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

Yahze L, Adams N, Bellotti A, 2021, A relabeling approach to handling the class imbalance problem for logistic regression, Journal of Computational and Graphical Statistics, ISSN:1061-8600

Adams N, Riddle-Workman E, Evangelou M, 2021, Multi-Type relational clustering for enterprise cyber-security networks, Pattern Recognition Letters, Vol:149, ISSN:0167-8655, Pages:172-178

Plasse J, Helfer Hoeltgebaum H, Adams N, 2021, Streaming changepoint detection for transition matrices, Data Mining and Knowledge Discovery, Vol:35, ISSN:1384-5810, Pages:1287-1316

Plasse J, Hoeltgebaum H, Adams NM, 2021, Streaming changepoint detection for transition matrices (Apr, 10.1007/s10618-021-00747-7, 2021), Data Mining and Knowledge Discovery, Vol:35, ISSN:1384-5810, Pages:1-1

Mikhailova A, Adams N, Hallsworth C, et al., 2021, Unsupervised deep learning-powered anomaly detection for instrumented infrastructure, Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, Vol:172, ISSN:2397-8759, Pages:135-147

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