2019 - 2021  

 

Dr Harry Bostock

University of Sussex

RF and microwave trapped ion quantum sensor for counter-eavesdropping

Dr Bostock’s research focuses on the development of quantum sensing using trapped ions. This emerging technology offers a new and better way to sense radiofrequency and microwave radiation using trapped Ytterbium ions on two-dimensional micro-fabricated chip traps. Capable of tuning frequency with sub-Hz bandwidth, applications include drone detection, narcotic and explosive sensing.

Dr Bostock's website

 

 

Dr Edmund Hunt

University of Bristol 

Risk-sensitive robot swarms for effective environmental monitoring and anomaly investigation

A robot swarm could be used to deploy a dynamic network of sensors in a hazardous environment with a subset of robots that move closer to detected anomalies to inspect them in higher resolution. Dr Hunt aims to develop suitable, risk-sensitive swarm control algorithms by drawing on knowledge in behavioural biology and financial risk management.

 

 

 

Dr Luce le Gorrec

University of Strathclyde

Scalable partitioning of large complex networks

Dr le Gorrec’s research focuses on understanding and processing networks to extract information from complex systems. Complex networks have become a key tool in many fields. With systems growing exponentially, manual analysis becomes more difficult. Dr le Gorrec’s research develops tools to handle large-scale networks based on their structure.

 

 

 

Dr Ying Lia Li

University College London

Optomechanical sensors: rapid prototyping for navigation and quantum technologies

Dr Li develops chip-scale optomechanical sensors that improve navigation accuracy due to lower noise measurements of acceleration and rotation. For long-term GPS denial, these inertial sensors can replace current co-technologies used for providing dead-time measurements and stabilisation in quantum navigation systems. Rapid prototyping is achievable based on prior field-testing of an accelerometer prototype.

Dr Li's website

 

 

 

Dr Keng Tiong (Kelvin) Ng

King's College London

Identification of illegal threat manufacturing activity via wastewater markers (ThreatMARK)

Early identification of the illicit manufacture of threat agents is critical for protecting public safety. Dr Ng develops new methods to identify and monitor illegal activity by detecting synthesised compounds, their markers, precursors, by-products and transformation products in sewer systems.

 

 

Dr Cédric Spire

Brunel University London

Novel Ways for Automated Error Detection in Diverse Data Types

Training data and information about the distribution of system variables are not available for many real-world problems. Dr Spire’s research addresses information-sparse situations by advancing a novel Bayesian methodology that learns system parameters and state space probability distribution. Empirical illustrations are made by learning the density of dark matter in galaxies.

 

2018 - 2020  

 

Dr Jerone Theodore Andrews

University College London

Anomaly detection for the identification of tampered facial images 

Facial morphing is a specific, recent type of image tampering that tries to circumvent systems that match appearance between photographs to confirm identity. Dr Andrews’ research proposes an unsupervised deep learning approach to understand and identify the range and extent of normal variation so that irregularities can be detected, irrespective of an adversary’s method.

 

 

 

 

Dr Raoul Franky Guiazon

University of Leeds 

Artificial behaviour based authentication for the Internet of Things

Dr Guiazon develops methods of device authentication based on the detection of anomalous behaviour in networks of connected devices, commonly known as the IoT. His research aims to provide fast and effective authentication protocols that are robust to attacks and suitable for IoT devices with limited battery and available processing power.

 

 

 

 

Dr David John Harris

University of Exeter

Evaluating virtual reality training of cognitive skills for counter-terror policing

Virtual environments (VEs) have considerable potential for simulation training in highly complex or dangerous scenarios. Dr Harris aims to develop and validate a VE for counter-terror police training, and to establish its functional precision by assessing the effect of the VE on psychophysiological processes during training.

Dr David John Harris' website 

 

 

 

 

Dr Timothy Helps

University of Bristol

Insect-inspired strong and soft semi-autonomous agents for remote missions

Insects exhibit incredible capabilities in terms of locomotion, adaptability and task-specific performance. Small, soft, semi-autonomous robots with biologically-comparable performance could revolutionise a wide range of fields. Dr Help’s research combines insect characteristics and locomotion strategies with cutting-edge artificial muscle research to deliver high-performance insect-inspired agents for remote missions.

Dr Timothy Helps' website 

 

 

 


2017 - 2019

 

Dr David Haynes 

City, University of London

The Nature of Risk in the Privacy Calculus

Dr Haynes is investigating online user behaviour and interviewing risk experts. Combined, these bring a deeper understanding of the nature of risk and support the development of a robust typology of risk, providing a foundation for future studies of privacy and risk.

 

 

 

 

Dr James Robinson

University College London

Environmentally stable rechargeable batteries for flexible wearable electronics

Metal-air batteries are a promising power source for wearable electronics due to their non-toxic, electrode materials. However, there are concerns about the use of corrosive electrolytes that are difficult to contain in a flexible structure. Dr Robinson aims to overcome this by incorporating a flexible, polymer electrolyte removing the need for aqueous components.

 

 

 

 

Dr Jonathan Michael Silver
  • City, University of London

Quantum and Optical Sensors

Dr Silver’s research covers three different applications of counterpropagating light in an optical microresonator. These include nonlinear-enhanced gyroscopes, which could lead to cheap, chip-based devices that rival current optical gyroscopes in sensitivity. The other applications are near-field sensors for refractive index, nanoparticle and biomolecule detection, and dual frequency comb generation for ultrafast spectroscopy and precision distance ranging.

Dr Jonathan Michael Silver's website 

 

 

 

 

Dr Fabio Alessio Vittoria

University College London

Stored energy detection in complex environments 

X-ray imaging is a valuable tool for security screening, but current methods do not fully reveal threat materials. Dr Vittoria’s research combines different x-ray methods with machine learning algorithms to provide more detailed information on the physical and chemical properties of a sample.