Imperial College London

ProfessorNiallAdams

Faculty of Natural SciencesDepartment of Mathematics

Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8837n.adams Website

 
 
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Location

 

544Huxley BuildingSouth Kensington Campus

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Summary

 

Summary

Niall Adams is Professor of Statistics at Imperial College London. In addition to a variety of undergraduate and postgraduate teaching, he conducts research in classification, data mining, streaming data analysis and spatial statistics. Applications for this research are diverse, including bioinformatics, cyber-security  and retail finance.

Dr Niall Adams' personal web page can be found at http://stats.ma.ic.ac.uk/~nadams

 

 

 

Other Significant Activities

Editorial panel for Applied Statistics,  Journal of the Royal Statistical Society Series C (2008-2012)

Editorial panel for Statistical Analysis and Data Mining (2009-2014)

Plenary Lectures

Ed: Big Data in Cyber-Security: Host-Based IP Flow Monitoring using Adaptive Estimation”, (invited keynote) SITA 13, 8th International Conference on Intelligent Systems: Theories and Applications, Rabat, Morocco, (2013)

“Efficient streaming classification methods”, (invited), German Classification Society, Karlshrue, Germany (2010). 

 “Temporally-adaptive linear classification for handling population drift in credit scoring”, (invited), COMPSTAT 2010, Paris, France (2010). 

Publications

Journals

Lu X, Adams N, Kantas N, 2019, On adaptive estimation for dynamic Bernoulli bandits, Foundations of Data Science, Vol:1, ISSN:2639-8001, Pages:197-225

Bakoben M, Bellotti A, Adams N, Identification of credit risk based on cluster analysis of account behaviours, Journal of the Operational Research Society, ISSN:0160-5682

Conference

Lau D-H, Adams NM, The importance of analysing data from instrumented infrastructure, International Conference on Smart Infrastructure and Construction

Mikhailova A, Adams N, Hallsworth C, et al., Unsupervised deep learning for instrumented infrastructure: a case study, International Conference on Smart Infrastructure and Construction

Ward S, Cohen E, Adams N, 2019, Fusing multimodal microscopy data for improved cell boundary estimation and fluorophore localization of Pseudomonas aeruginosa, Asilomar Conference on Signals, Systems and Computers, IEEE

More Publications