diffusion functions for the nonattainablity of a polytope. We use deep learning to estimate the drift and diffusion, and ensure that their constraints are satisfied throughout the training.

Signalling pathways can be modelled as a biochemical reaction network. When the kinetics are to follow mass-action kinetics, the resulting
mathematical model is a polynomial dynamical system. I will overview approaches to analyse these models with steady-state data using
computational algebraic geometry and statistics. Then I will present how to analyse such models with time-course data using differential
algebra and geometry for model identifiability. Finally, I will present how topological data analysis can be help distinguish models
and data.

Lisa Lamberti

Geometric models in algebra and beyond

Many phenomena in mathematics and related sciences are described by geometrical models.

In this talk, we will see how triangulations in polytopes can be used to uncover combinatorial structures in algebras. We will also glimpse at possible generalizations and open questions, as well as some applications of geometric models in other disciplines.

Jaroslav Fowkes

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Optimization Challenges in the Commercial Aviation Sector

The commercial aviation sector is a low-margin business with high fixed costs, namely operating the aircraft themselves. It is therefore of great importance for an airline to maximize passenger capacity on its route network. The majority of existing full-service airlines use largely outdated capacity allocation models based on customer segmentation and fixed, pre-determined price levels. Low-cost airlines, on the other hand, mostly fly single-leg routes and have been using dynamic pricing models to control demand by setting prices in real-time. In this talk, I will review our recent research on dynamic pricing models for the Emirates route network which, unlike that of most low-cost airlines, has multiple routes traversing (and therefore competing for) the same leg.

Wei Title: Adaptive timestep Methods for non-globally Lipschitz SDEs

Wei Abstract: Explicit Euler and Milstein methods are two common ways to simulate the numerical solutions of
SDEs for its computability and implementability, but they require global Lipschitz continuity on both
drift and diffusion coefficients. By assuming the boundedness of the p-th moments of exact solution
and numerical solution, strong convergence of the Euler-type schemes for locally Lipschitz drift has been
proved in [HMS02], including the implicit Euler method and the semi-implicit Euler method. However,
except for some special cases, implicit-type Euler method requires additional computational cost, which
is very inefficient in practice. Explicit Euler method then is shown to be divergent in [HJK11] for non-
Lipschitz drift. Explicit tamed Euler method proposed in [HJK + 12], shows the strong convergence for the
one-sided Lipschitz condition with at most polynomial growth and it is also extended to tamed Milstein
method in [WG13]. In this paper, we propose a new adaptive timestep Euler method, which shows the
strong convergence under locally Lipschitz drift and gains the standard convergence order under one-sided
Lipschitz condition with at most polynomial growth. Numerical experiments also demonstrate a better
performance of our scheme, especially for large initial value and high dimensions, by comparing the mean
square error with respect to the runtime. In addition, we extend this adaptive scheme to Milstein method
and get a higher order strong convergence with commutative noise.

Alexander Title: Functionally-generated portfolios and optimal transport

Alexander Abstract: I will showcase some ongoing research, in which I try to make links between the class of functionally-generated portfolios from Stochastic Portfolio Theory, and certain optimal transport problems.

This talk will discuss work-in-progress on the numerical approximation
of reflected diffusions arising from applications in engineering, finance
and network queueing models.  Standard numerical treatments with
uniform timesteps lead to 1/2 order strong convergence, and hence
sub-optimal behaviour when using multilevel Monte Carlo (MLMC).

In simple applications, the MLMC variance can be improved by through
a reflection "trick".  In more general multi-dimensional applications with
oblique reflections an alternative method uses adaptive timesteps, with
smaller timesteps when near the boundary.  In both cases, numerical
results indicate that we obtain the optimal MLMC complexity.

This is based on joint research with Eike Muller, Rob Scheichl and Tony
Shardlow (Bath) and Kavita Ramanan (Brown).

We examine the Foreign Exchange (FX) spot price spreads with and without Last Look on the transaction. We assume that brokers are risk-neutral and they quote spreads so that losses to latency arbitrageurs (LAs) are recovered from other traders in the FX market. These losses are reduced if the broker can reject, ex-post, loss-making trades by enforcing the Last Look option which is a feature of some trading venues in FX markets. For a given rejection threshold the risk-neutral broker quotes a spread to the market so that her expected profits are zero. When there is only one venue, we find that the Last Look option reduces quoted spreads. If there are two venues we show that the market reaches an equilibrium where traders have no incentive to migrate. The equilibrium can be reached with both venues coexisting, or with only one venue surviving. Moreover, when one venue enforces Last Look and the other one does not, counterintuitively, it may be the case that the Last Look venue quotes larger spreads.


a working version of the paper may be found here

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2630662

Finding rational points on curves - Jennifer Balakrishnan (Mathematical Institute, Oxford)

From cryptography to the proof of Fermat's Last Theorem, elliptic curves are ubiquitous in modern number theory.  Much activity is focused on developing methods to discover their rational points (those points with rational coordinates).  It turns out that finding a rational point on an elliptic curve is very much like finding the proverbial needle in the haystack.  In fact, there is no algorithm known to determine the group of rational points on an elliptic curve.

Hyperelliptic curves are also of broad interest; when these curves are defined over the rational numbers, they are known to have finitely many rational points.  Nevertheless, the question remains: how do we find these rational points?

I'll summarize some of the interesting number theory behind these curves and briefly describe a technique for finding rational points on curves using (p-adic) numerical linear algebra.

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Analysis, prediction and control of technological progress - François Lafond (London Institute for Mathematical Sciences, Institute for New Economic Thinking at the Oxford Martin School, United Nations University - MERIT)

Technological evolution is one of the main drivers of social and economic change, with transformative effects on most aspects of human life.  How do technologies evolve?  How can we predict and influence technological progress?  To answer these questions, we looked at the historical records of the performance of multiple technologies.  We first evaluate simple predictions based on a generalised version of Moore's law, which assumes that technologies have a unit cost decreasing exponentially, but at a technology-specific rate.  We then look at a more explanatory theory which posits that experience - typically in the form of learning-by-doing - is the driver of technological progress.  These experience curves work relatively well in terms of forecasting, but in reality technological progress is a very complex process.  To clarify the role of different causal mechanisms, we also study military production during World War II, where it can be argued that demand and other factors were exogenous.  Finally, we analyse how to best allocate investment between competing technologies.  A decision maker faces a trade-off between specialisation and diversification which is influenced by technology characteristics, risk aversion, demand and the planning horizon.

This talk will be an introduction to the notion of D-modules on

prestacks. We will begin by discussing Grothendieck's definition of

crystals of quasi-coherent sheaves on a smooth scheme X, and briefly

indicate how the category of such objects is equivalent to that of

modules over the sheaf of differential operators on X. We will then

explain what we mean by a prestack and define the category of

quasi-coherent sheaves on them. Finally, we consider how the

crystalline approach may be used to give a suitable generalization

of D-modules to this derived setting.