Archive
2025
| · | Invited Talk · INFORMS Applied Probability Society Meeting 2025 · An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces | |
| · | Talk · Northeast Systems and Control Workshop 2025 · An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces | |
| · | Poster · GA Tech ISYE-Stanford MS&E-Michigan IOE Joint Rising Stars Workshop · An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces | |
| · | Talk · GA Tech ISyE-Stanford MS&E-Michigan IOE Joint Rising Stars Workshop · Bayesian Algorithms for Decision-making under Uncertainty |
2024
2023
| · | Poster · NeurIPS 2023 · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent | |
| · | Poster · NeurIPS 2023 · Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds | |
| · | Poster · NeurIPS Dataset Track 2023 · The Cambridge Law Corpus: A Dataset for Legal AI Research | |
| · | Invited Talk · Université Grenoble Alpes · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent | |
| · | Invited Talk · ETH Zürich · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent | |
| · | Invited Talk · University College London · Physically Structured Neural Networks for Smooth and Contact Dynamics | |
| · | Talk · Vector Institute · Physically Structured Neural Networks for Smooth and Contact Dynamics | |
| · | Invited Talk · University of Tübingen · Pathwise Conditioning and Non-Euclidean Gaussian Processes | |
| · | Invited Talk · Lancaster University · Pathwise Conditioning and Non-Euclidean Gaussian Processes |
2022
2021
2020
| · | Poster · NeurIPS 2020 · Matérn Gaussian Processes on Riemannian Manifolds | |
| · | Talk · EMNLP 2020 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models | |
| · | Talk · TheAlgo 2020 · Matérn Gaussian Processes on Riemannian Manifolds | |
| · | Talk · University of Oxford · Pathwise, spectral, and geometric perspectives on Gaussian processes | |
| · | Talk · Imperial College London · Efficiently sampling functions from Gaussian process posteriors | |
| · | Talk · Symposium on Machine Learning and Dynamical Systems 2020 · Efficiently sampling functions from Gaussian process posteriors | |
| · | Talk · Imperial College London · Efficiently sampling functions from Gaussian process posteriors | |
| · | Talk · GE Research · Pathwise, spectral, and geometric perspectives on Gaussian processes | |
| · | Talk · AISTATS 2020 · Asynchronous Gibbs Sampling | |
| · | Talk · University College London · Towards physically structured probabilistic reinforcement learning | |
| · | Talk · PROWLER.io · Towards physically structured probabilistic reinforcement learning |
2019
| · | Talk · Linköping Workshop on Computational Text Analysis 2019 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models | |
| · | Poster · Workshop on Structural Inference in High-Dimensional Models 2019 · A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates |
2018
| · | Talk · CRISM Summer School 2018 · Pólya Urn Latent Dirichlet Allocation | |
| · | Talk · MaxEnt 2018 · A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates | |
| · | Poster · ISBA 2018 · Pólya Urn Latent Dirichlet Allocation | |
| · | Poster · Gregynog Statistical Conference 2018 · Pólya Urn Latent Dirichlet Allocation | |
| · | Talk · Carnegie Mellon University · Pólya Urn Latent Dirichlet Allocation |
2013
| · | Undergraduate Research Poster · SHAPE |
2025
| INFORMS Tutorials in OR · The Gittins Index: A Design Principle for Decision-Making Under Uncertainty |
| ICML · Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes |
2024
2023
2022
| PhD Thesis · Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces |
2021
2020
2019
| IEEE TPAMI · Pólya Urn Latent Dirichlet Allocation: A Doubly Sparse Massively Parallel Sampler |