Professor Sunil Vadera

School of Science, Engineering and Environment

Photo of Professor Sunil Vadera

Contact Details

New SEE Building

Office hours: Thursday 10am-12pm



Current positions

Professor of Computer Science


Sunil Vadera is a Professor of Computer Science at the University of Salford. He is a Fellow of the British Computer Society, a Chartered Engineer (C.Eng) and Chartered IT Professional (CITP).

He has held many leadership roles including serving as Dean of the School of Computing, Science and Engineering, Associate Dean of Research, Associate Head Teaching, and Director of Informatics Research Institute.

He is past Chair of the British Computer Society (BCS) Accreditations Committee which has responsibility for accreditation of the UK degrees in Computing. Sunil was awarded the Amity award for Research contributions to AI and Neural Networks in 2018.

Areas of research

Artificial Intelligence, Machine Learning, Deep Learning, Decision Tree Learning, Bayesian Networks, Cost-Sensitive Learning, Innovative Applications of AI

Areas of supervision

Deep Learning, Machine Learning


  • Deep Learning (Level 6)
  • Programme Leader for MSc in Artificial Intelligence

Research Interests

Sunil is the lead investigator of the University of Salford’s contribution to the Greater Manchester AI Foundry, which aims to support SMEs to develop innovative products using  AI. His research is driven by the desire to close the gap between theory and practice in data mining and AI, something he has been doing for over three decades by working with industry. Examples of his work includes:

  • Developing new models for real time sensor validation of gas turbines  in collaboration with the Mexican Instituto de Electricas
  • Data mining of near miss  explosions data for project funded by and in collaboration with the Health and Safety Executive
  • Analysis of SMART meters data of over 40,000 households for British Gas that aimed to gain insight into consumer behaviour
  • An FP7 funded project on Self-Learning Energy Efficient Buildings and Open Spaces
  • Several Innovate UK funded Knowledge Transfer Partnerships that use AI and Machine Learning to develop innovative applications

His main line of research in recent years has been in the field of cost-sensitive learning, and in deep learning, where he is studying methods of reducing the size of deep neural networks.  His research has been published in some of the leading outlets in the field, including the Computer Journal, ACM Transactions on Knowledge Discovery from Data, ACM Computing Surveys, Journal of the Operational Research Society, Expert Systems Journal, Foundations of Science, IEEE Transactions of Power Systems and IEEE Access.

Qualifications and Memberships


  • PhD in Computer Science (Manchester University, 1992)
  • Fellow of the British Computer Society,
  • Chartered Engineer (C.Eng) 
  • Chartered IT Professional (CITP)
  • EPSRC Peer Review College