Tony Duan
Staff ML Scientist at Tesla
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTony Duan is a Staff ML Scientist in Palo Alto with 12 years of experience building production-ready machine learning systems, currently leading efforts on Tesla Autopilot. He blends academic rigor from Stanford (MS CS) and UC Berkeley (BS EECS) with hands-on applied research gained at Microsoft Research and Stanford’s ML group under Andrew Ng. At Tesla he progressed from ML Scientist to Staff, shipping perception and decision-making models for autonomous driving at scale. He is an active contributor to probabilistic ML tooling—significantly enhancing the popular ngboost project with survival analysis features, mini-batching, scikit-learn integration, and second-order methods. Tony’s work sits at the intersection of research and engineering: he designs robust, experimentally-driven algorithms and then productionizes them for safety-critical systems. Colleagues describe him as someone who translates advanced statistical ideas into reliable, deployable components.
12 years of coding experience
6 years of employment as a software developer
BS EECS, BS EECS at University of California, BerkeleyMS CS, MS CS at Stanford University
English
Stackoverflow
307reputation
31kreached
0answers
3questions
Github Skills (14)
scikit-learn10
survival-analysis10
machine-learning10
python10
data-science10
gradient-boosting10
scikit10
machine-learning-models9
pytorch9
flink-streaming6
clojure6
apache-flink6
sql6
postgresql6
Programming languages (7)
Github contributions (5)
stanfordmlgroup/ngboost
Jun 2018 - Sep 2020
Natural Gradient Boosting for Probabilistic Prediction
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:1 release, 1 review, 180 commits in 2 years 2 months
Contributions summary:Tony contributed significantly to the Natural Gradient Boosting for Probabilistic Prediction project. Their work included implementing features for survival analysis such as mini-batching and incorporating scikit-learn base learners. They also added data simulation code and included experiments with the SPRINT trial. Furthermore, the user made updates including the addition of second-order methods.
pythonpredictionnaive-bayesnatural-gradientsmachine-learning
tonyduan/transformer-blocks
Jul 2019 - Dec 2022
Contributions:20 commits, 36 pushes in 3 years 5 months
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