Researchers within CRIS work in Labs associated with their specific field of interest. Details of the various labs within CRIS are still to be finalised, but the following labs are already operational:
Online documents require intelligence to automatically adapt content and layout to different display devices and reader contexts. The Adaptive Diagrams and Documents lab is researching techniques based on constraint optimisation to achieve this goal.
KDlab undertakes research in a wide range of knowledge discovery topics, with specific strengths in Bayesian Modelling, Bayesian Reasoning, Machine Learning and Data Mining.
Website: http://www.csse.monash.edu.au/~webb/kdlab.html
The CTI-Monash Centre for Research in Optimisation of travel, transport and logistics seeks to increase the accuracy, scalability, and flexibility of optimisation software for solving problems in industry and government organisations. Our approach is to combine techniques from research areas that are currently separate, including constraint programming, mathematical programming and population-based techniques. Our methodology is to separate problem modelling from problem solving, and to transform the model into a form that can be efficiently solved by hybrid algorithms.
The Natural Computation Lab studies natural phenomena as computational processes. We aim to understand the mechanisms that sustain and perpetuate biological and social systems. We use the insights gained to develop new solutions to a broad range of technical problems. Nature has evolved many ways of solving complex problems, and nature-inspired self-organizing methods provide a promising framework for managing the increasing complexity of man-made systems and organisations. Research focus areas include swarm intelligence, social networks, evolutionary computing, evolutionary theory, neural networks, and artificial life.
The User Modeling and Natural Language Group (UMNL) focuses on the application of Statistical and Machine Learning techniques to human-computer interaction.
User modeling consists of inferring and representing the beliefs, preferences, skills or objectives of users interacting with a computer system. A user model enables a system to adapt its behaviour to the needs and requirements of a user. User models have been used in several applications, such as Intelligent Tutoring Systems (as student models), on-line help systems, and information filtering systems.
Natural language processing consists of understanding and generating natural language discourse. Examples of applications of Natural language processing techniques are understanding user queries, explanation generation and document summarization.