Vinayak Rao
Computer Science · Purdue University West Lafayette
Publications
82
Citations
666
Est. group size
—
Recurring co-author estimate
Active years
54
Publishing since 1973
Vinayak Rao works in statistics and machine learning, developing methods for Bayesian nonparametric modeling and computational techniques such as Markov chain Monte Carlo (MCMC) sampling. His research focuses on building efficient algorithms for analyzing complex data structures, including stochastic processes (systems that evolve randomly over time), point processes (models for event data), and network data. Prospective students would likely engage with theoretical and algorithmic work on probabilistic modeling, sampling methods, and statistical inference.
Publication output was relatively steady and higher from 2017-2021 (averaging 7-9 per year), then declined notably from 2022-2025 before an uptick in 2026, suggesting a slowing recent cadence with a mean of 2.8 publications/year over the last five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Exact Gibbs sampling for stochastic differential equations with gradient drift and constant diffusion
Open MIND · 2026
- Exact Gibbs sampling for stochastic differential equations with gradient drift and constant diffusion
arXiv (Cornell University) · 2026
- Marginally constrained nonparametric Bayesian inference through Gaussian processes
Journal of Statistical Planning and Inference · 2024
- Detecting Jumps on a Tree: a Hierarchical Pitman-Yor Model for Evolution of Phenotypic Distributions
arXiv (Cornell University) · 2023
- Marginally Constrained Nonparametric Bayesian Inference through Gaussian Processes
arXiv (Cornell University) · 2022
- Evaluation of efficacy and safety of two herbal dentifrices in dental caries, toothache, and oral hygiene: A randomized active controlled prospective clinical study
Journal of Indian System of Medicine · 2021
- Efficient Parameter Sampling for Markov Jump Processes
Figshare · 2021
- Efficient Parameter Sampling for Markov Jump Processes
Journal of Computational and Graphical Statistics · 2020
- An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Journal of Computational and Graphical Statistics · 2020
- Efficient Parameter Sampling for Markov Jump Processes
Figshare · 2020
- An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Figshare · 2020
- A Stein–Papangelou Goodness-of-Fit Test for Point Processes
International Conference on Artificial Intelligence and Statistics · 2019
- An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
arXiv (Cornell University) · 2019
- The Indian Buffet Hawkes Process to Model Evolving Latent Influences
Uncertainty in Artificial Intelligence · 2018
- Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy
International Conference on Machine Learning · 2018
- arXiv (Cornell University)×25
- Uncertainty in Artificial Intelligence×3
- Figshare×3
- Journal of Computational and Graphical Statistics×2
- International Conference on Artificial Intelligence and Statistics×2
- Antik Chakraborty
Computer Science · Purdue University West Lafayette
- Sally Paganin
Computer Science · The Ohio State University
- Fangzheng Xie
Computer Science · Indiana University
- Radu Herbei
Computer Science · The Ohio State University
- Faming Liang
Mathematics · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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