Research interests & Projects

Research Interests

Artificial Intelligence and Machine Learning methods:

• Artificial Neural Networks and Deep Learning (e.g. Feed-forward networks, Deep neural networks, Recurrent networks)

• Bayesian modelling (Bayesian single level/hierarchical models)

• Committee Machines (e.g. Ensemble Learning, Mixture of Experts)

• Clustering methods (e.g. K-means clustering, Gaussian Mixture Models and Self-Organising Maps)

• Dimensionally Reduction (e.g. Principal Component Analysis, Kernel PCA)

• Fuzzy Logic and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

• Inference approximations (e.g. Expectation Maximisation, Variational Bayesian)

• Information theory (e.g. Mutual Information)

• Kernel machines (e.g. Support Vector Machines, Relevance Vector Machines, Gaussian Processes)

• Recursive Bayesian estimations (e.g. Kalman Filter, Extended Kalman Filter, Particle Filter)

• Sampling methods (e.g. Monte Carlo, Importance Sampling, Gibbs sampler)

Health Monitoring Technologies: 

• Feature Extractions

• Diagnostics

• Prognostics

Modelling and Control Design:

• Intelligent Control Systems

• Systems Identification 

• Modelling of Complex Systems 

Applications:

• Data Analytics and Machine Learning for Applied Physics 

  • Friction process at nanoscale  

  • Atmosphere and environmental sciences 

• Intelligent sensors calibration and virtual sensors

  • Low-cost sensors for monitoring air quality and meteorology

• Diagnostics and Prognostics

  • Bearing condition monitoring 

  • Gas turbine engines health monitoring 

  • Lithium-ion batteries condition monitoring 

• Modelling & Control

  • Bio-medical robotics 

  • Robot manipulator 

  • Twin rotor helicopter