Adtones intelligently tailors mobile ads to users in developing countries and shares the revenue with them to subsidize their phone bills.

Adtones

Boston Consulting Group is a management consulting firm that advises businesses and organizations on strategy, transformation and growth.

Boston  Consulting Group

The BCG Henderson Institute applies insights from academic disciplines to the strategic challenges facing business, government and society.

BCG Henderson Institute

Bit Bio is a human synthetic biology enterprise, which reprograms human cells for use in research, drug discovery and cell therapy.

BitBio

Cancer Research UK supports scientific and medical research to help beat cancer sooner. It is the world's largest cancer research charity.

Cancer Research UK

The Centre for Defence Enterprise funds research that could lead to a capability advantage for the armed forces and national security.

Centre for  Defence Enterprise

The U.S. Defence Advanced Research Projects Agency makes pivotal investments in breakthrough technologies that benefit national security.

Defence Advanced  Research Projects Agency

The U.S. Defence Threat Reduction Agency funds research to prepare for and combat weapons of mass destruction and improvised threats. 

Defence Threat  Reduction Agency

The European Innovation Council funds world-class scientists and innovators to develop and accelerate disruptive technology businesses.

European  Innovation Council

Framework Programme 7 was created by the European Commission to fund scientific research and innovation in Europe and associated countries.

Framework Programme 7

The Garfield Weston Foundation supports a range of charities across the UK and the restoration and redevelopment of their buildings.

Garfield Weston  Foundation

Horizon 2020 was created by the European Commission to fund scientific research and innovation across Europe and other member countries.

Horizon 2020

Invenia is a company of scientists and engineers that uses machine learning to optimize complex decision making and reduce inefficiencies.

Invenia Labs

LIMS Ventures is the start-up incubator of the London Institute which accelerates projects with potential for widespread commercialization.

LIMS Ventures

The Medical Research Council, one of the seven UK Research Councils, supports basic and applied medical research throughout the UK.

Medical Research Council

The U.S. Office of Naval Research supports basic research and technology for future naval power and the preservation of national security.

Office of  Naval Research

The Pilgrim Trust supports the preservation of architecturally important buildings and historically significant artifacts and documents.

Pilgrim Trust

The Porter Foundation supports projects in education, the environment, culture, health and welfare that encourage excellence and innovation.

Porter Foundation

The Rose Foundation provides financial assistance to charities undertaking building restoration and development in and around London.

Rose Foundation

The Edward Harvist Trust supports organisations that improve the quality of life for local people in Westminster and other London boroughs.

Edward Harvist Trust

The European Commission Migration and Home Affairs supports research to ensure the security of the EU and cooperation with non-EU countries.

European Commission  Migration and Home Affairs

The London Institute for Mathematical Sciences

We’re bringing mathematicians and biologists together to discuss novel techniques for modelling cell biology on Wednesday, 16th September.

Modelling cell biology

Parallels between the perfect and abundant numbers and their recursive analogs point to deeper structure in the recursive divisor function.

Ample & pristine numbers

Physicists and biologists discuss theoretical models of cell programming and reprogramming, shaped by experimental innovations at Bit Bio.

Reprogramming the cell

Welcome to our new postdoc Ryan Hannam. He’ll be developing theoretical insights into cell programming in our collaboration with BitBio.

Welcome onboard

Welcome to our new science writer Pippa Cole. She’ll be making our cutting-edge research accessible to scientists and non-scientists alike.

Most real-world networks are either a snapshot of a graph that evolves over time, or one instance of a collection of related graphs. Some aspects of the graph are random while others—such as the connectivity or density of cycles—are fixed. The trick is to figure out which is which. By treating a graph as a random variable, it is possible to deduce the constraints by comparing the behaviour they give rise to with real data. This is hard, and nearly all random graphs that can be solved analytically are locally tree-like. But empirically we know that realistic graphs have short loops.
In this project we develop sophisticated mathematical methods for deducing the behaviour of graphs and processes on them. By treating topological patterns as constraints on a random graph ensemble, we go beyond the replica trick and cavity method to handle graphs with a finite fraction of short loops. We start with triangles, the “harmonic oscillator” of loopy random graphs, and proceed to larger loops, along the way introducing the imaginary replica technique.
The theory of tailored graph ensembles offers a deeper understanding of graph structure. It enables us to predict the null behaviour of neural and genetic regulatory networks and to detect network communities. It also offers an indirect approach to longstanding problems on lattices, like the 3D Ising model.

Predicting the behaviour of graphs and processes on them by treating topological patterns as constraints on a random graph ensemble.

Artificial intelligence of graphs

Networks represent interactions between multiple elements. Widely accessible and large data sets makes it easy to build networks, but extracting meaning from them remains a challenge. Part of the problem is that mathematical methods for characterizing networks are primitive and sundry. While the theory of graphs—idealized networks containing internal symmetry—is more advanced, the mathematical foundations for both networks and graphs, and their relation, are underdeveloped.
In this project we investigate new mathematical methods to describe the topology and geometry of networks and graphs, without regard to the kinds of data from which they are derived. The broad range of techniques under development include applications and extensions of spectral graph theory, tailored graph ensembles, replica methods, community detection and enumerative combinatorics.
A deeper understanding of network and graph structure underpins many other research endeavours. Examples range from biomedical data for personalized medicine to predicting behaviour from social interaction networks to the stability of interconnected financial institutions.

Creating mathematical tools for characterizing the structure of ideal graphs and irregular networks, and the behaviour of processes on them.

The structure of how things relate

Memristors are resistors that preserve memory. They can be used to regulate and limit the amount of current that flows through a circuit, as they are able to change the value of their resistance based on their memory of the current and the voltage in the network. 
This project investigates how an analytical description of simple memristive circuits can be used to understand the properties of more complicated networks of memristors. Our overall approach uses idealised models where the dynamics can be solved exactly to uncover emergent behaviour. For example, we explore the relationship between the topology of the circuit and the dynamics of memristors using techniques from graph theory. Using mean field theory, we identify the asymptotic behaviour of memristor dynamics beyond the linear regime. We verify our analytic results with numerical solutions, demonstrating the ability of simple models to explain the collective behaviour of complex networks. 
The idea that memristors have internal memory means that it is possible for a network of them to learn. This ability can be harnessed to implement models of neural systems, and hence study and mimic the brain. It also makes memristors a potential alternative paradigm for solid-state memory.

Understanding the dynamics of networks of memristors, a new paradigm for low-power computation inspired by the structure of the brain.

Remembering to learn

Social media is eradicating physical separation as a barrier to communication. The result is a world that is highly virtually connected. There are direct paths between producers and consumers of content, as well as ways to monitor an audience’s responses and reactions.
In this project, we investigate how the collective behaviour of social media users can be understood based on their data footprints. We study how users interact with news and fake news, and the effect of interventions to debunk the latter. Using data from a vast set of Facebook users, we identify distinct groups who follow either scientific or conspiracy-based content. Our analysis reveals how each polarised group responds to material aiming to discredit false rumours. We use Twitter data to attribute users’ online engagement with a political party to either support or opposition. Trends in long-term data can predict outcomes of political elections, whereas short-term data is unreliable.
The real-time flow of social media data offers a unique opportunity to understand a population’s sentiment towards ideas. Predicting their reaction to content can inform political and commercial decisions. It can also help curb false belief based on nonfactual sources and its propagation.

Investigating the adverse effects of information asymmetry and deliberate errors in social media and the press, and attempts to remedy them.

News and fake news in a connected world

Slurries are fluids full of solid particles, like lava and ice cream. Their solid forms are made up of randomly oriented crystalline grains. Determining such materials’ 3D structures is tricky, requiring costly and tedious imaging techniques. One approach is x-ray tomography, where the structure is deduced by merging multiple 2D x-ray images taken at different angles. Another is an iterative imaging process, where a thin layer at the surface of a sample is repeatedly sliced off, and an image is acquired after each cut. 
In this project we develop a mathematical formalism for capturing the 3D structure of a material’s grains in a single shot. It allows for the quick measurement of the size and shape distribution of grains using just one 2D image and electron diffraction data. We verify our method on simulated materials consisting of randomly oriented ellipsoids and blocks, and test it on minerals. 
Our approach is applicable to any isotropic polycrystalline material, and will help geologists and materials scientists determine 3D structures at a fraction of the cost of current methods. It could shed light on fundamental processes of rock formation, help optimize oil extraction, and better connect percolation theory with geological percolation.

Reconstructing the 3d shape distribution of grains or other objects randomly packed together with access only to 2d slices  through them.

A private institutefor curiosity-drivenresearch

Deducing three dimensions from two

Events

Modelling cell biology

Papers

Ample & pristine numbers

Events

Reprogramming the cell

News

Welcome onboard

Where we publish

Who's funding us