Vinayak Rao

I'm at Purdue now.

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: