Download Research Challenges in Applying Data Mining for

Research Challenges in Applying Data Mining for Managing Energy Consumption,
Finance and Social Inclusion
Professor Sunil Vadera, University of Salford, UK
Sunil Vadera is the Dean of the School of Computing, Science and Engineering at the
University of Salford in Greater Manchester, UK. Sunil was Chair of the UK BCS
Knowledge Discovery and Data Mining Symposium held in Salford in 2009, A Programme
Chair of the IFIP conference on Intelligent Information Processing in 2010, 2012,2016. His
research has been published in some of the leading outlets, including the Computer Journal,
ACM Transactions on Knowledge Discovery from Data, ACM Computing Surveys, Expert
Systems Journal, Foundations of Science, and IEEE Transactions of Power Systems. Sunil
was Chair of the British Computer Society Academic Accreditations Committee, that has
responsibility for professional accreditation of programmes in the UK, from 2007-2009. He
holds a PhD in Computer Science from the University of Manchester, is a Fellow of the BCS
and was awarded the BDO best British Indian Scientist and Engineer in 2014 in recognition
of his contributions to the field.
Sunil Vadera has led a number of projects in applying data mining and machine learning for
problems in Energy, Finance, and Policy over the last decade, including:
 Developing new models for real time sensor validation of gas turbines
 Data mining of near miss data for the health and safety executive
 Analysis of SMART meters data for British Gas
 A major FP7 funded project on Self-Learning Energy Efficient Buildings and Open
Spaces
 Analysing factors affecting children in need and troubled families
 Sub-prime lending aimed at improving financial inclusion
 Data mining for predicting client churn for IDOX Ltd, a major Software House
This presentation will give an overview of his experiences with some of these projects,
highlight the lessons learned and outline the future challenges that need to be addressed if Big
Data Analytics is going to be successful in addressing regional and global challenges such as
managing energy consumption, climate change, finance, health and social inclusion.
Selected publications

Lomax, S. and Vadera, S. (2016). A Cost-Sensitive Decision Tree Learning
Algorithm Based on a Multi-Armed Bandit Framework, The Computer Journal,
doi:10.1093/comjnl/bxw015, available online at:
http://comjnl.oxfordjournals.org/citmgr?gca=comjnl%3Bbxw015v1.

Nashnush, E., and Vadera, S. (2016). Learning cost-sensitive Bayesian networks via
direct and indirect methods, Integrated Computer-Aided Engineering, doi:
10.3233/ICA-160514, available on line at:
http://content.iospress.com/articles/integrated-computer-aided-engineering/ica514

Lomax, S. and Vadera, S.(2013). A survey of cost-sensitive decision tree induction
algorithms, ACM Computing Surveys, Vol45, No 2, 35 pages

Sunil Vadera (2010), CSNL: A Cost-Sensitive Non-Linear Decision Tree Algorithm,
ACM Transactions on Knowledge Discovery from Data, Vol 4, No 2, pp1-25

Ibarguengoyatia, P. Sucar, E. and Vadera, S. (2008). Sensor Validation, in Bayesian
Networks, O. Pourret, P. Naim, P. and B. Marcot (Eds), Wiley, pp187- 202.