
Current Home Page at the University of Washington
Assistant Professor, Computer Science and Engineering, University of Washington (starting 11/15/00)
Alumnus (CNL and Sloan Center for Theoretical Neurobiology),Salk Institute (1997-2000)
Rajesh P.N. Rao
Ph.D., University of Rochester, 1998
The primary goal of my research is to discover the computational
principles underlying the brain's remarkable ability to learn, process
and store information, and to apply this knowledge to the task of
building adaptive robotic systems and artificially intelligent
agents. Some of the questions that motivate my research include: How
does the brain learn efficient representations of novel objects and
events occurring in the natural environment? What are the algorithms
that allow useful sensorimotor routines and behaviors to be learned?
What computational mechanisms allow the brain to adapt to changing
circumstances and remain fault-tolerant and robust? By investigating
these questions within a computational and probabilistic framework, it
is often possible to derive algorithms that not only provide
functional interpretations of neurobiological properties but also
suggest solutions to difficult problems in computer vision, speech,
robotics and artificial intelligence. Some illustrative examples of my
research efforts in these directions are summarized here. A list of publications can be found here. Click here for a copy of
my CV.
Recent Publications:
- Neural Circuits in Silicon [News & Views] (Nature, 405, 891-892, 2000)
- Predictive Sequence Learning in Recurrent Neocortical Circuits (Advances in NIPS 12, 164-170, 2000)
- Learning to Maximize Rewards: Review of the book "Reinforcement Learning" (Neural Networks, 13(1), pp. 135-137, 2000)
- Predictive Coding in the Visual Cortex (Nature Neuroscience, 2(1), 79-87, 1999)
- An Optimal Estimation Approach to Visual Perception and Learning (Vision Research, 39(11), 1963-1989, 1999)
- Learning Lie Groups for Invariant Visual Perception (Advances in NIPS 11, pp. 810-816, 1999)
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What/Where Networks & Local Receptive Fields for Transformation Estimation (Network 9(2), pp. 219-234, 1998)
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Visual Attention during Recognition (Advances in NIPS 10, pp. 80-86, 1998)
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Learning Spatiotemporal Receptive Fields from Natural Images (Tech Report 97.4,Dept of Comp Sci, Univ of Rochester, 1997)
- Kalman
Filter Model of the Visual Cortex (Neural Computation 9(4), pp. 721-763, 1997)
- Deictic Codes for the Embodiment of Cognition (Behav. and Brain Sciences 20(4), pp. 723-767, 1997)
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Dynamic Appearance-Based Recognition (Proceedings of CVPR'97, pp. 540-546, 1997)
- Eye
Movements in Visual Cognition (Technical Report, 1997)
- Robust Kalman Filters (Technical Report, 1997)
- Kalman Filter Models for Invariant Recognition, Motion, and Stereo (Technical Report, 1996)
- Object-Centered Neglect (Computational Neuroscience: Trends in Research 1997)
- Modeling Human Eye Movements in Visual Search (Advances in NIPS 8, pp. 830-836, 1996)
- Face Recognition using Natural Basis Functions (Proceedings of IJCAI*95, pp. 10-17, 1995)
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An Active Vision Architecture based on Iconic Representations (Artificial Intelligence 78, pp. 461-505, 1995)
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Learning Saccadic Eye Movements using Multiscale Spatial Filters (Advances in NIPS 7, pp. 893-900, 1995)
Learning in Mobile Robots:
Rajesh Rao
The Salk Institute, CNL & Sloan Ctr
10010 N. Torrey Pines Road
La Jolla, CA 92037
WWW: http://www.cnl.salk.edu/~rao/
e-mail: rao@salk.edu
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