Chris has over 20 years of industry experience ranging from software engineering; network design, security and management; IT architecture and project leading. His particular expertise is in enterprise application integration and distributed computing using Python, Java, XML, web services and mobile technologies. Chris co-authored a book 'Visualage and Transaction Processing in a Client/Server Environment' and other whitepapers while working for IBM last century. Chris' awards include an IBM Excellence award, an ISSC CEO's award and an UTS faculty teaching excellence award.
Current research interest is in educational data science including learning analytics, educational data mining and data visualisation especially when applied to the undergraduate engineering and IT programs.
- Member of the Australian Computer Society
- Member of the ACM
- Member of the Computer Science Teachers Association (CSTA)
- NSW Steering committee member for Digital Careers Australia
- Member, Insearch Course Advisory Committee
- Member, UTS Academic Board
Educational Data Science: Learning Analytics, Educational Data Mining, Data Visualisation
Distributed computing: Web services; Mobile applications; Java Enterprise; Service Oriented Architecture; Enterprise Application IntegrationMiddleware; Large scale XML datamining.
- Teaching computer science, internet technology and e-business subjects for the school of software and school of electrical and data engineering
- Engineering and IT Undergraduate Programs Coordinator
Wong, CH, Brassard, M, Nadal, W & Bitterer, A 1995, VisualAge and Transaction Processing in a Client/Server Environment, Prentice Hall.
VisualAge is IBM's object-oriented technology product. This book discusses the role of VisualAge in the development of transaction based applications.
Wong, C 2018, 'Sequence based course recommender for personalized curriculum planning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Artificial Intelligence in Education, Springer Link, London, United Kingdom, pp. 531-534.View/Download from: UTS OPUS or Publisher's site
© Springer International Publishing AG, part of Springer Nature 2018. Students in higher education need to select appropriate courses to meet graduation requirements for their degree. Selection approaches range from manual guides, on-line systems to personalized assistance from academic advisers. An automated course recommender is one approach to scale advice for large cohorts. However, existing recommenders need to be adapted to include sequence, concurrency, constraints and concept drift. In this paper, we propose the use of recent deep learning techniques such as Long Short-Term Memory (LSTM) Recurrent Neural Networks to resolve these issues in this domain.