Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Professor of Statistics



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




6M55Huxley 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). 



Sanna Passino F, Adams N, Cohen E, et al., 2023, Statistical cybersecurity: a brief discussion of challenges, data structures, and future directions, Harvard Data Science Review, Vol:5, ISSN:2644-2353, Pages:1-10

Hallgren KL, Heard NA, Adams NM, 2022, Changepoint detection in non-exchangeable data, Statistics and Computing, Vol:32, ISSN:0960-3174, Pages:1-19

Shlomovich L, Cohen E, Adams N, 2022, A parameter estimation method for multivariate binned Hawkes processes, Statistics and Computing, Vol:32, ISSN:0960-3174

Shlomovich L, Cohen E, Adams N, et al., 2022, Parameter estimation of binned Hawkes processes, Journal of Computational and Graphical Statistics, Vol:31, ISSN:1061-8600, Pages:990-1000

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

More Publications