Recent News
New H2020 Green AI for 5G Grant

Investigating how to reduce energy consumption in neural networks that perform reinforcement optimisation in wireless networks.

Graph Sampling of Water Networks

IEEE Transactions on Network Science & Engineering paper on designing Graph Fourier Transforms to optimally joint sample network topology and water flow dynamics in water distribution networks. Funded by LRF via Alan Turing Institute.

Stability of load balancing networks

Uncertainty quantification for stability as a function of local generalised dynamics and any global topology - to appear in IEEE Systems Journal. Funded by EPSRC.

Crowd Classification of Structures

Royal Society Open Science paper on designing neural networks to classify bridge load capacities from crowd sourced images in disaster and conflict zones to supplement human expertise.

Point Cluster Process of Tweets

5 papers accepted to flagship IEEE Smart City Conference. Funded by EPSRC.

Global structural of air transport

Tracking the evolving multi-scale structure of air transport and how it maps to socio-economic factors enables us to predict the future of aircraft design - Springer Nature Applied Sciences (SNAS) paper. Funded by Alan Turing Institute, working with Airbus.

Social networks in conflict zones

Our paper: "Conflict Detection in Linguistically Diverse Online Social Networks: a Russia-Ukraine Case Study" is accepted to ACM MEDES. Funded by DSTL.

3 Papers on Neural Network for NLP

3 papers published in IEEE Access, using neural networks to identify human language is essential for understanding end users. Funded by H2020 and InnovateUK.

Predicting Urban Terrorism

Royal Society Open Science Paper on urban terrorism prediction using statistical trends. Funded by Alan Turing Institute.

Dynamic Network Model of Gangs

Longitudinal study of gang dynamics in Colombia based on historical reports accepted to PLOS ONE. Funded by DSTL & Alan Turing Institute.

Ancient Dynamics Paper to Appear

Our "Simulating Imperial Dynamics and Conflict in the Ancient World" paper has been accepted too Cliodynamics. Funded by Alan Turing Institute.

Nature: AI for Conflict Prevention

Nature paper with Sir Alan Wilson and Regius Professor Kristian Gleditsch makes a commentary on the benefits and limitations of AI in conflict prediction. Funded by Alan Turing Institute.

Molecular Comms for Nano Application

7 papers accepted to: ACM NanoCom, IEEE ICCC, and IEEE Trans. MBMC, and IEEE Trans Signal Processing. Funded by USAF, DSTL, and H2020.

IEEE ICC 2020 - Chair: Big Data

Oct 19: Chairing the technical track Big Data for the IEEE flagship ICC.

Media Coverage

BBC Futures: How AI could unlock world peace

Predicting where and when conflicts will escalate into war or where food shortages might lead to famine could help peacekeepers and aid agencies intervene before they turn into humanitarian disasters.

National Infrastructure Commission: Data for the Public Good

The recommendation is made that the government should encourage the uptake of new data-driven solutions to the asset management of critical infrastructures. The development of a national ‘digital twin’ of UK infrastructure can help to bridge geographic and sectorial divides, provide a framework for determining sensor locations, and serve as a technology demonstrator for new tools.

The Economist: It’s the alcohol talking

Humans have long experimented with how best to communicate at a distance. Smoke signals and drums date back to prehistoric times. The Romans used carrier pigeons as messengers to support their conquests. Since the early 1830s, however, communication has been dominated by electrical or electromagnetic signals, from the first telegraph to the carrier waves in fibre-optic cables and the wireless networks of cellular telephones. But now a new contender is signalling its presence: molecular communication.

BBC News: Can mapping conflict data explain, predict and prevent violence?

Interview by Gordon Corera - BBC Security correspondent

Press Association: Quicker commuter trains could help reduce delays

Commuter trains that skip stations could be one way of improving the rail network, say researchers. The aim would be to break “feedback loops” in the system that have been identified as one of the main causes of delays and cancellations. Britain’s rail network transports more than 1.7 billion passengers each year, including 1.1 billion commuting in or around London. Last year, only 86.9% of passenger services arrived on time in the London area and 4.8% of journeys were cancelled or significantly late, according to the Office of Rail and Road.

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Graph Sampling of Water Networks

IEEE Transactions on Network Science & Engineering paper on designing Graph Fourier Transforms to optimally joint sample network topology and water flow dynamics in water distribution networks. Funded by LRF via Alan Turing Institute.

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