Institute of Cyberspace Technology | Academic Staff

Prof. MA Chi Hung, Patrick 

PhD (The Hong Kong Polytechnic University)
Assistant Professor 

Chartered IT Professional, The British Computer Society 
Member, The British Computer Society
Member, The Hong Kong Computer Society 


Teaching Area: AI & Data Science 
Biography

Prof. Ma has many years of academic and industry experience in the fields of artificial intelligence and data science. Currently serving as Head of AI Programmes and Director of the AI Innovation Centre at the Institute of Cyberspace Technology of HKCT Institute of Higher Education, he also brings deep industry expertise from senior executive roles, including CTO, Technology Director, and Startup Co-founder & CEO. 

Prof. Ma earned his PhD in Computer Science from The Hong Kong Polytechnic University. His academic journey includes Visiting Assistant Professor and Lecturer positions at PolyU and Module Leader at CityU, and work as a Computational Biologist at The University of Hong Kong. He has published over 30 research papers in top-tier international journals and conferences, holds four invention patents, and led his team to receive prestigious awards including the Gold Award at the Geneva International Invention Exhibition. He has taught undergraduate and postgraduate AI and Data Science courses, supervised final-year and MSc projects, and mentored numerous students. 

In industry, Prof. Ma has driven cutting-edge AI commercialisation across multiple sectors and countries. He led the development of multifunctional edge AI chips, an edge-cloud collaborative training & inference engine, real-time video processing system, and big data analytics & predictive platform for manufacturing and retail. These innovations have delivered measurable impact—such as significant improvements in efficiency and performance—and successfully secured venture capital funding. 

Research Areas

AI & Data Science 

Selected Publications
Journal Articles

Lau, S.M., Luan, J., Cheung, L., Ma, C.H., & Lee, H. (2025). A novel method of emotion classification and reconstruction using VGGNet and StarGAN. Frontiers in Artificial Intelligence and Applications, 412, 399 – 415. 

Wang, Y., Cheung, L., Ma, C.H., Lee, H., & Lau, S.M. (2025). An emotional AI chatbot using an ontology and a novel audiovisual emotion transformer for improving nonverbal communication. Electronics, 14(21), 4304. 

Lau, S. M., Wu, J., Ma, C.H., & Lee, H. (2025). Using artificial intelligence, ontology, and 3D visualization in the metaverse for improving collaborative work and collective intelligence. The International Journal of Entrepreneurship and Innovation, 26(1), 66 – 84. 

Ma, C. H., & Chan, C. C. (2011). Incremental fuzzy mining of gene expression data for gene function prediction. IEEE Transactions on Biomedical Engineering, 58(5), 1246–1252. 

Ma, C. H., & Chan, C. C. (2010). Discovering interesting motif-sets for multi-class protein sequence classification. Journal of Computational Biology, 17(5), 733–743. 

Ma, C. H., & Chan, C. C. (2009). An iterative data mining approach for mining overlapping co-expression patterns in noisy gene expression data. IEEE Transactions on NanoBioscience, 8(3), 252–258. 

Ma, C. H., & Chan, C. C. (2009). A novel approach for discovering overlapping clusters in gene expression data. IEEE Transactions on Biomedical Engineering, 56(7), 1803–1809. 

Ma, C. H., & Chan, C. C. (2008). Inference of gene regulatory networks from expression data by discovering fuzzy dependency relationships. IEEE Transactions on Fuzzy Systems, 16(2), 455–465. 

Ma, C. H., & Chan, C. C. (2008). UPSEC: an algorithm for classifying unaligned protein sequences into functional families. Journal of Computational Biology, 15(4), 431–443. 

Ma, C. H., & Chan, C. C. (2007). An effective data mining technique for reconstructing gene regulatory networks from time series expression data. Journal of Bioinformatics and Computational Biology, 5(3), 651–668. 

Ma, C. H., Chan, C. C., Yao, X., & Chiu, K. Y. (2006). An evolutionary clustering algorithm for gene expression microarray data analysis. IEEE Transactions on Evolutionary Computation, 10(3), 296–314. 

Ma, C. H., Chan, C. C., & Chiu, K. Y. (2005). Clustering and re-clustering for pattern discovery in gene expression data. Journal of Bioinformatics and Computational Biology, 3(2), 281–301. 

Conference Papers

Ma, C. H., & Chan, C. C. (2008, November). Mining fuzzy association patterns in gene expression data for gene function prediction. In 2008 IEEE International Conference on Bioinformatics and Biomedicine, Philadelphia, USA. 

Ma, C. H., Chan, C. C., & Yao, X. (2008, June). An effective evolutionary algorithm for discrete-valued data clustering. In 2008 IEEE Congress on Evolutionary Computation, Hong Kong, China. 

Ma, C. H., & Chan, C. C. (2008, May). An effective data mining technique for the multi-class protein sequence classification. In Proceedings of the 2nd IEEE International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China. 

Ma, C. H., & Chan, C. C. (2008, March). Mining gene expression patterns for the discovery of overlapping clusters. In Proceedings of the 6th European Conference on Evolutionary Computation, Shanghai, China. 

Ma, C. H., & Chan, C. C. (2008, January). An effective hybrid algorithm for gene expression data clustering. In Proceedings of the 6th Asia Pacific Bioinformatics Conference, Kyoto, Japan. 

Ma, C. H., & Chan, C. C. (2007, July). A clustering algorithm for discovering overlapping clusters in gene expression data. In Proceedings of the 3rd International Conference on Computational Intelligence, Banff, Canada. 

Ma, C. H., & Chan, C. C. (2006, December). Inference of gene regulatory networks from time series expression data: a data mining approach. In 2006 IEEE International Conference on Data Mining, Hong Kong, China. 

Ma, C. H., & Chan, C. C. (2006, September). A fuzzy data mining technique for the reconstruction of gene regulatory networks from time series expression data. In 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Toronto, Canada. 

Ma, C. H., & Chan, C. C. (2006, September). An effective data mining technique for classifying protein sequences into functional families. In IEEE CIT06 - Bioinformatics, Seoul, Korea. 

Ma, C. H., & Chan, C. C. (2006, April). A novel data mining algorithm for reconstructing gene regulatory networks from microarray data. In ACM SAC06 - Bioinformatics, Dijon, France. 

Ma, C. H., & Chan, C. C. (2006, February). Inference of gene regulatory networks from microarray data: a fuzzy logic approach. In 4th Asia Pacific Bioinformatics Conference, Taiwan, China. 

Ma, C. H., & Chan, C. C. (2003, November). Discovering clusters in gene expression data using evolutionary approach. In Proceedings of the IEEE ICTAI03, California, USA. 

Ma, C. H., Chan, C. C., & Chiu, K. Y. (2003, September). Discovering clusters in gene expression data. In Proceedings of the 7th Joint Conference on Information Sciences, North Carolina, USA. 

Ma, C. H., & Chan, C. C. (2003, July). Clustering gene expression data with hybrid GA approach. In Proceedings of ASC03, Banff, Canada. 

Books

Ma, C. H., Chan, C. C., Yao, X, & Chiu, K.Y. (2008). Finding clusters in gene expression data using EvoCluster. In G.B. Fogel (Ed.), Computational Intelligence in Bioinformatics (pp. 41-66), IEEE Press and John Wiley & Sons, Inc. 

Ma, C. H., Chan, C. C., Yao, X, & Chiu, K.Y. (2008). Mining gene expression patterns for the discovery of overlapping clusters. In E. Marchiori and J.H. Moore (Ed.), Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (pp. 117–128), Springer Lecture Notes in Computer Science. 

Ma, C. H., & Chan, C. C. (2008). An effective hybrid algorithm for gene expression data clustering. Series on Advances in Bioinformatics and Computational Biology (vol. 6), Imperial College Press. 

Ma, C. H., & Chan, C. C. (2006). Inference of gene regulatory networks from microarray data: A fuzzy logic approach. Advances in Bioinformatics and Computational Biology (vol. 3), Imperial College Press. 

Ma, C. H., & Chan, C. C. (2003). Clustering gene expression data with hybrid GA approach. Artificial Intelligence and Soft Computing, ACTA Press.