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* [[Plenary_speakers#speaker1| Wednesday, July 6: Fredrik Gustafsson (Linköping University)]] | * [[Plenary_speakers#speaker1| Wednesday, July 6: Fredrik Gustafsson (Linköping University)]] | ||
* [[Plenary_speakers#speaker2| Thursday, July 7: Simon Godsill (University of Cambridge)]] | * [[Plenary_speakers#speaker2| Thursday, July 7: Simon Godsill (University of Cambridge)]] | ||
− | + | * [[Plenary_speakers#speaker3| Friday, July 8: Ba Tuong Vo (Curtin University)]] | |
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'''Simon Godsill''' is Professor of Statistical Signal Processing in the Engineering Department at Cambridge University. He is also a Professorial Fellow and tutor at Corpus Christi College Cambridge. He coordinates an active research group in Signal Inference and its Applications and is Head of the Signal Processing and Communications Laboratory at Cambridge. His group specialises in Bayesian computational methodology, multiple object tracking, audio and music processing, and financial time series modeling. A particular methodological theme over recent years has been the development of novel techniques for optimal Bayesian filtering and smoothing, using Sequential Monte Carlo or Particle Filtering methods. Prof. Godsill has published extensively in journals, books and international conference proceedings, and has given a number of high profile invited and plenary addresses at conferences such as the Valencia conference on Bayesian Statistics (twice), the IEEE Statistical Signal Processing Workshop, the Conference on Bayesian Inference for Stochastic Processes (BISP), the IEEE Workshop on Machine Learning in Signal Processing (2013) and FUSION (2016). He co-authored a Springer text Digital Audio Restoration with Prof. Peter Rayner in 1998. He was technical chair of the IEEE NSSPW workshop in 2006 on sequential and nonlinear filtering methods, and has been on the conference panel for numerous other conferences/workshops. Prof. Godsill has served as Associate Editor for IEEE Tr. Signal Processing and the journal Bayesian Analysis. He was Theme Leader in Tracking and Reasoning over Time for the UK’s Data and Information Fusion Defence Technology Centre (DIF-DTC) and Principal Investigator on many grants funded by the EU, EPSRC, QinetiQ, General Dynamics, MOD, Microsoft UK, Citibank, Mastercard, Google, DSO Singapore, Huawei and Jaguar Landrover. In 2009-10 he was co-organiser of an 18 month research program in Sequential Monte Carlo Methods at the SAMSI Institute in North Carolina and in 2014 he co-organised a research programme at the Isaac Newton Institute on Sequential Monte Carlo methods. In 2018 he will be General Chair of the FUSION Conference in Cambridge. Two of his journal papers have recently received Best Paper awards from the IEEE and IET. He continues to be a Director of CEDAR Audio Ltd. (which has received numerous accolades over the years, including a technical Oscar), and for which he was a founding staff member in 1988. The company has commercialised many of the ideas from Professor Godsill’s research over the years. | '''Simon Godsill''' is Professor of Statistical Signal Processing in the Engineering Department at Cambridge University. He is also a Professorial Fellow and tutor at Corpus Christi College Cambridge. He coordinates an active research group in Signal Inference and its Applications and is Head of the Signal Processing and Communications Laboratory at Cambridge. His group specialises in Bayesian computational methodology, multiple object tracking, audio and music processing, and financial time series modeling. A particular methodological theme over recent years has been the development of novel techniques for optimal Bayesian filtering and smoothing, using Sequential Monte Carlo or Particle Filtering methods. Prof. Godsill has published extensively in journals, books and international conference proceedings, and has given a number of high profile invited and plenary addresses at conferences such as the Valencia conference on Bayesian Statistics (twice), the IEEE Statistical Signal Processing Workshop, the Conference on Bayesian Inference for Stochastic Processes (BISP), the IEEE Workshop on Machine Learning in Signal Processing (2013) and FUSION (2016). He co-authored a Springer text Digital Audio Restoration with Prof. Peter Rayner in 1998. He was technical chair of the IEEE NSSPW workshop in 2006 on sequential and nonlinear filtering methods, and has been on the conference panel for numerous other conferences/workshops. Prof. Godsill has served as Associate Editor for IEEE Tr. Signal Processing and the journal Bayesian Analysis. He was Theme Leader in Tracking and Reasoning over Time for the UK’s Data and Information Fusion Defence Technology Centre (DIF-DTC) and Principal Investigator on many grants funded by the EU, EPSRC, QinetiQ, General Dynamics, MOD, Microsoft UK, Citibank, Mastercard, Google, DSO Singapore, Huawei and Jaguar Landrover. In 2009-10 he was co-organiser of an 18 month research program in Sequential Monte Carlo Methods at the SAMSI Institute in North Carolina and in 2014 he co-organised a research programme at the Isaac Newton Institute on Sequential Monte Carlo methods. In 2018 he will be General Chair of the FUSION Conference in Cambridge. Two of his journal papers have recently received Best Paper awards from the IEEE and IET. He continues to be a Director of CEDAR Audio Ltd. (which has received numerous accolades over the years, including a technical Oscar), and for which he was a founding staff member in 1988. The company has commercialised many of the ideas from Professor Godsill’s research over the years. | ||
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+ | {| id="mp-upper" style="width: 80%; margin:4px 0 0 0; background:none; border-spacing: 0px;" | ||
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+ | | class="MainPageBG" style="width:100%; border:1px solid #f36766; background:#f9d6c9; vertical-align:top; color:#000;" | | ||
+ | {| id="mp-left" style="width:100%; vertical-align:top; background:#f9d6c9;" | ||
+ | | style="padding:2px;" | <h2 id="mp-tfa-h2" style="margin:3px; background:#f5baa3; font-family:inherit; font-size:120%; font-weight:bold; border:1px solid #f36766; text-align:left; color:#000; padding:0.2em 0.4em;">Friday, July 8</h2> | ||
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+ | === <div align="center">Ba Tuong Vo, Curtin University</div> === | ||
+ | ==== <div align="center">Beyond the PHD Filter: Advances in Random Set Approaches</div> ==== | ||
+ | </div> | ||
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+ | Multi-object systems are complex dynamical systems in which the number of objects and their states are unknown and vary randomly with time. Multi-object systems arise in many application areas, including defence, computer vision, field robotics, biomedical research and machine learning. Indeed multi-object systems are ubiquitous in nature. Pioneered by Mahler almost two decades ago, the introduction of the random set or Finite Set Statistics approach to estimation and control for multi-object systems has attracted substantial interest from academia and industry alike. Though the Probability Hypothesis Density (PHD) filter has since become synonymous with the random set paradigm, the last decade has witnessed many new exciting developments beyond the PHD filter. This seminar presents an overview of the random set approach to multi-object systems and outlines recent theoretical and computational developments as well as new applications such as sensor control, visual surveillance, simultaneous localization and mapping, and autonomous driving. | ||
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+ | [[File:Btv.jpg|200px|frameless|left|link=]] | ||
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+ | '''Ba Tuong Vo''' is currently an Associate Professor with the Department of Electrical and Computer Engineering at Curtin University in Perth, Australia. He obtained Bachelor degrees in Science majoring in Applied Mathematics, Engineering majoring in Electrical and Electronic Engineering, and PhD with distinction all from The University of Western Australia. He has active research interests in multi-target tracking, Bayesian estimation, and statistical signal processing, and is currently an associate editor for IEEE Signal Processing Letters. He is also a recipient of an Australian Research Council Fellowship and a co recipient the 2010 DSTO Australian Museum Eureka Prize for "Outstanding Contributions in Support of Defense or National Security". He is best known for his works on random finite set algorithms for multi-sensor multi-target tracking. | ||
+ | </div> | ||
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Revision as of 12:16, 2 March 2016
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