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Editor’s Note

Sing Bing

Pages: pp. 2337-2338

It is our pleasure to announce that Inderjit Dhillon, Maja Pantic, and Eric Xing have agreed to join the editorial board. They will be handling a broad range of papers, as usual, but their emphasis will be as follows: Professor Dhillon, clustering and machine learning; Professor Pantic, tracking; and Professor Xing, machine learning.

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

Jiri Matas, Associate Editor-in-Chief

Max Welling, Associate Editor-in-Chief

About the Authors

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Inderjit Dhillon received the BTech degree from the Indian Institute of Technology at Bombay and the PhD degree from the University of California at Berkeley. He is a professor of computer science at The University of Texas at Austin. His main research interests are in data mining, machine learning, network analysis, and scientific computing. He is best known for his work on computational algorithms in these areas, in particular on eigenvalue/eigenvector computations, clustering, co-clustering, matrix approximations, dimensionality reduction, and metric/kernel learning. Software based on his research on eigenvalue computations is now part of all state-of-the-art numerical software libraries. He received a US National Science Foundation Career Award in 2001, a University Research Excellence Award in 2005, the SIAG Linear Algebra Prize in 2006, and the SIAM Outstanding Paper Prize in 2011. Along with his students, he has received several best paper awards at leading data mining and machine learning conferences. He is a senior member of the IEEE, a member of the ACM, and a member of SIAM.
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Maja Pantic received the MSc and PhD degrees in computer science from Delft University of Technology, The Netherlands, in 1997 and 2001. From 2001 to 2005, she was an assistant and then an associate professor in the Electrical Engineering, Mathematics and Computer Science (EEMCS) Department at Delft University of Technology. In 2006, she joined the Department of Computing at Imperial College London, United Kingdom, where she is full-time professor of affective and behavioral computing and head of the Intelligent Behaviour Understanding Group (iBUG), working on machine analysis of human nonverbal behavior and its applications to HCI. Since November 2006, she has also held an appointment as a part-time professor of affective and behavioral computing at EEMCS of the University of Twente, The Netherlands. In 2002, for her research on Facial Information for Advanced Interface (FIFAI), Professor Pantic received the Dutch Research Council Junior Fellowship (NWO Veni), awarded annually to the seven best young scientists in exact sciences in The Netherlands. In 2008, for her research on Machine Analysis of Human Naturalistic Behavior (MAHNOB), she received a European Research Council Starting Grant, awarded annually to the 2 percent best young scientists in any research field in Europe. In 2011, she received the BCS Roger Needham Award, awarded annually to a UK-based researcher for a distinguished research contribution in computer science within 10 years of receipt of their PhD. She currently serves as the editor in chief of the Image and Vision Computing Journal (IVCJ) and an associate editor for the IEEE Transactions on Systems, Man, and Cybernetics Part B (TSMC-B). She was and is the organizer of several conferences, including IEEE SMC 2004, IEEE FG 2008 and 2013, and ACII 2009, and she is the initiator and co-organizer of both CVPR for Human Communicative Behavior Analysis (CVPR4HB 2008-2011) and the Social Signal Processing Workshop (SSPW 2009-2011). She is one of the world’s leading experts in research on machine understanding of human behavior, including vision-based detection, tracking, and analysis of human behavioral cues like facial expressions and body gestures, and multimodal analysis of human behaviors like laughter, social signals, and affective states. She is also one of the pioneers in the design and development of fully automatic, affect-sensitive human-centered anticipatory interfaces, built for humans based on human models. She has published more than 150 technical papers in the areas of machine analysis of facial expressions and emotions, machine analysis of human body gestures, and human-computer interaction. Her work is widely cited and has more than 25 popular press coverage (including New Scientist, BBC Radio, and NL TV 1 and 3). She is a senior member of the IEEE, and has served as the Key Note Speaker and an organization/ program committee member for numerous conferences.
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Eric Xing received the BS degree in physics from Tsinghua University, his first PhD degree in molecular biology and bochemistry from Rutgers University, and then his second PhD degree in computer science from the University of California, Berkeley. He is an associate professor in the Machine Learning Department, the Computer Science Department, the Language Technology Institute, and the Lane Center of Computational Biology within the School of Computer Science at Carnegie Mellon University (CMU). His principal research interests lie in the development of machine learning and statistical methodology, especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds, and for building quantitative models and predictive understandings of the evolutionary mechanism, regulatory circuitry, and developmental processes of biological systems. He directs the Laboratory of Statistical Artificial Intelligence and Integrative Genomics (SAILING LAB) at Carnegie Mellon. His current work involves 1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models with evolving structures, sparse structured input/output models in very high-dimensional space, nonparametric techniques for infinite-dimensional models, and large scale, distributed, and methods for online inference and optimization; 2) computational and comparative genomic analysis of biological sequences, systems biology investigation of gene regulation, and statistical analysis of genetic variation, demography, and disease linkage; and 3) applications and system development in large scale social networks analysis, social media mining, computer vision, and natural language processing, which are supported by NSF, NIH, DARPA, ONR, AFOSR, Sloan Foundation, etc. He has been a member of the faculty at CMU since 2004. He has published more than 140 peer-reviewed papers in machine learning, statistics and computational biology, and is an action editor of the Machine Learning Journal, and an associate editor of the Annals of Applied Statistics and the PLoS Journal of Computational Biology. He also serves as a member of the DARPA Information Science and Technology (ISAT) Advisory Group. He is a recipient of the NSF Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Young Investigator Award, and best paper awards from a number of premier conferences, including UAI, ACL, SDM, and ISMB.
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