Dear all,
We are very excited to recommend the annual SIAM conference to you. This is a conference for PhD students that are interested in Maths or are working on problems involving Maths. The conference focuses on interdisciplinary research. PhD students from all departments are welcome! As usual, food and drinks will be provided throughout the day. 
We are also excited to share with you abstracts from our three keynote speakers (see below)! We hope you can join us at the conference on the 11th of June, Detailed programme will appear soon.
See you there,

  • Georg Ostrovski (Google Deepmind):
    Curiosity-Based Exploration in Deep Reinforcement Learning 
    In recent years, the use of deep neural networks in Reinforcement Learning has allowed significant empirical progress, enabling generic learning algorithms with little domain-specific prior knowledge to solve a wide variety of previously challenging tasks. Examples are reinforcement learning agents that learn to play video games exceeding human-level performance, or beat the world’s strongest players at board games such as Go or Chess. Despite these practical successes, the problem of effective exploration in high-dimensional domains, recognized as one of the key ingredients for more competent and generally applicable AI, remains a great challenge and is an active area of empirical research. In this talk I will introduce basic ideas from Deep Learning and its use in Reinforcement Learning and show some of their applications. I will then zoom in on the exploration problem, and present some of the recent algorithmic approaches to create ‘curious’ reinforcement learning agents.
  • Martins Bruveris (Brunel University London):
    Riemannian Metrics on Shape Spaces - Theory and Applications
    The word 'shape' denotes the external form, contour or outline of something. Shape is a basic physical property of objects and plays an important role in applications such as evolutionary biology or automated image understanding. Mathematically one can define a shape to be an embedded submanifold in an ambient space. The shape space is the collection of all shapes. To perform statistical inferences it is necessary to equip the shape space with additional structure, for example a distance function or a Riemannian metric. In this talk I will describe a class of Riemannian metrics that are used in shape analysis. By doing so we will see how functional analysis and differential geometry interact in infinite dimensions and that the infinite-dimensional landscape can be full of traps for the careless traveller.
  • David Ham (Imperial College London):
    Firedrake: using symbolic computation to overcome the productivity crisis in numerical computing
    Simulation based on numerically solving PDEs is a core challenge in computational mathematics. This is the only field in which entire top 50 supercomputers are procured for a single class of problem. However, the productivity of simulation software creation is spectacularly poor, with the combination of application, equations, numerics, and various levels of performance engineering creating a code so complex that advances become prohibitive. PhD students frequently spend the majority of their research time re-implementing known techniques in huge legacy programmes just to reach the starting point for their research contribution. The Firedrake project tackles this productivity crisis by taking a radically different approach to simulation software creation. Scientists and engineers create simulations by writing the finite element form of the PDE in high level mathematical code, similar to Mathematica or Maple. Firedrake then uses specialised compilers to apply a sequence of mathematical transformations to this code, each one applying some part of the discretisation and performance optimisation pipeline. The result is a system which is both highly productive and which facilitates the application of optimisations which would be intractable to apply by hand. In this talk I will lay out how the Firedrake system works, and show some of its mathematical capabilities.