I work as a Research Software Development Engineer in the machine learning group at Microsoft Research in Cambridge, UK.
My full-time role is in the development of Infer.NET which is a .NET platform for machine learning. I am also involved in its support within the internal and external communities, and its application to various problem domains. Infer.NET uses a model-based approach to provide state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, Bayesian networks, as well as a large range of custom models can be implemented using Infer.NET. You can think of Infer.NET as a language for modeling uncertainty (though in practice it is an API). Rather than requiring data to be shoe-horned into existing black-box machine learning algorithms, it encourages explicit modeling of the processes that generated the data. It then provides a compiler to convert inference queries on the model to a tailored bit of algorithmic code.
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—superseding 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.
- Cheng Zhang, Mike Gartrell, Thomas P. Minka, Yordan Zaykov, and John Guiver, GroupBox: A generative model for group recommendation, no. MSR-TR-2015-61, July 2015.
- Bar Shalem, yobach, John Guiver, and Chris Bishop, Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model, ECML/PKDD, September 2014.
- Allison J.B. Chaney, Mike Gartrell, Jake M. Hofman, John Guiver, Noam Koenigstein, Pushmeet Kohli, and Ulrich Paquet, A Large-scale Exploration of Group Viewing Patterns, ACM – Association for Computing Machinery, 25 June 2014.
- Matteo Venanzi, John Guiver, Gabriella Kazai, Pushmeet Kohli, and Milad Shokouhi, Community-Based Bayesian Aggregation Models for Crowdsourcing, in Proceedings of the 23rd International World Wide Web Conference, WWW2014 (Best paper award runner up), ACM, April 2014.
- Andrew D. Gordon, Thore Graepel, Nicolas Rolland, Claudio Russo, Johannes Borgstrom, and John Guiver, Tabular: A Schema-Driven Probabilistic Programming Language, no. MSR-TR-2013-118, 17 December 2013.
- Matteo Venanzi, John Guiver, Gabriella Kazai, and Pushmeet Kohli, Bayesian Combination of Crowd-Based Tweet Sentiment Analysis Judgments, Human Computer Interaction International Conference, November 2013.
- Allison Chaney, Mike Gartrell, Jake Hofman, John Guiver, Noam Koenigstein, Pushmeet Kohli, and Ulrich Paquet, Mining Large-scale TV Group Viewing Patterns for Group Recommendation, no. MSR-TR-2013-114, November 2013.
- Yoram Bachrach, Tom Minka, John Guiver, and Thore Graepel, How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing, in ICML , ICML, 2012.
- Elena Zheleva, John Guiver, Eduarda Mendes Rodrigues, and Natasa Milic-Frayling, Statistical Models of Music-listening Sessions in Social Media, in The 19th International World Wide Web Conference (WWW2010), April 26-30, 2010, Raleigh NC, USA, Association for Computing Machinery, Inc., April 2010.
- Peter A. Flach, John Guiver, Mohammed J. Zaki, Sebastian Spiegler, Bruno Golenia, Ralf Herbrich, Thore Graepel, and Simon Price, Novel Tools To Streamline the Conference Review Process: Experiences from SIGKDD'09, in SIGKDD Explorations, vol. 11, no. (2), Association for Computing Machinery, Inc., December 2009.