Vice Provost for Research
Professor of Computer Science and Computer Engineering
Raghavan's research predominantly concerns enhancing the parallel
performance, energy efficiency and reliability of computations that
involve high-dimensional sparse and unstructured data including
matrices, and graphs. Sparse representations of data arise from many
different contexts, for example, from largely local connections in a
system represented as a sparse graph or network or from partial
differential equations models where the number of nonzeroes in an
associated matrix is bounded by a small constant times its dimension.
Algorithms that exploit sparsity can deliver performance improvements by
constant factors to orders of magnitude. Consequently, they enable the
solution of larger or more complex problems for computation and
data-enabled science and engineering "at scale" in a variety of
Raghavan is a Professor of Computer Science in the Department of Electrical
Engineering and Computer Science at Vanderbilt University, where she is also
Vice Provost for Research. Prior to joining Vanderbilt in February 2016, she
was a Distinguished Professor of Computer Science and Engineering at the
Pennsylvania State University and served as the Associate Vice President for
Research and Director of Strategic Initiatives, in addition to being the
founding Director of the Institute for CyberScience, the coordinating unit on
campus for developing interdisciplinary computation and data-enabled science
and engineering and the provider of high-performance computing services for the
university. Raghavan received her Ph.D. in computer science from Penn State.
Prior to joining Penn State in August 2000, she served as an associate
professor in the Department of Computer Science at the University of Tennessee
and as a research scientist at the Oak Ridge National Laboratory.
Raghavan specializes in high-performance computing and computational science and engineering. She has led the development of “sparse algorithms” that derive from and operate on compact yet accurate representation of high dimensional data, complex models, and computed results. Raghavan has developed parallel sparse linear solvers that limit the growth of computational costs and utilize the concurrent computing capability of advanced hardware to enable the solution of complex large-scale modeling and simulation problems that are otherwise beyond reach. Raghavan was also among the first to propose the design of energy-efficient supercomputing systems by combining results from sparse scientific computing with energy-aware hardware optimizations used for small-embedded computers. In her professorial role, Raghavan is deeply involved in education and research, with 46 Masters and Ph.D. theses supervised and approximately 100 peer-reviewed publications. She has earned several awards including an NSF CAREER Award (1995), the Maria Goeppert-Mayer Distinguished Scholar Award (2002, University of Chicago and the Argonne National Laboratory), and selection as an IEEE Fellow (2013).
Raghavan is a prominent member of major professional societies including SIAM (Society of Industrial and Applied Mathematics) and IEEE (Institute of Electrical and Electronics Engineers). She is the Chair of the Technical Program of the 2017 IEEE/ACM Conference on Supercomputing and serves on the editorial boards of SIAM series on Computational Science and Engineering, and Software, Environments and Tools. Raghavan is also a member of the SIAM Committee on Science Policy and the SIAM Council, which together with its Board and officers leads SIAM. Raghavan serves on the Advisory Board of the Computing and Information Science and Engineering Directorate of the National Science Foundation and the National Academies Panel on Computational Sciences at the Army Research Laboratory.