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
Lau FD-H, Adams NM, Girolami MA, et al., 2018, The role of statistics in data-centric engineering, Statistics & Probability Letters, Vol:136, ISSN:0167-7152, Pages:58-62
Noble J, Adams NM, 2018, Real-Time Dynamic Network Anomaly Detection, Ieee Intelligent Systems, Vol:33, ISSN:1541-1672, Pages:5-18
Hoagn J, Cohen E, Adams N, 2017, Devising a fairer method for adjusting target scores in interrupted one-day international cricket, Electronic Journal of Applied Statistical Analysis, Vol:10, ISSN:2070-5948, Pages:745-758
Bodenham DA, Adams NM, 2017, Continuous monitoring for changepoints in data streams using adaptive estimation, Statistics and Computing, Vol:27, ISSN:0960-3174, Pages:1257-1270
Conference
Schon C, Adams N, Evangelou M, 2017, Clustering and Monitoring Edge Behaviour in Enterprise Network Traffic, 15th IEEE International Conference on Intelligence and Security Informatics - Security and Big Data (ISI), IEEE, Pages:31-36