Dr. Nizar Habash receives a Google Research Award
Preserving Morphological Richness in Poor-Pivot-based Statistical Machine Translation
The proposed effort addresses the weakness of using a morphologically poor language (English) as a pivot/bridge in statistical machine translation between morphologically rich languages (Hebrew and Arabic).
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CCLS Research Scientist Nizar Habash Receives QNRF Award
CCLS receives its first international grant award. The granting agency is the Qatar National Research Fund (QNRF), part of its National Priorities Research Program (NPRP). Out of 1,400 letters of intent submitted to the current NPRP cycle, 631 proposals were considered for review, and 145 projects were awarded (for a total of $121M). 
Drs. Salleb-Aouissi, Passonneau, Waltz, McCord, McGurk and Elhadad awarded a Research Initiatives in Science and Engineering (RISE) funding from the Columbia University Executive Vice President for Research
CCLS researchers, Ansaf Salleb-Aouissi, Rebecca Passonneau, David Waltz, their collaborators from Columbia University Medical School, Mary McCord, Harriet McGurk and CU Biomedical Informatics colleague Noémie Elhadad were awarded funding by Columbia University's Office of the Executive Vice President for Research, for their research on “Understanding Infantile Colic via Machine Learning”. 
CCLS wins GE Ecomagination Innovation Prize
The CCLS submission “Creation of a Columbia Machine Learning System to Optimize the Recharging of Electric Delivery Vehicles in Large Urban Cities” has been chosen as the “Winning University Program” in General Electric’s Ecomagination Innovation Challenge of 2010.GE’s CEO Jeff Immelt presented
the award funding the 3 year, $1.1 million project, at a live event in New York City on Novem
Dr. Monteleoni and co-authors win Best Application Paper Award at CIDU 2010
CCLS researcher, Claire Monteleoni, and her co-authors, Gavin Schmidt, and Shailesh Saroha, were awarded Best Application Paper at the NASA Conference on Intelligent Data Understanding (CIDU) 2010, for their paper, "Tracking Climate Models," which uses machine learning to combine the predictions of 20 global climate models.














