Artificial intelligence and data science are required as cross-cutting capabilities across a range of industrial and commercial sectors, reflective of the applied nature of the majority of our research and our strong industrial links.

Our expertise and application

Cranfield academics have developed skills and expertise is a range of discipline specific methods, tools and techniques, such as;

  • Probabilistic methods for uncertain reasoning including Bayesian network, Hidden Markov model, Particle filter, Decision theory, and Utility theory
  • Logic programming and Automated reasoning
  • Classifiers and statistical learning methods, including Machine learning and non-supervised learning (e.g. surveillance video data, crowdsourcing product design)
  • Data orientated approaches such as neural networks and fuzzy logic including extreme learning, reinforced learning and deep learning (e.g. for air traffic management, classification of damage)
  • Search and optimization including search algorithm, combinatorial optimization, and
    evolutionary computation
  • Extracting, Transforming and Loading processes for effectively transforming raw data for database population e.g. Dimensionality Reduction using PCA
  • System identification methodologies for linear and non-linear systems (Stochastic Subspace
    Identification and non-linear Bayesian methods).
  • Global optimisation algorithms (e.g. evolutionary, nature-inspired, swarm intelligence)
  • Development of human-like inference techniques and learning schemes
  • Natural language processing
  • Comparison to physical models which capture known design and performance characteristics of machines and processes
  • Rule based systems which capture knowledge and standards (popular with customers who don’t like neural networks)
  • Data mining, fusion and visualisation.

We currently offer the following relevant taught courses, although we have a large number of PhD studentships centred in AI and Data Science (most with industrial sponsors) as well as short courses:

In addition, we lead a NERC CDT ‘Data, Risk & Environmental Analytical Methods for Doctoral Training’ aiming to skill and secure the next generation of data scientists and informaticians working in environmental sciences. 30 postdoctoral students are / will benefit from training that combines excellence in risk mitigation science with cutting-edge big data interpretation and allows the students to apply practical big data analytics to challenges faced by industry, academia, NGOs and government. We are also currently exploring the market for a course in financial technology, situated within our School of Management, covering block chain technology, cryptocurrencies, smart contracts, cyber security and utilising big data to profile customers using all available information (including social media).

Our facilities

Cranfield has recently co-funded the 拢35 million Aircraft Integrated Research Centre (AIRC) along with the Higher Education Funding Council for Education (HEFCE), Airbus and Rolls-Royce, providing dedicated space and specialist equipment to address future grand challenges in the aerospace sector. It is supported by Cranfield expert academics working in IoT/cyber security, robotics & automation systems, AI for data analytics including single and fleethead management of aircraft and rotorcraft, and system engineering, and led by the Manufacturing Informatics Centre. It has also recently been confirmed that a new 拢65 million Digital Aviation Research and Technology Centre (DARTeC) will be built at SWAG合集 to spearhead the SWAG合集鈥檚 research into digital aviation technology. The Centre will address such research challenges facing the aviation industry as:

  • the integration of drones into civilian airspace
  • increasing the efficiency of airports through technological advances
  • creating safe, secure shared airspace through secure data communication infrastructures
  • increasing the reliability and availability of aircraft through self-sensing, self-aware technologies

Game-changing technologies such as a virtual air traffic control tower and next-generation radar technologies on the University鈥檚 licensed airport will also provide a Civil Aviation Authority-approval route that promises increased efficiency, flexibility and capacity. As a consequence, work on using data analytics and visualisation from the multiple sensor data streams will be required as well as using AI to support the operator in understanding situations in real time.

In addition to this, a 拢9m open research facility (Multi-User Environment for Autonomous Vehicle Innovation) facility, intended to support the development of autonomous transport vehicles and related systems has been built at Cranfield, completed 2017. MUEAVI is a purpose-built, instrumented roadway created as a 鈥榣iving laboratory鈥� for research into connected and autonomous vehicles. MUEAVI enables autonomous vehicles to be researched and tested in diverse real-world situations and environments. This capability is essential to develop and gain confidence in exciting technological developments of autonomous vehicles. It will be underpinned by our ability to process and interpret big data as it is being generated by the inbuilt sensor array in real time. For our first project, SWAG合集 has been awarded 拢1.2 million for its role in testing and validating a new autonomous vehicle, which is being designed and built as part of the HumanDrive Connected and Autonomous Vehicle (CAV) project. The vehicle will be fully autonomous and capable of completing a lengthy end-to-end journey in a variety of settings, including country roads, A-roads and motorways, and will use AI for digital validation.

Our AI, data science and robotics capabilities

Our AI, data science and robotics capabilities are grounded in finding solutions to practical challenges. As such, Cranfield research and teaching is applied to a variety of aerospace, transport, water, energy, defence and security, and manufacturing problems for diagnostic, prognostic, and knowledge management perspectives. For example, our activities use AI to underpin aircraft design, optimisation and trajectory resolutions, supporting safe AI in terms of network computing, developing autonomous systems for drones and self-driving cars, and assuring flow monitoring and control in oil and gas pipelines. There is also a current research cell exploring the broader issues of AI.

Examples of Cranfield data science interests include transforming raw data and data analytics for airline maintenance operations, radar and imaging signals, machine learning for signals and damage classification, identifying materials in waste management, transitioning towards a re-distributed manufacturing model for consumer goods, and structuring knowledge in high value manufacturing and maintenance service provision, managing staff turnover and supporting high reliability equipment.

Our robotics interests lie in engineering and automation although the ethical design and application of robotics, the impact of and skills required for AI, and the experience of the end user of AI-based systems are also a focus. For example, driver behaviour and psychology, human computer interactions in unmanned vehicles and human factors considerations on cognitive psychology. One of our academics, Dr Sarah Fletcher, was also involved in development and release of the first ever standard to guide the ethical design and application of robotics (BS 8611).