I am an assistant professor of statistics at the University of British Columbia. My research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and Bayesian theory. I was previously a postdoctoral associate advised by Tamara Broderick in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D. candidate under Jonathan How in the Laboratory for Information and Decision Systems (LIDS) at MIT, and before that I was in the Engineering Science program at the University of Toronto.

- I'll be giving talks at ISBA-EAC 2019, JSM 2019, and BNP 2019
- Two more JSM 2019 topic-contributed sessions accepted!: "Advances in Bayesian Nonparametric Methods and Its Applications" (co-organized with the jISBA board) and "Believable Big Bayes: Large-Scale Bayesian Inference with Finite-Data Guarantees" (co-organized with Jonathan Huggins)
- Two papers accepted at AISTATS 2019 -- one on scalable GP inference and one on compressing random features for kernel matrix approximation
- Our Hilbert coresets paper was accepted to the Journal of Machine Learning Research
- Our JSM 2019 invited session (co-organized with Jonathan Huggins and David Dunson) on ``Scaling up Bayesian inference for massive datasets'' was accepted
- Our NIPS 2018 workshop, All of Bayesian Nonparametrics (especially the useful bits), was accepted
- Our exchangeable trait allocations paper (with D. Cai and T. Broderick) was accepted in the Electronic Journal of Statistics
- I will be giving a talk at Allerton in October 2018
- I will be giving a talk at Boston Bayesians on June 20
- Our latest work on Bayesian coreset construction was accepted at ICML 2018
- I will be joining the University of British Columbia Department of Statistics in July as an Assistant Professor
- I will be giving a talk on our new Bayesian coreset construction algorithm at ISBA 2018
- I will be giving a talk on Bayesian coresets at JSM 2018
- Our truncated random measures paper (with J. Huggins, J. How, and T. Broderick) was accepted in Bernoulli
- Our dynamic clustering paper (with B. Kulis and J. How) was accepted in IEEE TPAMI

- My list of STAT548 qualifying course papers may be found here
- My talk on "How to Explain Things" may be found here
- STAT547P Fall 2018: Bayesian nonparametric modelling and inference
- DSCI100 Fall19, Spr20: Introduction to Data Science. Online textbook available here

Ph.D. Student

M.Sc. Student

Undergraduate Research Assistant

Practical bounds on the error of Bayesian posterior approximations: a nonasymptotic approach

Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-Seq data

Validated variational inference via practical posterior error bounds

International Conference on Artificial Intelligence and Statistics, 2020

International Conference on Artificial Intelligence and Statistics, 2020

Sparse variational inference: Bayesian coresets from scratch

Advances in Neural Information Processing Systems, 2019

Advances in Neural Information Processing Systems, 2019

Universal boosting variational inference

Advances in Neural Information Processing Systems, 2019

Advances in Neural Information Processing Systems, 2019

Data-dependent compression of random features for large-scale kernel approximation

International Conference on Artificial Intelligence and Statistics, 2019

International Conference on Artificial Intelligence and Statistics, 2019

Scalable Gaussian process inference with finite-data mean and variance guarantees

International Conference on Artificial Intelligence and Statistics, 2019

International Conference on Artificial Intelligence and Statistics, 2019

Bayesian coreset construction via greedy iterative geodesic ascent

International Conference on Machine Learning, 2018

International Conference on Machine Learning, 2018

Dynamic clustering algorithms via small-variance analysis of Markov chain mixture models

IEEE Transactions on Pattern Analysis and Machine Intelligence 41(6), 1338-1352, 2019

IEEE Transactions on Pattern Analysis and Machine Intelligence 41(6), 1338-1352, 2019

Exchangeable trait allocations

Electronic Journal of Statistics 12(2), 2290-2322, 2018

Electronic Journal of Statistics 12(2), 2290-2322, 2018

Efficient global point cloud alignment using Bayesian nonparametric mixtures

IEEE Conference on Computer Vision and Pattern Recognition, 2017

IEEE Conference on Computer Vision and Pattern Recognition, 2017

Coresets for scalable Bayesian logistic regression

Advances in Neural Information Processing Systems, 2016

Advances in Neural Information Processing Systems, 2016

Edge-exchangeable graphs and sparsity

Advances in Neural Information Processing Systems, 2016

Advances in Neural Information Processing Systems, 2016

Streaming, distributed variational inference for Bayesian nonparametrics

Advances in Neural Information Processing Systems, 2015

Advances in Neural Information Processing Systems, 2015

Small-variance nonparametric clustering on the hypersphere

IEEE Conference on Computer Vision and Pattern Recognition, 2015

IEEE Conference on Computer Vision and Pattern Recognition, 2015

Bayesian nonparametric set construction for robust optimization

American Control Conference, 2015

American Control Conference, 2015

Approximate decentralized Bayesian inference

Uncertainty in Artificial Intelligence, 2014

Uncertainty in Artificial Intelligence, 2014

Multiagent allocation of Markov decision process tasks

American Control Conference, 2013

American Control Conference, 2013

Simultaneous clustering on representation expansion for learning multimodel MDPs

Reinforcement Learning and Decision Making, 2013

Reinforcement Learning and Decision Making, 2013

Multiagent planning with Bayesian nonparametric asymptotics

Massachusetts Institute of Technology, 2013

Massachusetts Institute of Technology, 2013