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

Select recent publications:

  • Awan, J., and Rao, V.A. (2023)
    Privacy-aware rejection sampling
    Journal of Machine Learning Research [abs]

  • Ju, N., Awan, J., Gu R. and Rao, V.A. (2022)
    Data augmentation MCMC for bayesian inference from privatized data
    Advances in Neural Information Processing Systems [abs]

  • Zhang, B. and Rao, V.A. (2020)
    Efficient parameter sampling for Markov jump processes
    Journal of Computational and Graphical Statistics

  • Yang, J., Rao, V.A., and Neville, J. (2019)
    A Stein-Papangelou Goodness-of-Fit Test for Point Processes.
    Artificial Intelligence and Statistics (AISTATS 2019) (oral)