Faculty:Faculty of Science & Technology
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.
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.
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.