Ivor Tsang
Professor
Centre for Artificial Intelligence
University of Technology Sydney
Australia
Biography
Ivor W Tsang is an ARC Future Fellow and Professor of Artificial Intelligence, at University of Technology Sydney (UTS). He is also the Research Director of the UTS Priority Research Centre for Artificial Intelligence (CAI), which is the evolution of the UTS Flagship Research Centre for Quantum Computation & Intelligent Systems (QCIS). His research focuses on transfer learning, feature selection, big data analytics for data with trillions of dimensions, and their applications to computer vision and pattern recognition. He has more than 140 research papers published in top-tier journal and conference papers. According to Google Scholar, his H-index is 45. In 2009, Prof Tsang was conferred the 2008 Natural Science Award (Class II) by Ministry of Education, China, which recognized his contributions to kernel methods. In 2013, Prof Tsang received his prestigious Australian Research Council Future Fellowship for his research regarding Machine Learning on Big Data. In addition, he had received the prestigious IEEE Transactions on Neural Networks Outstanding 2004 Paper Award in 2007, the 2014 IEEE Transactions on Multimedia Prize Paper Award, and a number of best paper awards and honors from reputable international conferences, including the Best Student Paper Award at CVPR 2010, and the Best Paper Award at ICTAI 2011.
Research Interest
Big Data Analytics and Scalable Machine Learning Algorithms, Feature Selection for Big Dimensionality, Transfer Learning and Domain Adaptation, Learning from Ambiguity
Publications
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Feng L, Ong YS, Lim MH, Tsang IW. Memetic search with interdomain learning: A realization between CVRP and CARP. IEEE Transactions on Evolutionary Computation. 2015 Oct;19(5):644-58.r
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Yan Y, Tan M, Tsang IW, Yang Y, Zhang C, Shi Q. Scalable Maximum Margin Matrix Factorization by Active Riemannian Subspace Search. InIJCAI 2015 Jul 25 (pp. 3988-3994).
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Xu Z, Tsang IW, Yang Y, Ma Z, Hauptmann AG. Event detection using multi-level relevance labels and multiple features. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2014 (pp. 97-104).
