Juan Meza is Dean of the School of Natural Sciences at UC Merced, which has programs in Applied Mathematical Sciences, Environmental and Evolutionary Sciences, Health and the Environment, Biomedical Sciences, Biological Chemistry and Physics, Atomic, Molecular and Optical Physics and Condensed Matter Physics and Chemistry. He also holds a position as Professor of Applied Mathematics. Prior to joining UC Merced, Dr. Meza was a Senior Scientist and the Department Head of High Performance Computing Research at Lawrence Berkeley National Laboratory. The department focuses on research in computational sciences and engineering, numerical algorithms, scientific data management, visualization, and computer architectures. Dr. Meza has degrees in electrical engineering (B.S. 1978 and Masters 1979, Rice University) and computational mathematics (Masters, Ph.D., 1986, Rice University). Prior to joining LBNL, Dr. Meza was a Distinguished Member of the Technical Staff at Sandia National Labs. In 2010, Dr. Meza was elected a Fellow of the American Association for the Advancement of Science. He was also the recipient of the Association for Computing Machinery Distinguished Speaker, 2011, Hispanic Engineer and Information Technology Magazine, Top 200 Most Influential Hispanics in Technology in 2011, IEEE Computer Society Distinguished Visitor Program, 2010 the Hispanic Business 100 Most Influential Leaders (2009), the 2008 ACM Gordon Bell Prize for Algorithm Innovation, the SACNAS Distinguished Scientist Award for 2008, and the Blackwell-Tapia Prize (2008). He serves on numerous committees including the NRC Board on Mathematical Sciences and its Applications, the NRC US. National Committee for Mathematics, and the NSF Mathematical and Physical Sciences Advisory Committee. Dr. Meza also serves on the SIAM Board of Trustees, and the Advisory Boards for the Institute for Pure and Applied Mathematics and the Institute for Computational and Experimental Research in Mathematics. His current research interests include nonlinear optimization with an emphasis on parallel methods for simulation-based optimization. He has also worked on various scientific and engineering applications including development of high performance computing techniques for environmental management problems and carbon capture, methods for electronic structure calculations for nanoscience applications, nonlinear programming methods for detecting vulnerabilities in electric power grids, optimization methods for molecular conformation problems, optimal design of chemical vapor deposition furnaces, and semiconductor device modeling.