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Computational Statistics

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Computational Statistics

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Research Frontiers in Bayesian Computational Methods for High-Dimensional Data

Development of scalable Bayesian inference algorithms for problems involving thousands or millions of parameters using variational approximation and sampling techniques.

Variational Inference in Non-Euclidean Latent Spaces

Scalable Posterior Sampling Beyond Gradient Descent

Implicit Bias of Hamiltonian Monte Carlo Dynamics

Approximate Bayesian Computation for Intractable Likelihoods

Manifold-Aware Variational Autoencoders and Posteriors

Distributed Inference Under Communication Constraints

Bayesian Optimization in Combinatorial High Dimensions

Adaptive Tempering for Multimodal Posterior Landscapes

Neural Approximations to Intractable Conditional Distributions

Uncertainty Quantification in Deep Generative Models

All Computational Statistics PhD categories