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

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.

NIPS*98 Workshop on Statistical Theories of Cortical Function

Recent Publications:

• Neural Circuits in Silicon [News & Views] (Nature, 405, 891-892, 2000)
  • Postscript file
  • New York Times article
• 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)
  • Paper (PDF format)
  • News and Views by Koch & Poggio (PDF format)
• 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)
• What/Where Networks & Local Receptive Fields for Transformation Estimation (Network 9(2), pp. 219-234, 1998)
• Visual Attention during Recognition (Advances in NIPS 10, pp. 80-86, 1998)
• 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)
• 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)
• An Active Vision Architecture based on Iconic Representations (Artificial Intelligence 78, pp. 461-505, 1995)
• Learning Saccadic Eye Movements using Multiscale Spatial Filters (Advances in NIPS 7, pp. 893-900, 1995)

Learning in Mobile Robots:

• Short Video of the Robot
• Papers:
  • Rao, R.P.N. and Fuentes, O. (1998) "Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory," Autonomous Robots Journal Vol. 5, pp. 297-316, and Machine Learning Journal, Vol. 31, pp. 87-113.
  • Learning Navigational Behaviors using a Predictive Sparse Distributed Memory (The Fourth Int. Conf. on Simulation of Adaptive Behavior (SAB) 1996)
  • Hierarchical Learning of Reactive Behaviors (IEEE Int. Conf. on Systems, Man, & Cybernetics 1995)
  • Perceptual Homing using Self-Organizing Networks (Proc. of World Congress on Neural Networks 1995)

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