Rajesh P. N. Rao
Associate Professor, CSE, University of Washington
Faculty Member, Neurobiology & Behavior Program, University of Washington
Faculty Member, Faculty of 1000 Biology, Theoretical Neuroscience Section
Sloan Postdoctoral Fellow, Salk Institute (1997-2000)
Ph.D., University of Rochester, 1998
Research Interests:
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 brain-computer interfaces
(BCIs). How does the brain learn efficient representations of objects
and events occurring in the natural environment? What are the
algorithms that allow useful sensorimotor behaviors to be learned?
What computational mechanisms allow the brain to adapt to changing
circumstances and remain fault-tolerant and robust? How can the
knowledge gained through computational studies of the brain be used in
biomedical applications such as BCIs for the disabled? My students and
I are investigating these questions using a combination of
probabilistic techniques, computer simulations, and collaborative
neurobiological experiments. Such an interdisciplinary approach has
provided functional interpretations of several otherwise puzzling
neurobiological properties while at the same time suggesting
biologically-inspired solutions to problems in computer vision,
robotics and artificial intelligence. Click here for a copy
of my CV.
Visit the Laboratory for Neural Systems Web Page for a list of ongoing projects.
News from the Laboratory for Neural Systems
Recent professional activities
Gordon Research Conference on Theoretical Biology, invited speaker, 2006
Workshop on Bayesian Cognition, invited speaker, 2006
Computer Vision and Pattern Recognition (CVPR), program committee, 2006
Probabilistic Models of Cognition Workshop, invited speaker, 2005
Okinawa Computational Neuroscience
Course, co-organizer, 2004
ICDL program committee,
2004
AAAI-04
program committee, 2004
Mathematical
Neuroscience Workshop, invited speaker, 2004
Workshop on
STDP, invited speaker, 2004
NIPS
program committee, 2003
Sloan-Swartz
Annual Meeting on Theoretical Neurobiology, invited speaker,
2003
AISB Convention, keynote
speaker, 2003
Neural Information
and Coding Workshop, invited speaker, 2003
MBI Workshop on
Systems-Level Modeling/Neural Coding, invited speaker,
2002/2003
NIPS organizing committee,
2002
Meeting with the President of India, August 30, 2002.
Awards
Publications
Books
Selected Articles
- Planning and Acting in Uncertain Environments using Probabilistic Inference (with D. Verma, Proceedings of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2006)
- Dynamic imitation in a humanoid robot through nonparametric probabilistic inference (with Grimes and Chalodhorn, Proceedings of the 2006 Robotics Science and Systems Conference, 2006)
- Electrocorticography-based brain computer interface -- the Seattle experience (with Leuthardt et al., IEEE Trans Neural Syst Rehab Eng 14(2):194-8, 2006)
- A probabilistic model of gaze imitation and shared attention (with Hoffman et al., Neural Networks 19(3):299-310, 2006)
- Towards adaptive classification for BCI (with Shenoy et al., J Neural Eng 3(1):R13-23, 2006)
- Learning Dynamic Humanoid Motion using Predictive Control in Low Dimensional Subspaces, Chalodhorn, R., Grimes, D. B., Maganis, G. and Rao, R. P. N., Proceeding of the IEEE-RAS/RSJ International Conference on Humanoid Robots, Dec 2005 (Humanoids 2005)
- Learning Humanoid Motion Dynamics through Sensory-Motor Mapping in Reduced Dimensional Spaces, Chalodhorn, R., Grimes, D. B., Maganis, G., Rao, R. P. N., and Asada, M., Proceeding of the IEEE International Conference on Robotics and Automation, May 2006 (ICRA 2006)
- Neural models of Bayesian belief propagation (Bayesian Brain, 2007)
- Bayesian inference and attention in the visual cortex (Neuroreport 16(16), 1843-1848, 2005)
- Goal-based imitation as probabilistic inference over graphical models (Advances in NIPS 18, 2006)
- Learning shared latent structure for image synthesis and robotic imitation (Advances in NIPS 18, 2006)
- Hierarchical Bayesian inference in networks of spiking neurons (Advances in NIPS 17, 2005)
- Dynamic Bayesian networks for brain-computer interfaces (with P. Shenoy, Advances in NIPS 17, 2005)
- Adaptive myographic control of a robotic arm (Proceedings of AAAI, 523-528, 2005)
- Bilinear sparse coding for invariant vision (with D. Grimes, Neural Computation, 17(1), 47-73, 2005)
- Bayesian computation in recurrent neural circuits (Neural Computation, 16(1), 1-38, 2004)
-
Graphical models for planning and imitation (with D. Verma, Technical Report 2005-02-01, February 2005)
- A Bayesian model of imitation in infants and robots (with A. Shon and A. Meltzoff, Imitation and Social Learning in Robots, Humans, and Animals, Cambridge University Press, 2005)
- A probabilistic framework for model-based imitation learning (with A. Shon, D. Grimes, and C. Baker, Proceedings of the 26th Annual Meeting of the Cognitive Science Society, 2004)
- Probabilistic models of attention based on iconic representations and predictive coding (with Dana Ballard, Neurobiology of Attention, 2004)
- Probabilistic bilinear models for appearance-based vision (with David Grimes and Aaron Shon, Proc. of ICCV'03, 2003)
- Self-organizing neural systems based on predictive learning (with Terry Sejnowski, Phil. Trans. Royal Soc. Lond. A, Vol. 361, 2003)
- Imitation learning in infants and robots (with Andy Meltzoff, Proc. of AISB'03, 2003)
- A Bilinear Model for Sparse Coding (with D. Grimes, Advances in NIPS 15, 2003)
- Learning Temporal Patterns by Redistribution of Synaptic Efficacy (with A. Shon, Neurocomputing, 2003)
- Receptive Field (invited review article) (Encyclopedia of the Human Brain, 2002)
- Models of Attention (invited review article) (Encyclopedia of Cognitive Science, 2002)
- Eye Movements in Iconic Visual Search (Vision Research 42(11), 1447-1463, 2002)
- Awakening a sleeping cat: Invited review of Information Theory and the Brain (Neural Networks, 2002)
- Spike Timing Dependent Hebbian Plasticity as Temporal Difference Learning (Neural Computation, 13(10), 2221-2237, 2001)
- News and Views by Peter Dayan (Trends in Cog. Sci., 6(3), 105-106, 2002)
- Optimal Smoothing in Visual Motion Perception (Neural Computation, 13(6), 1243-1253, 2001)
- Predictive Learning of Temporal Sequences in Neocortical Circuits (Complexity in Bio. Info. Processing, 2001)
- Reliability of Spike Timing in Cortical Neurons (J. Neurophysiology, 85(4), 1782-1787, 2001)
- 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)
-
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)
- 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)
Teaching:
Prior Work:
- Learning in Mobile Robots:
-
Learning Spatiotemporal Receptive Fields from Natural Images (Tech Report 97.4,Dept of Comp Sci, Univ of Rochester, 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)
Other Activities:
- Band member, The Smashing Notkins (2002-2003).
Official Band Photo (copyright: Melissa Garcia, 2003).
Rajesh Rao
Computer Science & Engineering
566 Allen Center
University of Washington
Box 352350
Seattle, WA 98195-2350
Phone: 206-685-9141
Fax: 206-543-2969
WWW: http://www.cs.washington.edu/homes/rao
e-mail: rao[at]cs[dot]washington[dot]edu