Dustin Tran
Member Of Technical Staff at xAI
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDustin Tran is a Senior Staff Research Scientist at Google DeepMind with 12 years of experience designing and engineering probabilistic and deep generative models. He bridges rigorous statistical foundations and production ML, contributing to core projects like Stan (variational inference refactors), TensorFlow Probability/Edward2, VQ-VAE experiments in Google Research, and the Uncertainty Baselines. His work spans low-level mathematical fixes and templating to higher-level model design and deployment, including packaging and CI automation for Mesh TensorFlow. Based in Mountain View, Dustin combines doctoral-level training at Columbia and Harvard with internships at Google and OpenAI, making him comfortable translating research ideas into maintainable, open-source systems.
12 years of coding experience
7 years of employment as a software developer
Bachelor of Arts (B.A.), Mathematics, Statistics, Bachelor of Arts (B.A.), Mathematics, Statistics at University of California, BerkeleyDoctor of Philosophy (Ph.D.), Statistics, Doctor of Philosophy (Ph.D.), Statistics at Harvard UniversityDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Columbia University in the City of New York
Stackoverflow
479reputation
19kreached
2answers
6questions
Github Skills (38)
bayesian-statistics10
c-language10
probabilistic-programming10
variational-inference10
bayesian-data-analysis10
python10
data-science10
machine-learning10
bash10
cicd10
autoencoder10
bayesian10
deep-learning10
tensorflow10
neural-network10
Programming languages (7)
Github contributions (5)
blei-lab/edward
Feb 2016 - Dec 2018
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Role in this project:
ML Engineer
ML EngineerContributions:29 releases, 1640 commits, 539 PRs in 2 years 10 months
Contributions summary:Dustin's commits center around the development of new model structures. The user implemented a metagraph implementation, including support for random variables and distributions, to be utilized in the probabilistic programming language Edward. The updates demonstrate an understanding of TensorFlow's stochastic graph capabilities to build and train new model structures using the framework. The contributions introduce support for diverse distribution families to Edward's functionality.
inferenceneural-networksmachine-learningprobabilistic-programmingdeep-generative-models
google/uncertainty-baselines
Aug 2020 - Jul 2022
High-quality implementations of standard and SOTA methods on a variety of tasks.
Role in this project:ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:50 reviews, 98 commits, 6 PRs in 1 year 11 months
Contributions summary:Dustin primarily focused on implementing and adapting machine learning models for image classification tasks, particularly within the context of the Uncertainty Baselines project. The user's contributions included porting Edward2 baselines, integrating new uncertainty metrics from github.com/google/uncertainty-metrics, and modifying the training pipeline to accommodate new models and datasets. Furthermore, the user addressed code organization by restructuring modules for different uncertainty metrics.
implementationsstatisticsdata-sciencedeep-learningneural-networks
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