Vinayak Rao
I'm a postdoc at the department of Statistical science, Duke University working with
David Dunson.
I completed my PhD at the Gatsby Unit, UCL, where my advisor was Yee Whye Teh .
Here is my CV.
Here is my Google Scholar profile.
And here are some photographs I've taken.
Research Interests:
- Bayesian nonparametrics:
    Dependent nonparametric models, MCMC methods and deterministic approximations for efficient inference in nonparametric models
- Continuous time stochastic processes:
    MCMC methods for inference in Markov jump processes and continuous time Bayesian networks
- Point processes:
    Nonstationary renewal processes and repulsive point processes
- Markov chain Monte Carlo for doubly intractable problems
- Machine learning techniques for approximate inference
Submitted papers :
- Rao, V.A., Adams,R.P. and Dunson, D.B. (2013)
Bayesian inference for Matérn repulsive processes
(Under revision)
[arxiv:1308.1136]
[bibtex]
- Lin, L., Rao, V.A., and Dunson, D.B. (2013)
Bayesian nonparametric inference on the Stiefel manifold
[arxiv:1311.0907]
[bibtex]
- Rao, V.A., Lin, L. and Dunson, D.B. (2014)
Data augmentation for models based on rejection sampling
[arxiv:1406.6652]
[bibtex]
Publications:
- Yuan, X., Rao,V.A., Han,S., and Carin, L.
Hierarchical Infinite Divisibility for Multiscale Shrinkage
IEEE Transactions on Signal Processing (accepted for publication)
[pdf]
[supplementary]
- Lian, W., Rao,V.A., Eriksson,B. and Carin, L. (accepted)
Modeling correlated arrival events with latent semi-Markov processes
International Conference on Machine Learning (ICML 2014)
[pdf]
[JMLR]
[bibtex]
- Rao,V.A. and Teh, Y.W. (2013)
Fast MCMC sampling for Markov jump processes and extensions
Journal of Machine Learning Research 14:3295−3320, 2013
[pdf]
[JMLR]
[bibtex]
- Carlson, D., Rao, V.A., Vogelstein, J., and Carin L. (2013)
Real-time inference for a Gamma process model of neural spiking
Advances in Neural Information Processing Systems 26 (NIPS 2013)
[pdf]
[bibtex]
[code]
- Chen, C., Rao,V.A., Buntine,W. and Teh, Y.W. (2013)
Dependent normalized random measures (Oral presentation)
International Conference on Machine Learning (ICML 2013)
[pdf]
[supplementary]
[bibtex]
- Rao,V.A. and Teh, Y.W. (2012)
MCMC for continuous-time discrete-state systems
Advances in Neural Information Processing Systems 25 (NIPS 2012)
[pdf]
[supplementary]
[bibtex]
- Petralia, F., Rao,V.A. and Dunson, D.B. (2012)
Repulsive mixtures
Advances in Neural Information Processing Systems 25 (NIPS 2012)
[pdf]
[bibtex]
- Rao,V.A. and Teh, Y.W. (2011)
Gaussian process modulated renewal processes
Advances in Neural Information Processing Systems 24 (NIPS 2011)
[pdf]
[supplementary]
[bibtex]
- Rao,V.A. and Teh, Y.W. (2011)
Fast MCMC inference for Markov jump processes and continuous time Bayesian networks
27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
[pdf]
[bibtex]
- Rao,V.A. and Teh, Y.W. (2009)
Spatial normalized Gamma processes (Spotlight presentation)
Advances in Neural Information Processing Systems 22 (NIPS 2009)
[pdf]
[bibtex]
- 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)
[pdf]
[bibtex]
- Rao,V.A. and Howard,M.W. (2007)
Retrieved context and the discovery of semantic structure (Spotlight presentation)
Advances in Neural Information Processing Systems 20 (NIPS 2007)
[pdf]
[bibtex]
PhD Thesis :
Markov chain Monte Carlo for continuous-time discrete-state systems
PhD thesis, University College London
Supervisor: Yee Whye Teh
[pdf]
[bibtex]
Talks:
- Constructing dependent random probability measures from completely random measures, 9th workshop on Bayesian nonparametrics, Amsterdam, June, 2013 (invited talk) (slides)
- Efficient MCMC for continuous time discrete state systems, Department of Statistics, North Carolina State University, February 2013 (slides)
- 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 (slides)
- 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:
Department of Statistical Science,
Duke University, Box 90251,
Durham, NC, 27708-0251, USA
vrao (at) gatsby . ucl . ac . uk