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.