University of New Mexico
Project topic areasRobotics
Relational Representations
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This REU site focuses on integrating machine learning into real-world
applications though interdisciplinary collaborations. Machine
learning techniques enable computing devices to automatically discover
how to extract salient information from complex data sets and how to
optimally perform tasks. Applications include robot control, severe
weather prediction, computer security, brain-machine interfaces,
computational neuroscience, bioinformatics, law, and interactive art.
Students will receive training in a variety of areas, including
statistical machine learning, embedded system design, empirical
methods, sensor processing, control, embedded interfaces, technical
writing, oral presentation, ethics, and graduate school preparation.
Each student will be paired with a faculty mentor who is an expert in
machine learning and with a supporting mentor who is an expert in the
real-world application domain.
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2010 EligibilityEligible students are undergraduates who:
Program TimingDue to the advanced nature of this topic, students will be involved during both the summer and academic year (March - October). During the summer, students will spend a 12-week period working full time at either the University of Oklahoma or the University of New Mexico. During the academic year, students will participate from their home institution via video- and teleconference. This latter time (about 5 hours per week) will be used to prepare for the coming summer and to complete experiments and writing projects.We are no longer accepting applications.
Program Information
Linkes to Previous Years' Programs
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| FacultyAndrew H. FaggSupporting MentorsJerry BrotzgeCenter for Analysis and Prediction of Storms, OU
Adam Brown
Rodger A. Brown
Vince Calhoun
Kelvin K. Droegemeier
Emily Meazell
Melanie Moses
Robert Rennaker
Michael Richman
Chris Weaver |
The material on this web site is based upon work supported by the National Science Foundation under Grant No. IIS/REU/0755462. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Last modified: Tue Apr 27 01:31:07 2010