Vinayak Rao
I'm a 5th year PhD student at the Gatsby Unit, UCL, working with Yee Whye Teh .
I've graduated and started a post-doc at Duke University. Here's my new webpage.
Here's my (old) CV. Here are some photos I've taken.
Research Interests:
- Bayesian nonparametrics:
    Dependent nonparametric models, MCMC methods and deterministic approximations for efficient inference in nonparametric models
- Markov jump processes:
    MCMC methods for inference in Markov jump processes and continuous time Bayesian networks
- Point processes:
    Nonstationary renewal processes and repulsive point processes
Publications:
- Rao,V.A. and Teh, Y.W. (2012)
MCMC for continuous-time discrete-state systems (pdf coming soon)
Advances in Neural Information Processing Systems 25 (NIPS 2012)
- Petralia, F., Rao,V.A. and Dunson, D.B. (2012)
Repulsive mixtures (pdf coming soon)
Advances in Neural Information Processing Systems 25 (NIPS 2012)
- Rao,V.A. and Teh, Y.W. (2011)
Gaussian process modulated renewal processes pdf
Supplementary material pdf
Advances in Neural Information Processing Systems 24 (NIPS 2011)
- Rao,V.A. and Teh, Y.W. (2011)
Fast MCMC inference for Markov jump processes and continuous time Bayesian networks pdf
27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
- Rao,V.A. and Teh, Y.W. (2009)
Spatial normalized Gamma processes pdf
Advances in Neural Information Processing Systems 22 (NIPS 2009)
- Howard,M.W., Jing,B., Rao,V.A., Provyn, J.P. and Datey,A.V. (2009)
Bridging the gap: Transitive associations between items presented in similar temporal contexts
Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol 35(2)
- Rao,V.A. and Howard,M.W. (2007)
Retrieved context and the discovery of semantic structure pdf
Advances in Neural Information Processing Systems 20 (NIPS 2007)
Pre-prints :
- Rao,V.A. and Teh, Y.W. (2012)
Fast MCMC sampling for Markov jump processes and extensions [arxiv:1208.4818]
PhD Thesis :
Markov chain Monte Carlo for continuous-time discrete-state systems pdf
PhD thesis, University College London
Supervisor: Yee Whye Teh
Talks:
- Efficient MCMC for continuous time discrete state systems, Machine Learning Group, . University of Cambridge, November 2011
- Efficient MCMC for continuous time discrete state systems, Dept. of Computer Science. Brown University, USA, October 2011
- Spatial normalized random measures, 8th workshop on Bayesian nonparametrics, Veracruz, Mexico, July, 2011
- Expectation Propagation for Dirichlet process mixture models, Machine Learning Group, University of Cambridge, UK, August 2010
- Contextual retrieval in semantic memory: Building Semantic spaces with TCM, Society for Mathematical Psychology, 40th Annual Meeting, 2007
Awards:
- 2011: Bogue research fellowship to work with David Dunson at Duke University (2 months) and Erik Sudderth at Brown University (2 weeks)
- 2007: Outstanding student in Electrical Engineering, Syracuse University
Contact Details:
Gatsby Computational Neuroscience Unit
University College London
Alexandra House
17 Queen Square
London, WC1N 3AR
vrao (at) gatsby . ucl . ac . uk