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Editor’s Note

Sing Bing

Pages: pp. 209-210

Several AEs have reached the end of their terms, and we also have a number of new additions. I wish to thank Serge Belongie, Antonio Criminisi, Salil Prabhakar, Dale Schuurmans, and Yoram Singer for their service to the journal, which is greatly appreciated by all of us.

It is my pleasure to announce that Mikhail Belkin, Miguel Á. Carreira-Perpiñán, Yasuyuki Matsushita, Sudeep Sarkar, Amos Storkey, and Yee Whye Teh have agreed to join the editorial board. They will be handling a broad range of papers, as usual, but Professors Belkin, Carreira-Perpiñán, Storkey, and Teh will be focusing in machine learning, while Dr. Matsushita and Professor Sarkar will be handling papers in computer vision.

Brief biographies of these distinguished additions to our masthead appear below. Welcome aboard, and thank you in advance for all of your hard work!

Ramin Zabih, Editor-in-Chief

Sing Bing Kang, Associate Editor-in-Chief

Neil Lawrence, Associate Editor-in-Chief

Jiri Matas, Associate Editor-in-Chief

Max Welling, Associate Editor-in-Chief

About the Authors

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Mikhail Belkin received the PhD degree from the Department of Mathematics at the University of Chicago in 2003. He is currently an associate professor in the Department of Computer Science and the Department of Statistics at Ohio State University. His research focuses on designing and analyzing algorithms for machine learning based on nonlinear structure of high dimensional data, particularly using manifold and spectral methods, and on understanding their theoretical limits. He is also interested in connections between machine learning and human cognition. He has been an organizer for a number of events in Machine Learning, including the 2009 Machine Learning Summer School/Workshop on Theory and Practice of Computational Learning in Chicago.
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Miguel Á. Carreira-Perpiñán received the degree of “licenciado en informática” (MSc degree in computer science) from the Technical University of Madrid in 1995 and the PhD degree in computer science from the University of Sheffield in 2001. He is an associate professor in electrical engineering and computer science at the University of California, Merced. Prior to joining UC Merced, he did postdoctoral work at Georgetown University (in computational neuroscience) and the University of Toronto (in machine learning), and was an assistant professor at the Oregon Graduate Institute (Oregon Health & Science University). He is the recipient of a US National Science Foundation CAREER award and a best student paper award at Interspeech. His research interests lie in machine learning, in particular unsupervised learning problems such as dimensionality reduction, clustering, and denoising, with an emphasis on optimization aspects, and with applications to speech processing (e.g. articulatory inversion and model adaptation), computer vision, sensor networks, and other areas.
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Yasuyuki Matsushita received the BS, MS, and PhD degrees in electrical engineering and computer science from the University of Tokyo in 1998, 2000, and 2003, respectively. He joined Microsoft Research Asia in April 2003. He is currently a Lead Researcher in the Visual Computing Group. His areas of research are computer vision (photometric techniques, such as radiometric calibration, photometric stereo, shape-from-shading) and computer graphics (image relighting, video analysis, and synthesis). Dr. Matsushita served as an area chair for the IEEE Computer Vision and Pattern Recognition (CVPR) 2009 and International Conference on Computer Vision (ICCV) 2009, and program cochair for PSIVT 2010 and 3DIMPVT 2011. He is an editorial board member of the International Journal of Computer Vision (IJCV), IPSJ Journal of Computer Vision and Applications (CVA), The Visual Computer Journal, and the Encyclopedia of Computer Vision. He is serving as a program cochair of ACCV 2012, cemo cochair for ICCV 2011, and area chair for ECCV 2012. He is an appointed Guest Associate Professor at Osaka University (April 2010-) and the National Institute of Informatics, Japan (April 2011-). He is a senior member of IEEE.
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Sudeep Sarkar received the BTech degree in electrical engineering from the Indian Institute of Technology, Kanpur, in 1988. He received the MS and PhD degrees in electrical engineering, on a University Presidential Fellowship, from The Ohio State University, Columbus, in 1990 and 1993, respectively. Since 1993, he has been with the Computer Science and Engineering Department at the University of South Florida (USF), Tampa, where he is currently a professor and a Research Administration Faculty Fellow in the Office of Research and Innovation. He was a recipient of the US National Science Foundation CAREER award in 1994, the USF Teaching Incentive Program Award for undergraduate teaching excellence in 1997, the Outstanding Undergraduate Teaching Award in 1998, and the Ashford Distinguished Scholar Award in 2004. He is a fellow of the International Association for Pattern Recognition and is an IEEE Computer Society Distinguished Visitor Program Speaker, 2010-2012. His current research interests include automated sign language recognition, biometrics, nano-computing, and perceptual organization in images. He has more than 15 years of experience in these areas. He has graduated 14 PhD students and 22 MS students. He has published more than 50 journal papers and 100 conference/workshop papers. He is the coauthor of the book Computing Perceptual Organization in Computer Vision (World Scientific). He is also the coeditor of the book Perceptual Organization for Artificial Vision Systems (Kluwer). He is currently the co-Editor-in-Chief for Pattern Recognition Letters and Associate Editor for IET Computer Vision. He has served on the editorial boards for the IEEE Transactions on Pattern Analysis and Machine Intelligence (1999-2003), Pattern Analysis and Applications Journal, Pattern Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Part-B, and Image and Vision Computing. He has served as area chair for ICCV, CVPR, and ICPR.
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Amos Storkey is a lecturer in Machine Learning at the School of Informatics, University of Edinburgh. His degree is in mathematics from Trinity College Cambridge, with Part III Maths focusing on theoretical physics. The subject of his doctorate at Imperial College, London was neural networks and Gaussian process models. Prior to his lectureship, while also in Edinburgh, he held a research fellowship from Microsoft Research. He has extensive publications in Machine Learning, including in the areas of Machine Learning Markets, continuous time models, image processing and modeling, and data integration in dynamic environments, with applications in areas of medical imaging, astronomy, and genetic epidemiology, among others.
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Yee Whye Teh received the PhD degree in computer science from the University of Toronto in 2003. He is a reader (associate professor equivalent) at the Gatsby Computational Neuroscience Unit, University College London. He is interested in Bayesian and statistical machine learning. His current focus is on developing Bayesian nonparametric methodologies for unsupervised learning, computational linguistics, and genetics. Prior to his appointment, he was the Lee Kuan Yew Postdoctoral Fellow at the National University of Singapore and a postdoctoral fellow at the University of California, Berkeley. He was program cochair of AISTATS 2010 and serves as an associate editor for the Journal of the Royal Statistical Society B, Machine Learning Journal, and Bayesian Analysis.
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