Abhi develops statistical and machine learning methods for large spatial datasets as well as Bayesian models for multi-source epidemiological datasets.

Aki uses Bayesian methods and statistical computing to tackle methodological challenges in healthcare analytics and large-scale biomedical applications.

In many scientific applications, uncertainty of estimates from an upstream analysis needs to be propagated into a downstream analysis without feedback. Cutting feedback, or cut-Bayes, achieves this by constructing a cut-posterior distribution that prevents backward information flow. However, sampling-based implementations of cutting feedback, like nested MCMC, are computationally intensive, while existing variational inference (VI) approaches require two approximations and access to upstream data. We propose NeVI-Cut, a neural network-based variational inference method for cutting feedback. NeVI-Cut directly uses upstream samples without requiring access to upstream data, avoiding extra approximation error. We employ normalizing flows, neural network-based generative models, to flexibly model the downstream conditional distribution. We provide theoretical guarantees for NeVI-Cut to approximate any cut-posterior. A triply stochastic algorithm implements the method. Simulation studies and two real-world analysis show that NeVI-Cut achieves substantial computational gains over sampling-based cutting feedback methods and is more accurate than parametric variational cut approaches.