Nancy M. Amato


Nancy Marie Amato is an American Computer Scientist noted for her research on the algorithmic foundations of motion planning, computational biology, computational geometry and parallel computing. Amato is the Abel Bliss Professor of Engineering and Head of the Department of Computer Science at the University of Illinois at Urbana-Champaign. Amato is noted for her leadership in broadening participation in computing, and is currently a member of the steering committee of CRA-WP, of which she has been a member of the board since 2000.

Education

Amato received a Bachelor of Arts degree in Economics and a Bachelor of Science degree in Mathematical Sciences from Stanford University in 1986. She received an MS in Computer Science from the University of California, Berkeley in 1988, with advisor Manuel Blum. She received a PhD in Computer Science from the University of Illinois at Urbana-Champaign in 1995 with advisor Franco P. Preparata, and her thesis was entitled "Parallel Algorithms for Convex Hulls and Proximity
Problems".

Career and research

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.
In July 2018, Amato was named the next head of the Department of Computer Science at the University of Illinois at Urbana-Champaign, starting in January 2019.
Amato has several notable results.
Her paper on probabilistic roadmap
methods 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,
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 the University of Illinois at Urbana-Champaign. STAPL is a parallel C++ library.

Awards and honors

Her other notable awards include: