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Professor Nello Cristianini
Professor Nello Cristianini
Professor of Artificial Intelligence
BSc(Trieste), MSc(Lond), PhD(Bristol)
Theory and applications of Pattern Analysis algorithms. Applied to problems in Genomics, Web Analysis, Machine Intelligence. --After spending many years developing tools for the automatic discovery of patterns in data, and for enabling computer systems to learn from experience, my group is now focused on pushing the envelope in two core directions: detection of patterns in genomic data; detection of patterns in media content, with implications for web-based intelligence.
Nello Cristianini is a Professor of Artificial Intelligence at the University of Bristol since March 2006, and a holder of the Royal Society Wolfson Merit Award. He has wide research interests in the area of computational pattern analysis and its application to problems ranging from genomics, to computational linguistics and artificial intelligence systems. He has contributed extensively to the field of kernel methods. Before the appointment to Bristol he has held faculty positions at the University of California, Davis, and visiting positions at the University of California, Berkeley, and in many other institutions. Before that he was a research assistant at Royal Holloway, University of London. He has also covered industrial positions. He has a PhD from the University of Bristol, a MSc from Royal Holloway, University of London, and a Degree in Physics from University of Trieste. Since 2001 has been Action Editor of the Journal of Machine Learning Research (JMLR), and since 2005 also Associate Editor of the Journal of Artificial Intelligence Research (JAIR). He is co-author of the books 'An Introduction to Support Vector Machines' and 'Kernel Methods for Pattern Analysis' with John Shawe-Taylor, and "Introduction to Computational Genomics" with Matt Hahn (all published by Cambridge University Press).
- web mining
- cognitive systems
- computational genomics
- machine intelligence
- Flaounas, I, Ali, O, Lansdall-Welfare, T, De Bie, T, Mosdell, N, Lewis, J & Cristianini, N 2013, Research Methods in the Age of Digital Journalism: Massive-scale automated analysis of news-content: topics, style and gender. Digital Journalism, vol 1.
- Lansdall-Welfare, TO, Lampos, V & Cristianini, N 2012, Effects of the Recession on Public Mood in the UK. in: Mining Social Network Dynamics (MSND) session on Social Media Applications in News and Entertainment (SMANE) at WWW '12. ACM, pp. 1221 - 1226
- Lansdall-Welfare, TO, Flaounas, I & Cristianini, N 2012, Scalable corpus annotation by graph construction and label propagation. in: International Conference on Pattern Recognition Applications and Methods.
- Hensinger, E, Flaounas, I & Cristianini, N 2012, Modelling and predicting news popularity. Pattern Analysis and Applications.
- Turchi, M, De Bie, TEP, goutte, c & Cristianini, N 2012, Learning to Translate: a statistical and computational analysis. Advances in Artificial Intelligence, vol 1.
- Sudhahar, S, Lansdall-Welfare, T, Flaounas, I & Cristianini, N 2012, ElectionWatch: Detecting Patterns In News Coverage of US Elections. in: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL)., pp. 82
- Lampos, V & Cristianini, N 2011, Nowcasting Events from the Social Web with Statistical Learning. ACM Transactions on Intelligent Systems and Technology, vol 3.
- Flaounas, I, Ali, O, Turchi, M, Snowsill, T, Nicart, F, De Bie, T & Cristianini, N 2011, NOAM: News Outlets Analysis and Monitoring Systeml. in: ACM SIGMOD/PODS Conference, Athens, Greece, June 12-16 2011. ACM, pp. 1275-1278
- Hensinger, E, Flaounas, I & Cristianini, N 2011, Learning Readers' News Preferences with Support Vector Machines. Proceedings of 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011., pp. 322-331
- Ali, O, Flaounas, I, De Bie, T & Cristianini, N 2011, Celebrity Watch: Browsing News Content by Exploiting Social Intelligence. in: Machine Learning and Knowledge Discovery in Databases (ECML PKDD). Berlin: Springer-Verlag, pp. 613
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