Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Professor of Statistics



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




544Huxley BuildingSouth Kensington Campus





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




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). 



Shlomovich L, Cohen E, Adams N, 2022, A parameter estimation method for multivariate aggregated Hawkes processes, Statistics and Computing, ISSN:0960-3174

Shlomovich L, Cohen E, Adams N, et al., 2022, Parameter estimation of binned Hawkes processes, Journal of Computational and Graphical Statistics, ISSN:1061-8600

Yahze L, Adams N, Bellotti A, 2022, A relabeling approach to handling the class imbalance problem for logistic regression, Journal of Computational and Graphical Statistics, Vol:31, ISSN:1061-8600, Pages:241-253

Holtegebaum H, Adams N, Lau D-H, 2021, Unsupervised streaming anomaly detection for instrumented infrastructure, Annals of Applied Statistics, Vol:15, ISSN:1932-6157, Pages:1101-1125

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

More Publications