CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.06 - June
Issue No.06 - June (2013 vol.35)
Published by the IEEE Computer Society
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.79
Two factors make TPAMI a wonderful journal. Our community is a fertile source of exciting intellectual creations and scientific discoveries, and this factor ensures there are fine papers for the journal to publish. In this editorial, I want to acknowledge the other factor—the large community of volunteers who find and promote strong papers.
Referees are scholars who sacrifice time from their scholarship to evaluate the work of others. Authors consistently undervalue the work of referees. It’s easy to feel that a referee who doesn’t like a paper is wrong, and one who does like a paper doesn’t like it enough. When you are cross with a referee, remember this: The reason you want your paper to get into TPAMI is that the papers accepted by TPAMI’s referees are very good indeed. Space does not allow naming every referee. TPAMI does not have a mechanism to identify exceptional referees�(I like to feel that all our referees are above average) and so I and the editorial board would like acknowledge�the work of every referee for this journal. You know who you are: thank you for your efforts, without which this journal would disappear.
Associate Editors are scholars who sacrifice time from their scholarship to ensure that papers are sent to the right referee, and to produce a recommendation that is�consistent with the reviews and with TPAMI’s rules and practices. Authors tend to undervalue the work of AE’s, too; and again, when you are cross with an AE, remember that you submitted to TPAMI because TPAMI’s Associate Editors are very good indeed. TPAMI has a large pool of Associate Editors, allowing the journal to handle a very large number of submissions. I cannot name each Associate Editor here, but you will see their names listed elsewhere in each copy of the journal.
It is a pleasure to welcome a distinguished group of new Associate Editors to our editorial board. The board will now include: Hiroshi Ishikawa, Rong Jin, Hugo Larochelle, Fei-Fei Li, Yael Moses, Tobias Scheffer, Charles Sutton, Ben Taskar, Huan Xu, and Jieping Ye.�We try not to overload Associate Editors, and so their terms are limited. Horst Bischof, Ravi Ramamoorthi, Pedro Felzenszwalb, Arthur Gretton, Ales Leonardis, and Charles Stewart are leaving the editorial board. I thank the Associate Editors for their efforts, without which this journal would disappear.
Associate Editors in Chief are scholars who sacrifice time from their scholarship to ensure papers reach the right Associate Editor. Authors tend not to be aware of their work. Associate Editors in Chief make a huge contribution to our journal by steering papers. Much of the effectiveness of our reviewing process depends on how well they understand which of our colleagues knows what aspect of our discipline. We welcome a new Associate Editor in Chief, Stan Sclaroff, to the editorial board. I thank the Associate Editors in Chief for their efforts, without which this journal would disappear.
David A. Forsyth
Editor in Chief
Stan Sclaroff received the bachelor’s degree in computer science and English from Tufts University in 1984 and the master’s and PhD degrees from the MIT Media Lab in 1991 and 1995, respectively. He is currently a professor of computer science and the chair of the Department of Computer Science at Boston University. He is founder and co-head of the Image and Video Computing research group at Boston University. His current research interests include object tracking and recognition, analysis of human activity and gesture, and image/video database indexing, retrieval, and data mining methods. In 1996, he received a US Office of Naval Research Young Investigator Award and a US National Science Foundation Faculty Early Career Development Award. He has coauthored more than 150 scholarly publications in the areas of tracking, video-based analysis of human motion and gesture, surveillance, deformable shape matching, and recognition, as well as image/video database indexing, retrieval, and data mining methods. He has served on the technical program committees of more than 100 computer vision conferences and workshops. He served as an Associate Editor for the IEEE Transactions on Pattern Analysis, 2000-2004, and 2006-2011. He is a senior member of the IEEE.
Hiroshi Ishikawa received the BS and MS degrees in mathematics from Kyoto University in 1991 and 1993, respectively, and the PhD degree in computer science from New York University, with the Harold Grad Memorial Prize. After working at Nagoya City University as a faculty member, he moved to the Department of Computer Science and Engineering at Waseda University, Tokyo, in 2010, where he is now a full professor. Currently, he is also a Sakigake “PRESTO” Researcher in the JST Mathematics Program at the Japan Science and Technology Agency. He has also received the IEEE Computer Society Japan Chapter Young Author Award in 2006 and the MIRU Nagao Award in 2009. He is a member of the IEEE.
Rong Jin received the PhD degree from Carnegie Mellon University in 2003. He is an associate professor in the Computer and Science Engineering Department at Michigan State University. His research is focused on statistical machine learning and its application to large-scale information management, including web document retrieval, collaborative filtering, image and video analysis, visual object recognition, bioinformatics, and neuron data analysis. His research work involves a wide range of machine learning problems, including supervised learning, kernel learning, clustering, semi-supervised learning, active learning, transfer learning, and crowd sourcing-based learning. He has extensive research experience with a variety of machine learning algorithms, including Bayesian models, conditional random fields, support vector machines, and boosting. He received the US National Science Foundation Career award in 2006, and a best student paper award from COLT in 2012. He is currently an associate editor of the ACM Transactions on Knowledge Discovery from Data.
Hugo Larochelle received the PhD degree from the Université de Montréal, under the supervision of Yoshua Bengio. He is an assistant professor at the Université de Sherbrooke. Before joining the Computer Science Department of the Université de Sherbrooke in 2011, he spent two years in the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. His research includes the development of deep and probabilistic neural networks for supervised, semi-supervised, and unsupervised learning, with a particular focus toward high-dimensional and structured data such as image and natural language data.
Fei-Fei Li graduated from Princeton University in 1999 with a physics degree. She received the PhD degree in electrical engineering from the California Institute of Technology in 2005. She is an associate professor in the Computer Science Department at Stanford University. Her main research interest is in computer vision, machine learning, as well as cognitive and computational neuroscience. In computer vision, her work focuses on making sense of and telling stories of the visual world, including the recognition of the objects, scenes, and activities, both in static images and in videos. In human vision, she and her students use a combination of behavioral, psychophysical, and neuroimaging experiments as well as statistical pattern analysis methods to investigate the fundamental problems of visual recognition, visual attention and the neuro-mechanisms underlying behaviors. From 2005 to August 2012, she was an assistant professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign, the Computer Science Department at Princeton University, and the Computer Science Department at Stanford University. Her work has appeared in top journals and conferences such as Nature, Proceedings of the National Academy of Sciences, Journal of Neuroscience, IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, CVPR, ICCV, ECCV, NIPS, ICML, etc. She has served as an area chair for numerous first tier conferences, and has chaired or coorganized a number of tutorials, workshops, summer schools, and international challenges. She is a recipient of a Microsoft Research New Faculty award, the Alfred Sloan Fellowship, a number of Google Research Awards, a US National Science Foundation CAREER award, and an IEEE CVPR 2010 Best Paper Honorable Mention. She and her students have been winners of a number of international visual computing competitions (AAAI-SVRC 2007, PASCAL VOC 2011, 2012). She publishes using the name L. Fei-Fei.
Yael Moses received the MSc and PhD degrees in computer science from the Weizmann Institute in 1986 and 1994, respectively. She is currently a senior lecturer in the Efi Arazi School of Computer Science at the Interdisciplinary Center (IDC), Herzliya. She is a member of the IEEE. She is a founder of IDC’s KerenOr program, which makes higher education accessible to students from underprivileged areas. She was a postdoctoral fellow in the Engineering Department at Oxford University from 1993 to 1994 and at the Weizmann Institute of Science from 1994 to 1998. She spent a sabbatical year at the University of New South Wales and the National ICT Australia, Sydney. Her early work concentrated on theoretical aspects of object recognition.
Tobias Scheffer received the doctoral degree in 1999 from the Technische Universität Berlin. He is a professor of computer science at the University of Potsdam. His research addresses fundamental topics in machine learning (currently adversarial learning, active model evaluation, and transfer learning problems), and some of the many applications of machine learning, for instance, in the areas of computer security (spam, botnets, network attacks), and information retrieval. From 2007 to 2008, he was the head of the Machine Learning Group at the Max Planck Institute of Computer Science in Saarbrücken. Between 2003 and 2006, he was an assistant professor at Humboldt-Universität zu Berlin.
Charles Sutton received the PhD degree in computer science in 2008 from the University of Massachusetts Amherst, working with Andrew McCallum. He has been a lecturer (equivalent to a US assistant professor) in the School of Informatics at the University of Edinburgh since 2009. From 2007-2009, he was a postdoctoral researcher at the University of California, Berkeley, under the supervision of Michael I. Jordan. He has published more than 30 papers in machine learning, graphical models, natural language processing, and the application of machine learning methods to computer systems, which have accumulated over 1,500 citations. He has reviewed for all the major conferences and journals in machine learning and NLP, and he has served as an area chair for AISTATS and ECML.
Ben Taskar received the bachelor’s and doctoral degrees in computer science from Stanford University. After working as a postdoctoral researcher at the University of California, Berkeley, he joined the faculty at the University of Pennsylvania Computer and Information Science Department in 2007, where he co-directed PRiML: Penn Research in Machine Learning. In the spring of 2013, Ben joined the Computer Science and Engineering Department at the University of Washington. His research interests include machine learning, computer vision, and natural language processing. He has been awarded the Sloan Research Fellowship, the US National Science Foundation CAREER Award, and was selected for the Young Investigator Program by the US Office of Naval Research and the US Defense Advanced Research Projects Agency’s Computer Science Study Group. His work on structured prediction received best paper awards at the NIPS and EMNLP conferences.
Huan Xu received the BEng degree in automation from Shanghai Jiaotong University, Shanghai, China, in 1997, the MEng degree in electrical engineering from the National University of Singapore in 2003, and the PhD degree in electrical engineering from McGill University, Canada, in 2009. From 2009 to 2010, he was a postdoctoral associate at The University of Texas at Austin. He has been an assistant professor in the Department of Mechanical Engineering at the National University of Singapore since 2011. His main research interests lie in machine learning and optimization, in particular in learning and decision making under uncertainty, and sparse and low-rank representation.
Jieping Ye received the PhD degree in computer science from the University of Minnesota, Twin Cities, in 2005. He is currently an associate professor of computer science and engineering at Arizona State University (ASU). He is also a core faculty member of the Center for Evolutionary Medicine and Informatics of the Bio-design Institute at ASU. His research interests include machine learning, data mining, and biomedical informatics. He won the outstanding student paper award at the International Conference on Machine Learning in 2004 and the US National Science Foundation CAREER Award in 2010. He is a senior member of the IEEE.
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