MIKE TIPPING received a B.Eng. degree (First Class) in electronic engineering from Bristol University in 1990, and an M.Sc. in Artificial Intelligence from the University of Edinburgh in 1992. He completed his Ph.D. at Aston University in 1996, in the field of neural computing. His thesis focussed on data analysis and visualisation with neurally-inspired topographic mapping systems.

From 1996–98, Mike was a Research Fellow in the Neural Computing Research Group at Aston University, working on probabilistic learning models and their application to exploratory data analysis and visualisation. His influential papers from that time on "probabilistic principal component analysis" remain highly-cited.

Mike joined Microsoft Research (Cambridge, UK) in 1998, where he focussed on the development and application of advanced probabilistic models in machine learning, artificial intelligence, and statistical pattern recognition. During his early years at Microsoft, he concentrated on advancing "Bayesian learning" methodology, with particular application to handwriting recognition for the "Pocket PC". Most notably, he invented a learning system called the "relevance vector machine" which has subsequently become extremely popular world-wide. He has also made original contributions to applications in information retrieval, image super-resolution, signal processing, bioinformatics and interactive entertainment, and has filed for and/or been awarded fourteen patents to date.

A number of Mike's research papers in these, and other, areas are available on the "Science" page of this site (and a comprehensive set is available elsewhere online).

Mike left Microsoft Research in early 2006 and, alongside Mark Hatton, set up "Vector Anomaly" in order to further the development and application of innovative research technologies.