Issue No. 02 - February (2008 vol. 30)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.14
To support the continuing increase in submission arising from the popularity of TPAMI with authors, we are pleased to announce that Professor Zoubin Ghahramani will be joining David Fleet as an Associate Editor-in-Chief (AEIC) of TPAMI. He will help to maintain the high quality that readers expect and the timeliness and thoroughness of reviewing that authors' demand. The AEIC works hand-in-hand with the Editor-in-Chief in all aspects of TPAMI's editorial review process, including establishing policies, selecting special issues, selecting editors, helping the flow of papers through the review process, handling appeals, etc. Dr. Ghahramani is a leader in the field of machine learning, an area of increasing importance to TPAMI, for both the quality of his research and his service to the community.
We are also happy to announce that the TPAMI editorial board is expanding with the addition of four new Associate Editors, Dr. Sing Bing Kang, Professor Kevin Murphy. Dr. Salil Prabhakar, and Professor Dale Schuurmans. Dr. Kang will handle papers on image-based rendering, vision for graphics, image and video processing/enhancement, and stereopsis and structure from motion. Professor Murphy will oversee papers about graphical models, structure learning, causal inference, unsupervised learning, and Bayesian methods. Dr. Prabhakar will oversee the review process of papers in all aspects of biometric systems and theoretical and empirical evaluation of computer vision algorithms. Professor Schuurmans' expertise includes machine learning techniques (support vector machines, kernel methods, clustering, dimensionality reduction), graphical models, optimization, and Monte Carlo methods, and he has begun to handle papers in these areas.
Their brief biographies are below. Welcome to TPAMI's editorial board!
David J. Kriegman, Editor-in-Chief
David Fleet, Associate Editor-in-Chief
Zoubin Ghahramani received the BA and BSE degrees from University of Pennsylvania and the PhD degree in 1995 from the Massachusetts Institute of Technology (MIT), working with Professor Mike Jordan. He did postdoctoral research in computer science at the University of Toronto, working with Professor Geoff Hinton, and was a faculty member in the Gatsby Unit at University College London from 1998 to 2005. He is a professor of information engineering at the University of Cambridge, United Kingdom, and he is also an associate research professor of machine learning at Carnegie Mellon University. His work has included research on human sensorimotor control, cognitive science, statistics, and machine learning. His current focus is on Bayesian approaches to statistical machine learning, nonparametric methods, graphical models, and approximate inference. He is also actively working on applications of machine learning to bioinformatics and information retrieval. He has published more 100 peer reviewed papers and serves on the editorial boards of several leading journals in the field, including the Journal of Machine Learning Research, Annals of Statistics, Journal of Artifical Intelligence Research, Machine Learning, Foundations and Trends in Machine Learning, and Bayesian Analysis. He also serves on the board of the International Machine Learning Society and was program chair of the 2007 International Conference on Machine Learning.
Sing Bing Kang received the BEng and MEng degrees in electrical engineering from the National University of Singapore in 1987 and 1990, respectively. He subsequently received the MSc and PhD degrees in robotics from Carnegie Mellon University in 1992 and 1994, respectively. He is currently a senior researcher at Microsoft Corporation working on environment modeling from images as well as image and video enhancement. He has coedited two books in computer vision ( Panoramic Vision and Emerging Topics in Computer Vision), and recently coauthored a book on image-based rendering. He has also served as an area chair and a member of technical committee for ICCV, CVPR, and ECCV and is currently program cochair for ACCV 2007. He will be program cochair for CVPR 2009.
Kevin Murphy received the BSc degree from the University of Cambridge, the MSc degree from the University of Pennsylvania, the PhD degree from the University of California, Berkeley, and did postdoctoral research at the Massachusetts Institiute of Technology (MIT). He has been an assistant professor at the University of British Columbia (UBC), Vancouver, Canada in the Departments of Computer Science and Statistics since September 2004. He holds a Canada research chair in machine learning/computational statistics. He develops and applies Bayesian inference techniques to problems in computational biology and computer vision. He is best known for his work in the area of Bayesian networks/graphical models. His current focus is on learning network topology from small sample size, high-dimensional data sets.
Salil Prabhakar received the BTech degree in computer science and engineering from the Institute of Technology, Banaras Hindu University, Varanasi, India, in 1996. After working for IBM India for a year, he joined the Department of Computer Science and Engineering at Michigan State University, East Lansing, where he completed the PhD degree in 2001. Since 2001, he has been the chief scientist at DigitalPersona, Inc., a leading provider of fingerprint-based biometric solutions. Dr. Prabhakar's research interests include pattern recognition, image processing, computer vision, machine learning, biometrics, data mining, and multimedia applications. He is coauthor of more than 30 technical publications and holds two patents. He coauthored the Handbook of Fingerprint Recognition (Springer, 2003), which received the PSP award from the Association of American Publishers. He is an associate editor of the Pattern Recognition Journal and was one of the guest editors of the April 2007 special issue of IEEE TPAMI on "biometrics: progress and directions." He is a senior member of the IEEE.
Dale Schuurmans received the BSc and MSc degrees in computing science and mathematics from the University of Alberta and the PhD degree in computer science from the University of Toronto. He is a professor of computing science and Canada Research Chair in Machine Learning at the University of Alberta. He has previously been an associate and an assistant professor at the University of Waterloo, a postdoctoral fellow at the University of Pennsylvania, a researcher at the NEC Research Institute, and a research associate at the National Research Council, Canada. Professor Schuurmans has served as an action editor for the Journal of Machine Learning Research, Artificial Intelligence and Machine Learning. He has also served on the editorial board of the Journal of Artificial Intelligence Research and, in 2004, he was program cochair for the International Conference on Machine Learning (ICML). Professor Schuurmans' research interests include machine learning, optimization, and search. He is the author of approximately 100 publications in these areas and has received outstanding paper awards from the International Joint Conference on Artificial Intelligence (IJCAI), the National Conference on Artificial Intelligence (AAAI), and the IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.
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