Nancy M. Amato

From Wikipedia, the free encyclopedia
Jump to: navigation, search
Nancy M. Amato
Born Portland, Oregon, US
Nationality American
Fields Computer Science
Institutions Texas A&M University
Alma mater University of Illinois at Urbana-Champaign
University of California, Berkeley
Stanford University
Doctoral advisor Franco P. Preparata
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)
ACM Fellow (2014)

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.

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.


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.


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.[1] 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.[2] 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.[3] In another paper she and a student wrote introduced a novel technique, approximate convex decomposition (ACD), for partitioning a polyhedron into approximately convex pieces.[4] 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.[5]


In 2010, she was named an IEEE Fellow " For contributions to the algorithmic foundations of motion planning in robotics and computational biology." [6]

Her other notable awards include:

  • ACM Fellow in 2015[7]
  • CRA A. Nico Habermann Award in 2014[8]
  • Fellow, American Association for the Advancement of Science (AAAS) 2013[9] for contributions to the algorithmic foundations of motion planning, computational biology, computational geometry, and parallel computing.
  • Hewlett-Packard/Harriett B. Rigas Award, 2013.[10]
  • ACM Distinguished Member in 2012[11]


  1. ^ Nancy M. Amato; Osman B. Bayazit; Lucia K. Dale; Christopher Jones & Daniel Vallejo (1998). "OBPRM: An Obstacle-Based PRM for 3D Workspaces". Robotics: The Algorithmic Perspective (Selected Contributions of WAFR 1998): 155–168. 
  2. ^ Guang Song & Nancy M. Amato (2004). "A Motion Planning Approach to Folding: From Paper Craft to Protein Foldin". IEEE Transactions on Robotics and Automation: 60–71. 
  3. ^ Lydia Tapia; Xinyu Tang; Shawna Thomas & Nancy M. Amato (2007). "Kinetics Analysis Methods For Approximate Folding Landscapes". Bioinformatics. 23: 539–548. doi:10.1093/bioinformatics/btm199. 
  4. ^ Jyh-Ming Lien & Nancy M. Amato (2006). "Approximate Convex Decomposition of Polygons". Computational Geometry. 35 (1-2): 100–123. doi:10.1016/j.comgeo.2005.10.005. 
  5. ^ Gabriel Tanase, Antal Buss, Adam Fidel, Harshvardhan, Ioannis Papadopoulos, Olga Pearce, Timmie Smith, Nathan Thomas, Xiabing Xu, Nedhal Mourad, Jeremy Vu, Mauro Bianco, Nancy M. Amato, and Lawrence Rauchwerger (2011). "The STAPL Parallel Container Framework". In Proceedings of the ACM SIGPLAN Symposium of Principles and Practice of Parallel Programming (PPoPP): 235–246. 
  6. ^ Institute of Electronics and Electrical Engineering (2010). "Fellow Class of 2010". Institute of Electrical and Electronics Engineering. Retrieved 2013-04-28. 
  7. ^ Association of Computing Machinery. "ACM Fellows Named for Computing Innovations that Are Advancing Technology in the Digital Age". ACM. Retrieved 2015-12-08. 
  8. ^ Computing Research Association. "A. Nico Habermann Award". CRA. Retrieved 2014-02-28. 
  9. ^ American Association for the Advancement of Science (AAAS) (2013-11-25). "AAAS Council Elects 388 New AAAS Fellows". AAS. Retrieved 2014-02-28. 
  10. ^ IEEE Education Society. "Hewlett-Packard/Harriett B. Rigas Award". IEEE. Retrieved 2014-02-28. 
  11. ^ Association of Computing Machinery (2012-12-18). "ACM Recognizes Distinguished Members for Computing Advances that Sustain Competitiveness - 2012 Recipients Embody the Rewards of Participation in the Computing Community". ACM. Retrieved 2014-02-28. 

External links[edit]