Dr David Chik

Research Fellow

Anglia Ruskin IT Research Institute

Areas of Expertise: Computing and technology

David has a passion for solving real world problems (especially in healthcare) using advanced computational intelligence and through global collaboration.


View David's profile on Google Scholar.


Before coming to Anglia Ruskin, David worked as a software engineer in Inferret Limited Japan, where he obtained hands-on experience of developing speech recognition systems and implementing machine learning on big data. 

David's research experience includes KyuTech Japan, where he developed a home safety robot; RIKEN Japan, where he developed a brain model of working memory and executive functions; Plymouth University UK, where he developed a brain model of visual attention; and the University of New South Wales in Australia, where he developed a nonlinear dynamical model of heart disease.

David has published 30 refereed research articles, and received a Spotlight Presentation Award for his poster in Neuroinformatics 2010. He served as a conference session chair in DS07, and has been an invited speaker in many universities (Sydney, 2006; Exeter, 2008; Cologne, 2009; KyuTech, 2012). He's a member of UK Mathematical Neuroscience Network and Japanese Neural Network Society.

Research interests

David's areas of research interests include:

  • Deep learning – a branch of machine learning using multiple nonlinear processing layers to model high-level abstractions in data.
  • Mobile health – the practice of medicine and public health supported by mobile devices. One objective is to develop an intelligent assessment and reminder system for the elderly, disabled, and citizens with chronic diseases.
  • Domestic robots – robots that are capable of undertaking household works and engaging in social interactions.
  • Computational neuroscience – the study of brain function in terms of the information processing properties of the structures that make up the nervous system.

Areas of research supervision

David is currently supervising the following PhD researcher

We welcome applications for postgraduate research under the supervision of David Chik. We also have a number of exciting research project opportunities that you may want to consider. These are self-funded research proposals that have already been identified by our staff.


  • PhD, University of Hong Kong
  • BSc (Hons) Physics, University of Hong Kong

Selected recent publications

Chakraborty, A., Chik, D., Biba; M. and Hossain, M.A. A Decision Scheme based on Adaptive Morphological Image Processing for Mobile Detection of Early Stage Diabetic Retinopathy. SKIMA 2017.

He, C.,Li, W. and Chik, D. Waveform Compensation of ECG Data Using Segment Fitting Functions for Individual Identification. 2017 International Conference on Computational Intelligence and Security

Hossen, M.Z., Chik, D., Chakraborty, A. and Hossain M.A., 2015. Real-time mobile enabled scheme for virtual spectacle frame selection. SKIMA2015, paper id: 62.

Chik, D., 2014. Compact neural network: parameter reduction using sign combinations. ICIC Express Letters, 8(8), pp.2105-2111.

Tripathi, G.N., Chik, D. and Wagatsuma H., 2013. How difficult is it for robots to maintain home safety? A brain-inspired robotics point of view. ICONIP 2013, Part I, Lecture Notes in Computer Science, 8226, pp.528-536.

Chik, D., Tripathi, G.N. and Wagatsuma H., 2013. A method to deal with prospective risks at home in robotic observations by using a brain-inspired model. ICONIP 2013, Part III, Lecture Notes in Computer Science, 8228, pp.33-40.

Borisyuk, R., Chik, D., Kazanovich, Y. and da-Silva-Gomes J., 2013. Spiking neural network model for memorizing sequences with forward and backward recall. BioSystems, 112(3), pp.214-223.

Chik, D. and Dundas, J., 2013. Machine implementation of human-like intuition. ICIC Express Letters, 7(8), pp.2231-2235.

Chik, D., 2013. Theta-alpha cross-frequency synchronization facilitates working memory control - a modeling study. Springer Plus, 2(14), pp.1-10.

Chik, D., 2012. Does dynamical synchronization among neurons facilitate learning and enhance task performance? Journal of Computational Neuroscience, 33(1), pp.169-177.