Hanjun Dai
Research Scientist at Google
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolHanjun Dai is a research scientist and manager with 14 years of experience building and shipping cutting-edge ML research at Google, DeepMind, and Google Brain, with a PhD in Machine Learning from Georgia Tech. He leads teams on reinforcement learning, tool use, and reasoning with large language models, and served as Gemini RL post-training and pro model release captain for high-profile product launches showcased at Google I/O and Cloud Next. His research spans generative modeling, sampling, and optimization, with influential open-source contributions to the google-research repo including scalable deep generative models for sparse graphs and discrete energy-based models. Recognized with a Google Research Tech Impact Award, he combines rigorous academic work on graph and temporal models with hands-on engineering to move papers into released code and demos. An often-overlooked strength is his track record across both internal product-facing model releases and foundational research, bridging reproducible research and production-grade ML systems.
13 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Machine Learning, Doctor of Philosophy (Ph.D.), Machine Learning at Georgia Institute of TechnologyBachelor of Science (B.S.), Computer Science, Bachelor of Science (B.S.), Computer Science at Fudan University
Chinese, English
Github Skills (9)
machine-learning10
pytorch10
graph-algorithms10
deep-learning10
autoregressive-models9
tensorflow9
grapher8
graph8
graphing8
Programming languages (4)
Github contributions (5)
google-research/google-research
Jul 2020 - Nov 2022
Google Research
Role in this project:
ML Engineer
ML EngineerContributions:12 commits in 2 years 4 months
Contributions summary:Hanjun primarily contributed to the `google-research/google-research` repository, which focuses on AI and machine learning research. The commits demonstrate the release of code for a paper on scalable deep generative modeling for sparse graphs, including code related to graph structures and algorithms. Further contributions include releasing demo and fairness teaching code, and code related to a NeurIPS 2020 paper on discrete energy-based models, as well as the video timeline modeling.
googlemachine-learningai
Hanjun-Dai/graph_adversarial_attack
Jun 2018 - Jul 2022
Adversarial Attack on Graph Structured Data (https://arxiv.org/abs/1806.02371)
Contributions:18 commits, 13 pushes, 1 branch in 4 years 1 month
pytorchadversarial-attacksarxivabsstructured
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