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
[arxiv:1704.02369]
- 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)
[pdf]