UNIVERSITY PARK, Pa. ‑‑ Five Penn state faculty members have been named Fellows of the American Association for the Advancement of Science, the organization announced.
The 2016 Fellows are Paul Babitzke, professor of biochemistry and molecular biology; Kathleen Postle, professor of biochemistry and molecular biology; Joan M. Redwing, professor of materials science and engineering, chemical engineering and electrical engineering; Jorge O. Sofo, professor of physics, and materials science and engineering; and Rongling Wu, distinguished professor of public health sciences and statistics, and director, Center for Statistical Genetics.
The American Association for the Advancement of Science is the world's largest general scientific society and the publisher of the journal Science. Election as a AAAS Fellow is an honor bestowed upon members by their peers. This year, 391 Fellows were selected for their scientifically or socially distinguished efforts to advance science or its applications. The Fellows will receive certificates and pins on Feb. 18 at the AAAS Fellow's Forum during the 2017 AAAS Annual meeting in Boston.
Babitzke was named for outstanding contributions in the field of microbial genetics and molecular biology, particularly the role of RNA in prokaryotic gene regulation.
Postle becomes a Fellow for distinguished contributions to the understanding of the TonB system of Gram-negative bacteria in vivo.
Redwing receives her award for key contributions to the understanding of materials synthesis of nanostructured materials including nanowires, 2D structures, group-III nitrides, topological insulators and boride-based superconductors.
Sofo becomes a Fellow for contributions to computational discoveries in transport, structural, optical and thermoelectric properties of materials, including the successful prediction of graphane, a hydrogenated form of graphene.
Wu becomes a Fellow for distinguished contributions to the field of statistical genetics, particularly inventing functional mapping to model and predict phenotypes of complex dynamic traits using genetic information.