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
Bodenham D, Adams N, 2023, Dean Bodenham and Niall Adams's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning', Journal of the Royal Statistical Society Series B: Statistical Methodology, ISSN:1369-7412
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