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Amandy Nwana wins Bouchet Graduate Honor Society research award in sciences

Wednesday, April 22, 2015

Amandianeze (Amandy) Nwana, a Ph.D. candidate in ECE and a Cornell Sloan Fellow, won the Bouchet Graduate Honor Society's research award in the sciences for his presentation entitled, “Increasing Network Efficiency via Latent Diffusion Processes.” The 2015 Bouchet Conference was held April 10-11 at Yale University, New Haven, C.T.

Amandy presented his work based on the increasingly popular idea that information spreads in a network much like viruses spread over a population, with the goal of predicting what information will be most important or popular in the future. He explained a case study that was performed on prefetching the future most popular YouTube videos on a campus in order to reduce end-to-end network traffic as well as delays experienced in the local network, both of which correspond to freeing up network resources.

The Bouchet Graduate Honor Society research award in sciences is presented annually to the member of the society who gives the most outstanding oral presentation in the Science, Technology, Engineering and Mathematics (STEM) fields division at the annual Bouchet Conference on Diversity and Graduate Education.

The Bouchet Graduate Honor Society recognizes outstanding scholarly achievement and promotes diversity and excellence in doctoral education and the professoriate. The Bouchet Society seeks to develop a network of preeminent scholars who exemplify academic and personal excellence, foster environments of support, and serve as examples of scholarship, leadership, character, service, and advocacy for students traditionally underrepresented in the academy.

Amandy is a fourth-year Ph.D. student under the direction of Professor Tsuhan Chen and a member of the Advanced Multimedia Processing (AMP) Lab. He is interested in the study of social and information networks and how to leverage the rich and complex information from (implicit and explicit) social interactions in data to help solve traditional estimation, prediction and detection tasks.

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