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 and the Amity Award for
Research in 2018 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 a major Software House.
Marat Akhmet is a professor of mathematics at Middle East Technical University (Ankara, Turkey) known for his research on the chaos and bifurcation theory in differential equations and hybrid systems with applications in physics, neural networks, biology, medicine and economics . Born in Kazakhstan, he studied at Aktobe State University. He received his doctorate in 1984 at Kiev University . He has been awarded a Science Prize of TUBITAK (Turkey, 2015), for best achievments in scientific research. He is an author of four books: "Principles of Discontinuous Dinamical Systems", Springer, 2010, "Nonlinear Hybrid Continuous Discrete-Time Models", Atlantis Press (Springer), 2011, "Neural networks with Discontinuous Impact Activations," Springer, 2014, and "Replication of Chaos in Neural Networks, Economics and Physics", Springer&HEP, 2015. His has solved the Second Peskin conjecture for Integrate-and-fire biological oscillators, has introduced and developed theory of differential equations with piecewise constant argument of generalized type, many aspects of discontinous dynamical systems. The last decade his main subject of research is input-output analysis of chaos and irregular behavior of hybrid neural networks.
Branislav Vuksanovic was born in Osijek,
Croatia in 1962. He graduated from the
University of Belgrade, Serbia with degree in
Electrical and Power Engineering. He holds MSc
degree in Measurement and Instrumentation from
South Bank University, London and a PhD in
Active Noise Control from the University of
Previously, he worked as a Project Engineer for Croatian Electricity Board in Osijek, Croatia. During his academic career he worked as a Research Fellow at Sheffield and Birmingham Universities on Optical Brain Imaging and Medical Video Compression projects. He also worked as a Lecturer at the University of Derby where he was a member of Sensors and Controls Research Group. Currently he works as a Senior Lecturer at the University of Portsmouth, School of Engineering. He has published papers in the field of active noise control, biomedical signal processing and pattern recognition for intrusion detection and knowledge based authentication. He also published one book in Digital Electronics and Microcontrollers field.
Dr Branislav Vuksanovic is a member of IET, ILT and IACSIT. His current research interests are in the application of pattern recognition techniques for power systems and analysis of ground penetrating radar and ECG data.