We are proud to announce that our Research Scientist, Edo Liberty, has been recognized with this year's Best Research Paper award at the 19th annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). Held in Chicago, Illinois, between August 11 and 14 this year, KDD is considered by most to be the premier international forum for data mining and big data researchers and practitioners from academia, industry, and government. The conference features an array of events organized to facilitate an exchange of ideas, research results, and experiences that include keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, and demonstrations. With a 17% acceptance rate for paper submissions, KDD is one of the most competitive computer science conferences. That's why we couldn't be happier with Edo and eight other accepted papers (four by Francesco Bonchi) from Yahoo Labs scientists, represented by our 15-person delegation in Chicago. The Best Paper recognition comes at an exciting time of growth for Yahoo Labs and transition for Edo Liberty. Edo recently moved from our Haifa lab to our New York City lab in order to grow and manage a new Scalable Machine Learning Research group. The group will develop scalable machine learning and data mining algorithms, and introduce new data platform capabilities, among other projects. Edo's paper, entitled "Simple and Deterministic Matrix Sketching," describes how to keep a small sketch matrix, B, for a very large matrix, A, that is only available as a read-once stream of vectors (matrix columns). Technically, the algorithm bounds the distance in operator norm of the covariance matrices of A and the sketch B. Practically, it produces an efficient low rank approximation procedure which is many times faster than standard SVD based solutions and which can be applied to data streams of unbounded length.