Sharon Zhou
Vice President Of Artificial Intelligence at Coursera
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSharon Zhou is a Vice President of Artificial Intelligence and founder-turned-executive with 12 years building and teaching generative AI, currently leading a team that transitioned her startup Lamini into AMD. She completed a PhD at Stanford under Andrew Ng, created an early generative AI course, and has taught AI to nearly a million learners through Coursera and live events, blending rigorous research (NeurIPS top paper) with accessible explanations. As CEO of Lamini she raised >$25M and developed novel methods to reduce LLM hallucinations using agentic data workflows and mixture-of-expert LoRAs, work that complements her contributions to the BIG-bench benchmark on calibration metrics. An active angel investor and speaker, she moves fluidly between startups, policy, and enterprise adoption, and brings a humanities-minded perspective from a Classics background—aiming to build ideas that endure.
12 years of coding experience
11 years of employment as a software developer
Acton-Boxborough Regional High SchoolJoint B.A. (double major) Classics and Computer Science, Joint B.A. (double major) Classics and Computer Science at Harvard UniversityDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Stanford University
French, Chinese, Greek, Latin, Greek
Github Skills (9)
workbench10
machine-learning10
testbench10
calibration10
python10
natural-language-processing9
nlp9
pytest8
tensorflow5
Programming languages (9)
Github contributions (5)
google/BIG-bench
Jun 2021 - Jul 2021
Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models
Role in this project:
Data Scientist
Data ScientistContributions:3 reviews, 13 commits, 2 PRs in 1 month
Contributions summary:Sharon focused on improving metrics and calibration within the `big-bench` project, a benchmark for language models. Their contributions primarily involved modifications to the `task_metrics.py` file, implementing and refining Brier score calculations for evaluating model performance. The user added tests, fixed bugs, and improved the documentation related to the scoring functions. They also ordered and normalized target scores to improve calibration metrics accuracy.
language-model
sharonzhou/generator
May 2019 - Jan 2020
Contributions:62 commits in 8 months
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