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
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.