Talks

Towards physically plausible probabilistic reinforcement learning
PROWLER.io

Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
WCTA ‘19

A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates (poster)
HDI ‘19

Pólya Urn Latent Dirichlet Allocation
CRISM ‘18

A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates
MaxEnt ‘18

Pólya Urn Latent Dirichlet Allocation (poster)
ISBA ‘18

Strategies for Distributed Bayesian Computation
Linkoping University

Pólya Urn Latent Dirichlet Allocation (poster)
Gregynog ‘18

Pólya Urn Latent Dirichlet Allocation
Carnegie Mellon University

Asynchronous Markov Chain Monte Carlo and Gibbs Sampling (poster)
NIPS AABI ‘17

Bayesian Inference on Clusters and GPUs: the Systems Perspective on Scalable Computation
JSM ‘17

Bayesian Inference and Markov Chain Monte Carlo Algorithms on GPUs
Nvidia GTC ‘17

Bayesian Inference on Clusters and GPUs: the Hardware/Software Approach to Scalable Computation
Carnegie Mellon University

Asynchronous Gibbs Sampling (poster)
SAMSI DPDA ‘16

Asynchronous Gibbs Sampling and GPU-accelerated Gibbs Sampling
MCQMC ‘16

Asynchronous Gibbs Sampling (poster)
ISBA ‘16

Rigorizing and Extending the Cox-Jaynes Derivation of Probability
BFF ‘16

Asynchronous Gibbs Sampling
MCMSki ‘16

Asynchronous Gibbs Sampling
University of Warwick

SHAPE (undergraduate research poster)
NSF REU on Computer Go at Lewis & Clark College ‘13