Computer Science Ph.D. Student at Columbia

dustin@cs.columbia.edu

Blog

I am a Ph.D. student in Computer Science at Columbia, where I am advised by David Blei and Andrew Gelman. I work in the fields of Bayesian statistics, machine learning, and deep learning. In particular, my research interests are in probabilistic models, approximate inference, and generally foundations of statistical analysis.

I am a developer for Edward, a library for probabilistic modeling, inference, and criticism. I am also fortunate to be a member of the Stan development team. Recently, I transferred to Columbia from a Statistics Ph.D. at Harvard, where I worked with Edo Airoldi and also spent time at the Harvard Intelligent Probabilistic Systems group.

Some of my work is available as preprints on arXiv.

**Discussion of "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*, To appear

**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

**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

**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

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. Check it out on Github.

Stan is a probabilistic programming language featuring fast, highly optimized inference algorithms. It supports a large class of models, with a user base of roughly 10,000. Alp Kucukelbir and I are developing variational inference. I also work on algorithms for optimizing marginal distributions. Check it out on Github.

We build large-scale estimation tools in R using stochastic gradient descent, available on CRAN. The library I'm developing with Panos Toulis includes a slew of stochastic gradient methods, built-in models, visualization tools, hypothesis testing, convergence diagnostics, and other cool stuff. Check it out on Github.

- Twitter Cortex — Cambridge, MA, 2016
- Google Brain — Mountain View, CA, 2016

- International Conference on Learning Representations — San Juan, PR, 2016
- NIPS Workshop: Advances in Approximate Bayesian Inference — Montreal, CA, 2015
- NIPS Workshop: Black Box Learning and Inference — Montreal CA, 2015

- Harvard University — Cambridge, MA, 2015
- Massachusetts Institute of Technology — Cambridge, MA, 2015
- Harvard University — Cambridge, MA, 2015