About Associate Professor Rafael A. Calvo

Rafael’s work aims at building software applications that learn, and that help people learn.

Dr Rafael Calvo conducts research centred around developing data mining algorithms and software architectures that integrate into real world applications. These applications are referred to as Intelligent Information Systems and he has created both web-based and mobile application systems His best example of developing new algorithms is a world-class pattern-recognition method for forecasting sunspots, a standard benchmark for time series forecasting that has important effects on weather and telecommunications. The research was based on a series of neural network algorithms that forecast the number of sunspots within a given period of time. The pattern-recognition method was later applied to other areas to successfully forecast monsoon rainfall in India. Monsoon rainfall has dramatic effects on the lives of more than a billion people living in the Indian subcontinent; improving the forecast of monsoon rainfall helps planning for flood emergencies and optimum crop yields.

Dr. Calvo has successfully applied novel machine learning techniques to other fields and built information systems that use them, particularly text mining. They include: a Naive Bayes and Neural Network that automatically classifies corporate announcements in the Capital Markets CRC; Naïve Bayes that classifies and redirects student assignments in mathForum.org and a mobile phone application for the Australian Biosecurity CRC that uses Naïve Bayes to produce probable diseases from a list of disease symptoms.Note: Dr. Calvo has also made significant contributions to the field of educational technologies. RC introduced the concept of “e-learning frameworks”, bringing together the ideas of educational design patterns (i.e. scripts) with architectural abstractions used in software engineering. His research has provided increasing evidence of how student conceptions about learning tasks are related to the strategies and intent with which they approach the tasks, and with the learning outcomes. This student-centred research approach has been applied to several empirical studies around the development of learning technologies and to a new development methodology informed by the student experience. Dr. Calvo's most recent research interest is the development of Brain Computer Interfaces that allow a user to control a device by just using his brain signals.

Selected publications

  • García Adeva, J. J. and Rafael Calvo. (2006) ‘Mining Text with Pimiento’. IEEE Internet Computing. Vol. 10, No. 4. pp. 27-35, July/August.
  • Turani, A and Calvo, R.A. (2006) ‘Beehive: A Software Application for Synchronous Collaborative Learning’. Campus Wide Information Systems. Vol 23, No 3. pp 196-209.
  • Turani, R.A. Calvo and P. Goodyear (2005) "An Application Framework for Collaborative Learning" International Web Engineering Conference 2005. D. Lowe, M. Gaedke (Eds.), Lecture Notes in Computer Science 3579, pp 243-251. Springer-Verlag Berlin Heidelberg.
  • Lee J.M. and Calvo R.A. (2005) Scalable document Classification. Intelligent Data Analysis. 9, 4, p04-29.
  • Calvo R.A. (2003). ‘User Scenarios for the Design and Implementation of iLMS’, in R. Calvo and M. Grandbastien (eds), Proceedings of the first workshop ‘Towards Intelligent Management  Systems’, Volume IV of the Supplemental Proceedings of the 11th International Conference on Artificial Intelligence in Education, University of Sydney, Australia.
  • Williams K., R. A. Calvo and D. Bell (2003). Automatic categorization of questions for a mathematics education service. Proceedings of the 11th International Conference on Artificial Intelligence in Education, University of Sydney, July 2003. Sydney, IOS Press.
  • Calvo R.A. (2002). Introduction to e-commerce systems. Pearson Education 2002, 220pp.
  • Calvo R.A., H. Navone and H.A. Ceccatto (2000). Neural network analysis of time series: applications to climatic data., In Smolka P.P., Wolkheimer W. (Eds). Southern Hemisphere Paleo- and Neoclimates: key sites, methods, data and models. Springer Science Publishers, Heidelberg, 7–16.
  • Partridge M. and R.A. Calvo. Fast dimensionality reduction and simple PCA. Intelligent Data Analysis, 2(3), 1998.
  • Calvo R. A., H. A. Ceccatto and R.D. Piacentini. Neural network prediction of solar activity. The Astrophysical Journal, 444(2): 916–21, 1995