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Introduction of New Associate Editors

David J.

Pages: 913-914

It is with great pleasure that we welcome Fredrik Kahl, Michael Lindenbaum, Yi Ma, Aleix Martinez, Jianbo Shi, and Alexander J. Smola as TPAMI Associate Editors. Their addition greatly enhances the depth of our editorial board in computer vision and machine learning. They help to replace Irfan Essa and Benjamin B. Kimia, who now have retired from TPAMI's AE Board and who we thank for their hard work.

Professor Fredrik Kahl will handle papers in multiple view geometry, structure from motion, calibration, optimization methods, photometric stereo, and image-based modeling. Professor Michael Lindenbaum will handle the review process for papers in perceptual organization and segmentation, attention, performance prediction in computer vision, low-level vision, statistical methods, and learning in computer vision. Professor Yi Ma will be considering manuscripts in multiple-view geometry, motion analysis, and segmentation and grouping. Professor Aleix Martinez will oversee papers in pattern recognition and machine learning, face recognition, statistical methods, biologically inspired vision, analysis and recognition of sign languages, and object recognition. Professor Jianbo Shi will be reponsible for the areas of image segmentation, video activity recognition, human recognition, image search, and biometrics. Professor Alexander J. Smola will provide support for papers in kernel methods, optimization, graphical models, learning theory, and nonparametric statistics. Their brief biographies appear below.

Welcome aboard. We look forward to working with you.

David J. Kriegman, Editor-in-Chief

David Fleet, Associate Editor-in-Chief

About the Authors

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Fredrik Kahl received the MSc degree in computer science and technology in 1995 and the PhD degree in mathematics in 2001. His thesis was awarded the Best Nordic Thesis Award in pattern recognition and image analysis 2001-2002 at the Scandinavian Conference on Image Analysis 2003. He was a postdoctoral research fellow at the Australian National University in 2003-2004 and at the University of California, San Diego in 2004-2005. He is currently an associate professor at the Centre for Mathematical Sciences, Lund University, Sweden. His primary research areas include geometric computer vision problems and optimization methods for both continuous and discrete domains. In 2005, together with Didier Henrion, he received the ICCV 2005 Marr Prize for work on global optimization methods applied to geometric reconstruction problems.
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Michael Lindenbaum received the BSc, MSc, and DSc degrees from the Department of Electrical Engineering at the Technion, Israel, in 1978, 1987, and 1990, respectively. From 1978 to 1985, he served in the Israeli Defense Forces. He did his postdoctoral research at the NTT Basic Research Labs in Tokyo, Japan, and, since 1991, he has been with the Department of Computer Science, Technion. He was also a consultant to HP Labs, Israel, and spent a sabatical at NEC Research Institute, Princeton, New Jersey, in 2001. He served on several committees of computer vision conferences, coorganized the IEEE Workshop on Perceptual Organization in Computer Vision, and was an associate editor of Pattern Recognition and Pattern Recognition Letters. He has worked in digital geometry, computational robotics, learning, and various aspects of computer vision and image processing. Currently, his main research interest is computer vision and, especially, statistical analysis of object recognition and grouping processes.
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Yi Ma received two bachelors degrees in automation and applied mathematics from Tsinghua University, Beijing, China in 1995. He received the MS degree in electrical engineering and computer science in 1997, the MA degree in mathematics in 2000, and the PhD degree in electrical engineering and computer science, in 2000 all from the University of California, Berkeley. Since 2000, he has been on the faculty of the Electrical and Computer Engineering Department of the University of Illinois, Urbana-Champaign, where he now holds the rank of associate professor. His main research areas are in systems theory and computer vision. Professor Ma was the recipient of the David Marr Best Paper Prize at the International Conference on Computer Vision in 1999 and honorable mention for the Longuet-Higgins Best Paper Award at the European Conference on Computer Vision in 2004. He received the CAREER Award from the US National Science Foundation in 2004 and the Young Investigator Program Award from the US Office of Naval Research in 2005. He is a senior member of the IEEE and a member of the ACM.
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Aleix Martinez received the PhD degree from Universitat Autónoma de Barcelona and Université de Paris, under an EU program, in 1998. His original work was on robotics and artificial intelligence, but, over the years, he has become interested in computer vision and learning. He furthered his computer vision and pattern recognition studies at Purdue University, where he was a postdoctoral student in 1998. From 1999 to 2000, he was with the Sony Computer Science Lab in Paris. He then returned to Purdue to extend his expertise in cognitive science, especially in linguistics. In 2002, he moved to The Ohio State University as an assistant professor of electrical and computer engineering. He is also affiliated with the Department of Biomedical Engineering and to the Center for Cognitive Science and is the founder and director of the Computational Biology and Cognitive Science Lab. His most cited works are in pattern recognition and face recognition and he is well-known for his AR face database. His current areas of interest are learning, vision, linguistics, and their interactions.
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Jianbo Shi studied computer science and mathematics as an undergraduate at Cornell University where he received the BA degree in 1994. He received the PhD degree in computer science from the University of California at Berkeley in 1998 for his thesis on normalize cuts image segmentation algorithm. He joined The Robotics Institute at Carnegie Mellon University in 1999 as a research faculty, where he led the Human Identification at Distance (HumanID) project, developing vision techniques for human identification and activity inference. In January 2003, he joined the Department of Computer and Information Science at the University of Pennsylvania as an assistant professor. In 2004, he received a US National Science Foundation CAREER award on learning to see--a unified segmentation and recognition approach. His current research focus is on object recognition-segmentation, mid-level shape representation, and human behavior analysis in video.
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Alexander J. Smola received the PhD degree from the University of Technology, Berlin in 1998. He is a professor at the Australian National University and program leader of the Statistical Machine Learning Program at National ICT, Australia. From 1999 to 2001, he was a postdoctoral fellow at the Australian National University, working on learning theory. In 2002, he became leader of the Machine Learning Group at the Research School of Information Sciences and Engineering. He has been in charge of machine learning research at NICTA since 2004. He has authored the book Learning with Kernels with Professor Bernhard Schoelkopf. He has edited three books and a fourth one is forthcoming. He initiated and coorganized the Machine Learning Summer Schools. He is a member of the editorial board of the Journal of Machine Learning Research, Statistics, and Computing and the IEEE Transactions on Neural Networks. He served on the program committees of COLT, NIPS, and ICML several times. He has written more than 100 publications and organized several workshops at NIPS and ICML. His interests are the connections between graphical models, kernels, and nonparametric statistics. He has worked on efficient optimization methods for kernel methods, novel inference algorithms, such as kernel-PCA, single-class SVM, or automatic parameter adjustment methods for support vector regression. His recent interests include Hilbert space methods for the analysis of distributions, e.g., to derive efficient contrast functions for independent component analysis, feature extraction, or two-sample tests.
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