Professor Sunil Vadera

Head of School

  • Newton Building Room 122
  • T: +44 (0)161 295 5262
  • E: S.Vadera@salford.ac.uk
  • SEEK: Research profile

Office Times

“Please contact me via email for an appointment”

Biography

I am a Professor of Computer Science and Head of the School of Computing, Science and Engineering at the University of Salford.  I am a Fellow of the British Computer Society, a Chartered Engineer (CEng) and Chartered IT Professional (CITP).

I gained a first class BSc (Hons) in Computer Science and Mathematics from the University of Salford in 1982. Following graduation, I began my career as a Research Assistant and progressed to a Lectureship in Computer Science in 1984.  I hold a PhD from the University of Manchester in the area of Formal Methods of Software Development which was awarded in 1992.  I was promoted to a Senior Lecturer in 1997 and became a Professor in 2000.  I have held various roles at Salford including as Associate Dean Research and Innovation,  Director of the Informatics Research Institute and Associate Head Teaching.

Externally, I was Chair of the British Computer Society Academic Accreditations Committee with responsibility for professional accreditation of all UK University programmes in Computer Science from January 2007 to December 2009.  I have been an external examiner for several institutions including Loughborough University, Liverpool University,  Leeds Metropolitan University, Northumbria University,  London Southbank University and Middlesex University.  I have also carried out reviews of Computer Science departments in Universities in Jordan, Algeria, China,  India and Sri Lanka.

Teaching

Introduction to A.I.

Artificial Intelligence and Neural Networks

Research Interests

My research interests are in Machine Learning and Data Mining. 

My research has included work on machine learning for sensor validation with the Mexican Instituto de Electricas, a British Gas funded project applying data mining on SMART meters data, a British Gas funded project applying data mining to SMART meter data with a view to understanding energy consumption behaviour,  the development of a system known as Dust-Expert that advises on the relief venting of explosions in chemical processes for the Health and Safety Executive and research on data mining for credit rating of sub-prime loans for East Lancashire Moneyline (IPS) Ltd .

I am currently working on an FP7 funded project SEEDS: Self learning Energy Efficient builDings and open Spaces project that aims to reduce energy consumption and CO2 emissions by using self-learning methods based on data collected from wireless sensors.  The project is with partners from  industrial partners from Europe including Fraunhofer, CEMOSA, Ferrovial, Softcrits, CIDAUT and the University of Stavanger.

Qualifications and Memberships

Fellow of the BCS (FBCS), CITP, C.Eng.

PhD in Computer Science, University of Manchester, 1992

MSc in Computer Science, University of Manchester 1986

BSc (Hons) Computer Science and Mathematics, University of Salford, 1982

Publications

Susan Lomax and Sunil Vadera(2013). A survey of cost-sensitive decision tree induction algorithms. ACM Computing Surveys. 45, 2, Article 16, 35 pages.

Zhongzhi Shi,  David Leake, Sunil Vadera(Eds), Intelligent Information Processing VI, Springer, Berlin, Heidelberg, 2012.

Susan Lomax and Sunil Vadera(2011).  An empirical comparison of cost-sensitive decision tree induction algorithms, Expert Systems, Volume 28, No 3, pp 227-268

Khairy Kobbacy and Sunil Vadera  (eds), Special Issue on Intelligent Management Systems in Operations, Journal of Manufacturing Technology Management, Vol 22, No 6, 2011.

Sunil Vadera(2010). CSNL: A cost-sensitive non-linear decision tree algorithm. ACM Transaction on Knowledge. Discovery from Data 4, 2, 25 pages.

F. Meziane and S. Vadera (Eds), Artificial intelligence applications for improved software engineering development: new prospects, Information Science Reference, Hershey, New York,  2010.

Sunil Vadera, et al. (2008), Using Wittgenstein’s family resemblance principle to learn exemplars, Foundations of Science, Volume 13, No 1, pp67-74.

PH Ibargüengoytia, S Vadera, LE Sucar (2006). A probabilistic model for information and sensor validation, The Computer Journal 49 (1), pp 113-126.