Machine learning, pattern recognition and neural information processing systems are among my primary research areas. Methods from these areas can be used to separate good data samples from bad data samples, and my team and I work on making those methods more precise and faster. I am also interested in knowledge representation and artificial intelligence - in my lab we are building software systems that can control autonomous robots.
Early on I expected to become an artist, musician or architect, however a knack for solving problems of a mathematical and scientific nature meant people continued coming to me with more and more challenging tasks.
I'm fascinated by how humans can learn amazing skills such as playing a musical instrument, building a computer or recognising faces. I want to understand how this works and how we can model it.
Associate Professor Chalup completed his undergraduate studies in Germany at the universities of Konstanz, Erlangen-Nuernberg, and Heidelberg where he graduated in mathematics with neuroscience. In 2002 he received his PhD from Queensland University of Technology in Brisbane where he studied at the Machine Learning Research Centre.
Associate Professor Chalup then came to Newcastle where he started the Interdisciplinary Machine Learning Research Group which is now part of the Newcastle Robotics Lab. These groups have the common objective to advance research in the area of 'Anthropocentric Biocybernetic Computing'. It investigates the complex interactions between humans and their environment on all levels.
When applied to real-world computing and autonomous agents the aim is to develop artificial systems that approximate human-like skills on tasks such as vision processing, facial expression analysis, space representation, and human-robot interaction. Machine learning techniques are employed for fine tuning the parameters of general models until they perform at extraordinary levels of skill on selected tasks.
The area of Big Data Analytics is using methods from machine learning, data mining and other domains and adjusts them so that they can cope with huge data sets. This will have significant impact on the future of health and other areas.