Research Scientist at Google

trandustin@google.com

Blog

I am a research scientist at Google and a fourth-year Ph.D. student, where I am advised by David Blei and Andrew Gelman. I am broadly interested in advancing science and intelligence, and where the ideas involve probability, programs, and/or neural nets.

I lead development of Edward, a probabilistic programming language in TensorFlow, and contribute to TensorFlow Probability. I used to be on the Stan development team. Previously, I was a Statistics Ph.D. student at Harvard before transferring to Columbia, where I worked with Edo Airoldi and also spent time at the Harvard Intelligent Probabilistic Systems group.

Recently, I have been giving the following talk:

Some of my work is available as preprints on arXiv.

**TensorFlow Distributions**

A backend for efficient, composable manipulation of
probability distributions.

Joshua V. Dillon, Ian Langmore, **Dustin
Tran**, Eugene Brevdo, Srinivas Vasudevan, Dave
Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A.
Saurous

**Expectation propagation as a way of life: A
framework for Bayesian inference on partitioned
data**

How to distribute inference with massive data sets and how
to combine inferences from many data sets.

Andrew Gelman, Aki Vehtari, Pasi Jylänki, Tuomas Sivula,
**Dustin Tran**, Swupnil Sahai, Paul
Blomstedt, John P. Cunningham, David Schiminovich,
Christian Robert

**Edward: A library for probabilistic modeling,
inference, and criticism**

Everything and anything about probabilistic models.

**Dustin Tran**, Alp Kucukelbir, Adji B. Dieng,
Maja Rudolph, Dawen Liang, David M. Blei

**Model criticism for Bayesian causal inference**

How to validate inferences from causal models.

**Dustin Tran**, Francisco J. R. Ruiz, Susan
Athey, David M. Blei

**Stochastic gradient descent methods for estimation with
large data sets**

Fast and statistically efficient algorithms for
generalized linear models and M-estimation.

**Dustin Tran**, Panos Toulis, Edoardo M.
Airoldi

*Journal of Statistical Software*, To appear

**Implicit causal models for genome-wide association
studies**

Generative models applied to causality in genomics.

**Dustin Tran**, David M. Blei

*International Conference on Learning Representations*, 2018

**Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches**

How to make weight perturbations in evolution strategies and
variational BNNs as mini-batch-friendly as activation perturbations
in dropout and batch norm.

Yeming Wen, Paul Vicol, Jimmy Ba, **Dustin Tran**,
Roger Grosse

*International Conference on Learning Representations*, 2018

**Hierarchical implicit models and likelihood-free
variational inference**

Combining the idea of implicit densities with hierarchical Bayesian
modeling and deep neural networks.

**Dustin Tran**, Rajesh Ranganath, David M.
Blei

*Neural Information Processing Systems*, 2017

**Variational inference via $\chi$-upper bound
minimization**

Overdispersed approximations and upper bounding
the model evidence.

Adji B. Dieng, **Dustin Tran**, Rajesh
Ranganath, John Paisley, David M. Blei

*Neural Information Processing Systems*, 2017

**Comment, "Fast approximate inference for
arbitrarily large semiparametric regression models via
message passing"**

The role of message passing in automated inference.

**Dustin Tran**, David M. Blei

*Journal of the American Statistical Association*,
112(517):156–158, 2017

**Automatic differentiation variational inference**

An automated tool for black box variational inference,
available in Stan.

Alp Kucukelbir, **Dustin Tran**, Rajesh Ranganath,
Andrew Gelman, David M. Blei

*Journal of Machine Learning Research*, 18(14):1–45, 2017

**Deep probabilistic programming**

How to build a language with rich compositionality for
modeling and inference.

**Dustin Tran**, Matthew D. Hoffman, Rif A.
Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

*International Conference on Learning Representations*, 2017

**Operator variational inference**

How to formalize computational and statistical tradeoffs in variational inference.

Rajesh Ranganath, Jaan Altosaar, **Dustin
Tran**, and David M. Blei

*Neural Information Processing Systems*, 2016

**Hierarchical variational models**

A Bayesian formalism for constructing expressive
variational families.

Rajesh Ranganath, **Dustin Tran**, David M.
Blei

*International Conference on Machine Learning*, 2016

**Spectral M-estimation with application to hidden
Markov models**

Applying M-estimation for sample efficiency and robustness
in moment-based estimators.

**Dustin Tran**, Minjae Kim, Finale Doshi-Velez

*Artificial Intelligence and Statistics*, 2016

**Towards stability and optimality in stochastic gradient
descent**

A stochastic gradient method combining numerical stability
and statistical efficiency.

Panos Toulis, **Dustin Tran**, Edoardo M.
Airoldi

*Artificial Intelligence and Statistics*, 2016

**The variational Gaussian process**

A powerful variational model that can universally
approximate any posterior.

**Dustin Tran**, Rajesh Ranganath, David M.
Blei

*International Conference on Learning Representations*, 2016

**Copula variational inference**

Posterior approximations using copulas, which find
meaningful dependence between latent variables.

**Dustin Tran**, David M. Blei, Edoardo M.
Airoldi

*Neural Information Processing Systems*, 2015

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. (See Edward2 in TensorFlow Probability for latest developments.)

Observations provides a one line Python API for loading standard data sets in machine learning. It automates the process from downloading, extracting, loading, and preprocessing data. Observations helps keep the workflow reproducible and follow sensible standards.