Zi Wang
Senior Research Scientist at Google DeepMind
Cambridge, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolZi Wang is a Staff Research Scientist based in Cambridge, MA with 11 years of experience advancing probabilistic and frontier AI research across Google Brain and DeepMind. Trained at Tsinghua and MIT (PhD), Zi blends deep theoretical expertise with production-oriented engineering—evident from contributions to Google's uncertainty-baselines repo where they refactored and extended active learning pipelines and hyperparameter sweeps. They teach advanced ML topics at Harvard, connecting cutting-edge research to graduate education, and have a track record in applied ML from autonomous driving internships to large-scale research at Google. Colleagues know Zi for shipping robust experimental infrastructure and for a pragmatic focus on uncertainty and data acquisition strategies that bridge research and internal product needs.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Massachusetts Institute of TechnologyBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Tsinghua University
Github Skills (13)
machine-learning10
deeplearning-ai10
deep-learning10
tensorflow10
python10
active-learning10
flax9
jax9
artificial-neural-networks9
neural-network9
bayesian-methods7
statistics6
probabilistic-programming6
Programming languages (4)
Github contributions (5)
google/uncertainty-baselines
Jan 2022 - May 2022
High-quality implementations of standard and SOTA methods on a variety of tasks.
Role in this project:
ML Engineer
ML EngineerContributions:10 reviews, 23 commits, 6 comments in 3 months
Contributions summary:Zi primarily contributed to the active learning module within the uncertainty-baselines repository, making significant modifications to the `active_learning.py` file. Their work involved refactoring the active learning code, adding support for internal use, and updating hyperparameter sweeps. They implemented features related to acquiring data points using various methods and integrated profiler and parameter logging. The user demonstrated expertise in active learning techniques within the context of deep learning models.
implementationsstatisticsdata-sciencedeep-learningneural-networks
zwyls/zwyls.github.io
Sep 2015 - Nov 2022
Contributions:118 commits, 123 pushes, 1 branch in 7 years 3 months
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