John P. Guiver

Microsoft Research Ltd, 7 JJ Thomson Avenue, Cambridge CB3 0FB, England Email: joguiver@microsoft.com Phone: +44 1223 479 749 (direct) Fax: +44 1223 479 999 WWW: http://research.microsoft.com/~joguiver/

I work as a Research Software Design Engineer in the information retrieval group at Microsoft Research in Cambridge, UK. My current area of interest is in applying machine learning to problems in information retrieval. Here are some publications relating to ranking:

SoftRank: Optimising Non-Smooth Ranking Metrics M. Taylor, J. Guiver, S. Robertson, and T. Minka. WSDM ’08, pages 77–86. ACM, 2008.

Learning to Rank with SoftRank and Gaussian Processes John Guiver and Edward Snelson, SIGIR’08, July 20–24, 2008, Singapore.

I am also a member of the Infer.NET team. Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented using Infer.NET. Infer.NET is currently in a beta state, and only internal to Microsoft, though we hope to make it available externally at some point.

Before working at Microsoft Research, I spent several years developing Advanced Control software at Aspen Technology. One of the highlights was the development of a fully non-linear Model Predictive Controller (Aspen Apollo) which has been widely adopted in the polymer manufacturing industry. This was made possible by developing a new form of non-linear regression model (a Bounded Derivative Network) which has more natural interpolation and extrapolation properties than a traditional Multi-layer Perceptron model, and which provides guaranteed global behaviours such as montonic responses in specified input variables. A description of Bounded Derivative Networks can be found in 'Introducing the bounded derivative network—superceding the application of neural networks in control', P. Turner, J. Guiver, Journal of Process Control, pages 407–415, 2005

A description of the many practical issues faced in putting a non-linear controller into a large-scale manufacturing plant can be found in 'Experiences with Non-linear MPC in Polymer Manufacturing', Kelvin Naidoo, John Guiver, Paul Turner, Mike Keenan, Michael Harmse in Assessment and Future Directions of Nonlinear Model Predictive Control (Lecture Notes in Control and Information Sciences)

The Aspen Apollo controller was derived from an earlier controller developed at NeuralWare ('A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model', H. Zhao, J. Guiver, R. Neelakantan, L.T. Biegler, Control Engineering Practice, 9, 2001., 'State space nonlinear process modeling: identification and universality' ,G.B. Sentoni, J.P. Guiver, H. Zhao, and L.T. Biegler. AIChE Journal, March 1998).

At NeuralWare (one of the early commercial neural net companies, founded by Casey and Jane Klimasauskas in 1987), I was a principal research engineer and was, for many years, the lead sofware developer, responsible for the design and evolution of the company's neural net products.