RESEARCH My research focusses on use of information theory to develop and extend machine learning techniques. Previous projects have targeted data compression, cognitive science neural network representation, computational creativity, the problem of induction, kolmogorov complexity, sequence prediction, algorithmic music, analogy formation, minimum description length encoding, information refinement and formal concept analysis. Talks   Monograph   Text book	PAPERS Recent papers include Representation recovers information. Cognitive Science, 33, No. 8, 2009, pp. 1-30, Hierarchical markov modeling for generative music. In Proceedings of the International Computer Music Conference ICMC-09, 2009, pp. 49-52, Analogy as exploration. Proceedings of the 5th International Joint Workshop on Computational Creativity. Listing of online papers Listing of online presentations	C H R I S   T H O R N T O N Dept. of Informatics, Univ. of Sussex, UK Email: C.Thornton at domain sussex.ac.uk
TEACHING I teach a 16 lecture module on Machine Learning, a 16 lecture module on Automated Reasoning and AI Programming, a 14 lecture module on Knowledge Representation and a 16 lecture module on Generative Creativity. I also convene our MSc programme in Creative Systems. In this pioneering initiative, students learn to combine use of new creative technologies with methods from artificial intelligence and computational creativity.	SOFTWARE Replex Music. Informational mashups of symbolic music. The applet lets you use hierarchical Markov models to generate variations of sequential data from `Markov cascades'. Can be used to generate variations of MIDI music. Psychophone Demonstrates formation of sequential communication from conceptual structure. (See lower-middle window for usage info.) BugWorks Drag-and-drop robot-simulator with automatic tutor. Select 'Tutor' for usage info.	CV BSc Economics, MSc Computer Science (Imperial College), DPhil Artificial Intelligence. Lecturer in AI (University of Edinburgh), Lecturer in Informatics (Sussex). Expertise in Machine Learning, Neural Networks, Complex Systems Analysis and Genetic Algorithms. Tecnical skills in Web mining, programming (Java/C/Perl) and Data Mining. Teaching experience in many topics of computer science (e.g., Operation Systems, Networks) and AI (e.g., Game Playing and Knowledge Representation).

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