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School of Electronic Engineering and Computer Science

Professor Andrea Cavallaro

Andrea

Director of Centre for Intelligent Sensing & Professor in Multimedia Signal Processing

Email: a.cavallaro@qmul.ac.uk
Telephone: +44 20 7882 5165
Room Number: Peter Landin, CS 436
Website: http://www.eecs.qmul.ac.uk/~andrea
Office Hours: Monday 15:00-17:00

Profile

Andrea Cavallaro is Turing Fellow at The Alan Turing Institute, the UK National Institute for Data Science and Artificial Intelligence, and Full Professor at Queen Mary University of London, a Russell Group university. He is the founding Director of the Centre for Intelligent Sensing and the Director of Research of the School of Electronic Engineering and Computer Science. He is a Fellow of the International Association for Pattern Recognition (IAPR) for “contributions to image processing and multi-sensor scene understanding,” and a Fellow of the Higher Education Academy. He serves as Editor-in-Chief of Signal Processing: Image Communication, as Senior Area Editor for the IEEE Transactions on Image Processing, as member of the IEEE Video Signal Processing and Communication Technical Committee and as member of the Technical Directions Board of the IEEE Signal Processing Society.

Professor Cavallaro received his Ph.D. in Electrical Engineering from the École polytechnique fédérale de Lausanne (EPFL) in 2002. He was a Research Fellow with British Telecommunications (BT) in 2004 and was awarded the Royal Academy of Engineering Teaching Prize in 2007; three student paper awards on target tracking and perceptually sensitive coding at IEEE ICASSP in 2005, 2007 and 2009; and the best paper award at IEEE AVSS 2009. He was selected as IEEE Signal Processing Society Distinguished Lecturer (2020-2021) and is the past Chair of the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee (2020-2021). He also served as elected member of the IEEE Multimedia Signal Processing Technical Committee and chair of the Awards committee of the IEEE Signal Processing Society, Image, Video, and Multidimensional Signal Processing Technical Committee. He served as Area Editor for the IEEE Signal Processing Magazine (2012-2014); and as Associate Editor for the IEEE Transactions on Image Processing (2011-2015), IEEE Transactions on Signal Processing (2009-2011), IEEE Transactions on Multimedia (2009-2010), IEEE Signal Processing Magazine (2008-2011) and IEEE Multimedia (2016-2018). He also served as Guest Editor the IEEE Transactions on Multimedia (2019), IEEE Transactions on Circuits and Systems for Video Technology (2017, 2011), Pattern Recognition Letters (2016), IEEE Transactions on Information Forensics and Security (2013), International Journal of Computer Vision (2011), IEEE Signal Processing Magazine (2010), Computer Vision and Image Understanding (2010), Annals of the British Machine Vision Association (2010), Journal of Image and Video Processing (2010, 2008), and Journal on Signal, Image and Video Processing (2007).

Professor Cavallaro has published over 270 journal and conference papers, one monograph on Video tracking (2011, Wiley) and three edited books: Multi-camera networks (2009, Elsevier); Analysis, retrieval and delivery of multimedia content (2012, Springer); and Intelligent multimedia surveillance (2013, Springer).

Teaching

Introduction to Computer Vision (Postgraduate/Undergraduate)

In recent years, research in computer vision has made significant progress. This is largely driven by the recognition that effective visual perception is crucial in understanding intelligent behaviour - unless we understand how we perceive, we will never understand how we reason The first part of the module will introduce the relevant concepts and techniques in machine learning. In the second part we will show how these techniques can be applied to various areas in computer vision.

Research

Research Interests:

Professor Cavallaro has an established track record in building bridges between disciplines, such as linking signal processing and wireless networks for distributed tracking, machine learning and image processing for scene analysis, and audio-visual sensing and robotics for autonomous, collaborative systems. His research interests include machine learning for multimodal perception, computer vision, machine listening, and information privacy. 

Publications

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