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CS grad student Elif Yamangil wins Google Fellowship
MOUNTAIN VIEW, CA. - June 7, 2011 - Elif Yamangil, a Ph.D. candidate in Computer Science at the Harvard School of Engineering and Applied Sciences (SEAS), has been awarded the 2011 Google Fellowship in Natural Language Processing.
Yamangil, who is advised by Stuart Shieber, James O. Welch, Jr. and Virginia B. Welch Professor of Computer Science at SEAS and Director of the Office for Scholarly Communication, is particularly interested in Statistical Machine Learning, generative models, grammar induction, and Bayesian posterior inference, as well as novel uses of Web text such as data mining Wikipedia revision histories.
Moreover, she is working with Peter K. Bol, Charles H. Carswell Professor of East Asian Languages and Civilizations, Department of East Asian Languages and Civilizations, on the China Biographical Database (CBDB) Project to build computational methods and tools for data miningbiographical information about prominent individuals in Chinese historyprimarily from the seventh through nineteenth centuries.
Prior to enrolling at SEAS, Yamangil received her B.S. in Computer Science and Engineering from Sabanci University in Istanbul, Turkey.
Each year, the Google Fellowships are presented to exemplary Ph.D. students in computer science or related research areas.
The fellowships are designed to acknowledge each student's particular contributions to an area of specialty and provide funding for education and research.
Yamangil and her fellow award winners will receive a cash award (to cover tuition, fees and a stipend), an Android Phone, and be assigned a Google Research Mentor.
In total, 15 students received fellowships in a broad range of areas: Secure Cloud Computing, Machine Learning, Mobile Computing, Human-Computer Interaction, Programming Technology, Parallel Programming, Market Algorithms, Computer Vision, Computer Graphics, Design Development, Computer Networking, Structured Data, Natural Language Processing, Computer Architecture, and Search and Information Retrieval.