Nature Inspired Computing and Engineering
Nature presents the best example of how to solve complex problems efficiently and effectively. The main objectives of the Nature Inspired Computing and Engineering (NICE) research group are therefore to develop computational models and algorithms inspired from natural intelligence found in physical, chemical, social and biological systems, and to solve practical problems arising from various engineering fields related to health, security, energy and environment.
The NICE group adopts a bi-directional research strategy consisting of a top-down, objective-driven approach and a bottom-up problem-driven approach. The top-down approach aims to build up computational models for understanding biological and social intelligence found in nature. We are particularly interested in neural information processing in the brain and the organizing principles of neural development from the evolutionary perspective.
The bottom-up approach is concerned with developing efficient mathematical and statistical, machine learning and optimization algorithms for solving complex problems found in optimization and control, signal processing and pattern recognition, data mining and knowledge extraction, multi-criterion decision-making, and self-organization of collective systems. Real-world applications include brain-computer interfacing, medical image analysis, source localization and separation, motion tracking, threat detection, copyright protection, intelligent heat solutions, aerodynamic design optimization, and robotics.
Research Funding and Collaborations
Research within the NICE group has been funded by the EPSRC, BBSRC, Leverhulme Trust, Royal Academy of Engineering, as well as industry collaborators including Waterfall Solutions Ltd, Alpha-Active Ltd, Intellas UK Ltd, HR Wallingford, Bosch Thermotechnology Ltd, Aero Optimal Ltd, Santander, Moorfields Eye Hospital, Royal Surrey County Hospital and Royal Botanic Gardens, Kew.
The group has close collaborations with other research groups within the university and with other prestigious national and international institutions such as RIKEN Brain Science Institute in Japan, and Institute of Psychiatry and Epileptology, King’s College London, UK.
Self-organised adaptive multi-robot pattern formation
Leader Following 2: Obstacle avoidance
Following Morphogen Gradient by a Small Swarm of Kilobots