Computing Sciences Researchers Named Data Science Fellows
July 18, 2014 Tags: Awards
Contact: Linda Vu, +1 510 495 2402, firstname.lastname@example.org
As a Data Science Fellow, Daniela Ushizima will receive funding to explore Data, Information, Science and Computing (DISC) issues, specifically those that involve connecting diverse domain sciences to analytics tools. As Data Science Senior Fellows, Deb Agarwal, Wes Bethel, Peter Nugent and James Sethian will contribute their extensive expertise in data science approaches—honed over decades of research and collaboration—to advise about BIDS initiatives.
Founded in fall 2013, BIDS is part of a five-year collaborative effort between UC Berkeley, the University of Washington and New York University to dramatically accelerate the growth of data intensive discovery in a broad range of fields. The Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation support this work with a $37.7 million award.
Complete list of fellows: http://vcresearch.berkeley.edu/datascience/people
About Berkeley Lab Computing Sciences
The Lawrence Berkeley National Laboratory (Berkeley Lab) Computing Sciences organization provides the computing and networking resources and expertise critical to advancing the Department of Energy's research missions: developing new energy sources, improving energy efficiency, developing new materials and increasing our understanding of ourselves, our world and our universe. ESnet, the Energy Sciences Network, provides the high-bandwidth, reliable connections that link scientists at 40 DOE research sites to each other and to experimental facilities and supercomputing centers around the country. The National Energy Research Scientific Computing Center (NERSC) powers the discoveries of 5,500 scientists at national laboratories and universities, including those at Berkeley Lab's Computational Research Division (CRD). CRD conducts research and development in mathematical modeling and simulation, algorithm design, data storage, management and analysis, computer system architecture and high-performance software implementation.