Nationality American Fields Computer Science | Name Nancy Amato Institution Texas A&M University | |
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Born Portland, Oregon, US Doctoral students Steve Wilmarth
Daniel Vallejo
Greg Schmidt
Lucia Dale
Wookho Son
O. Burchan Bayazit
Guang Song
Jinsuck Kim
Jyh-Ming Lien
Xinyu Tang
Marco Morales (computer scientist)
Lydia Tapia
Shawna Thomas
Gabriel Tanase
Samuel Rodriguez (computer scientist)
Roger Pearce
Sam Ade Jacobs Known for motion planning
computational biology
computational geometry
parallel computing Notable awards IEEE Fellow (2010)
CRA A. Nico Habermann Award (2014) Alma mater University of Illinois at Urbana–Champaign, University of California, Berkeley, Stanford University | ||
Doctoral advisor Franco P. Preparata Institutions Texas A&M University | ||
Nancy M. Amato is an American Computer Scientist noted for her research on the algorithmic foundations of motion planning, computational biology, computational geometry and parallel computing.
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She is also noted for her leadership in broadening participation in computing. She is currently a member of the steering committee of CRA-W, of which she has been a member of the board since 2000.
Biography
Amato received a A.B. in Economics and an B.S. in Mathematical Sciences from Stanford University in 1986. She received a M.S. in Computer Science from the University of California, Berkeley in 1988 and a Ph.D in Computer Science from the University of Illinois at Urbana-Champaign in 1995.
She then joined the Department of Computer Science at Texas A&M University as an assistant professor in 1995. She was promoted to associate professor in 2000, to professor in 2004, and to Unocal professor in 2011.
Career
Amato has several notable results. Her paper on probabilistic roadmap methods (PRMs) is one of the most important papers on PRM. It describes the first PRM variant that does not use uniform sampling in the robot's configuration space. She wrote a seminal paper with one of her students that shows how the PRM methodology can be applied to protein motions, and in particular protein folding. This approach has opened up a new research area in computational biology. This result opens up a rich new set of applications for this technique in computational biology. Another paper she wrote with her students represents a major advance by showing how global energy landscape statistics such as relative folding rates and population kinetics can be computed for proteins from the approximate landscapes computed by Amato's PRM-based method. In another paper she and a student wrote introduced a novel technique, approximate convex decomposition (ACD), for partitioning a polyhedron into approximately convex pieces. Amato also co-leads the STAPL project with her husband Lawrence Rauchwerger, who is also a computer scientist on the faculty at Texas A&M. STAPL is a parallel C++ library.
Awards
In 2010, she was named an IEEE Fellow " For contributions to the algorithmic foundations of motion planning in robotics and computational biology."
Her other notable awards include: