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.

- 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

- 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]

- 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, University College London

Supervisor: Yee Whye Teh

[pdf] [bibtex]

- 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

- 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

Duke University, Box 90251,

Durham, NC, 27708-0251, USA