| Alexander Gammerman | |
| Birth Date: | 2 November 1944 |
| Birth Place: | Almaty, Soviet Union[1] |
| Nationality: | British |
| Workplaces: | Royal Holloway, University of London |
| Alma Mater: | Saint Petersburg State University Russia |
| Known For: | Conformal prediction |
| Field: | Machine learning Statistics |
Alexander Gammerman is a British computer scientist, and professor at Royal Holloway University of London. He is the co-inventor of conformal prediction. He is the founding director of the Centre for Machine Learning at Royal Holloway, University of London, and a Fellow of the Royal Statistical Society.
Gammerman's academic career has been pursued in the Soviet Union and the United Kingdom. He started working as a Research Fellow in the Agrophysical Research Institute, St. Petersburg. In 1983, he emigrated to the United Kingdom and was appointed as a lecturer in the Computer Science Department at Heriot-Watt University, Edinburgh.[1] Together with Roger Thatcher, Gammerman published several articles on Bayesian inference.[2] In 1993, he was appointed to the established chair in Computer Science at University of London tenable at Royal Holloway and Bedford New College, where he served as the Head of Computer Science department from 1995 to 2005.[3] In 1998, the Centre for Reliable Machine Learning was established, and Gammerman became the first director of the centre.
Gammerman has published 7 books, more than 150 research papers, and has an estimated h-index of 34.[4] He ranks amongst the top 1% researcher in artificial intelligence and machine learning. [5]
In 1996, Gammerman received the P.W. Allen Award from the Forensic Science Society.[6] In 2006, he became a Honorary Professor, at University College London. In 2009, he became a Distinguished Professor at Complutense University of Madrid, Spain. In 2019, he received a research grant funded by the energy company Centrica about predicting the time to the next failure of equipment.[7] In 2020, he received the Amazon Research Award for the project titled Conformal Martingales for Change-Point Detection[8] [9] [10]