Edwin R. Hancock holds a BSc degree in physics (1977), a PhD degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham, and a doctorate Honoris Causa from the University of Alicante in 2015. From 1981-1991 he worked as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). During this period, he worked on high energy physics experiments at the Stanford Linear Accelarator Center (SLAC) providing the first measurements of charmed particle lifetimes. He also held adjunct teaching posts at the University of Surrey and the Open University. In 1991, he moved to the University of York as a lecturer in the Department of Computer Science, where he has held a chair in Computer Vision since 1998. He leads a group of some 25 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical and pattern recognition. He is currently working on graph matching, shape-from-X, image databases, and statistical learning theory. His work has found applications in areas such as radar terrain analysis, seismic section analysis, remote sensing, and medical imaging. He has published about 170 journal papers and 610 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. He has also received best paper prizes at CAIP 2001, ACCV 2002, ICPR 2006, BMVC 2007 and ICIAP in 2009 and 2015. In 2009 he was awarded a Royal Society Wolfson Research Merit Award. In 1998, he became a fellow of the International Association for Pattern Recognition. He is also a fellow of the Institute of Physics, the Institute of Engineering and Technology, and the British Computer Society. In 2016 he became a fellow of the IEEE and was named Distinguished Fellow by the British Machine Vision Association. He is currently Editor-in-Chief of the journal Pattern Recognition, and was founding Editor-in-Chief of IET Computer Vision from 2006 until 2012. He has also been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Computer Vision and Image Understanding, Image and Vision Computing, and the International Journal of Complex Networks. He has been Conference Chair for BMVC in 1994 and Progrmme Chair in 2016, Track Chair for ICPR in 2004 and 2016 and Area Chair at ECCV 2006 and CVPR in 2008 and 2014, and in 1997 established the EMMCVPR workshop series. He has been a Governing Board Member of the IAPR since 2006, and is currently Vice President of the Association.
Editor-in-Chief Pattern Recognition
Area Editior Pattern Recognition, Associate Editor Journal of Complex Networks, Area Editor Computer Vision and Image Understanding.
Chair CAIP 2013, SIMBAD 2013, Area Chair CVPR 2014.
Member Royal Society Newton International Fellowships Panel
REF 2014 Panelist (output assessor) Subpanel 11 Computer Science and Informatics
Member University Research Committee, C-group (REF Strategy), Research Pump Priming Committe, Working Group on Research Performance Expectations.
Guest Editor: IEEE TNNLS, Pattern Recognition and Pattern Recognition Letters. IJCV.
Chair Computer Science Evaluation Commission, Czech Academy of Sciences and Arts.
Track Chair ICPR 2016, Co-Chair BMVC 2016.
Examples of recent talks:
From the Isaac Newton Institute in 2008.
From the Isaac Newton Institute in 2015.
My installation as Doctor Honoris Causa at the University of Alicante.
Rutherford Appleton-Laboratory (1981-1991) Research in high energy physics and pattern recognition
Open University (1992-1991) - Course tutor in Physics
University of Surrey (1989-1991) - visiting lecturer
University of York (1991-)
General areas: computer vision, statistical and structural pattern recognition, machine learning and complex networks.
3D shape recovery from 2D images
Physics of light reflectance from surfaces with complex structure and shape
Pattern analysis with non-Euclidean and non-numeric data, including structures (graphs, trees and strings), fields of vectors and tensors, and similarity data.
Complex networks, including analysis of directional graphs, evolving networks and analysis of network function.