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 preprints/publications:

  • Pei, X., Kim, M. and Rao, V.A. (2026)
    Exact Gibbs sampling for stochastic differential equations with gradient drift and constant diffusion
    [arxiv:2602.00512]

  • Banerjee, I., Honnappa, H., and Rao, V.A. (2025)
    Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
    Operations Research [abs]

  • Beraha, M., Favaro, S. and Rao, V.A. (2024)
    MCMC for Bayesian nonparametric mixture modeling under differential privacy
    Journal of Computational and Graphical Statistics [pdf]

  • 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]