Qingyun Wu
Founder And CEO at Penn State University
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolQingyun Wu is a founder and CEO based in the San Francisco Bay Area with 11 years of experience building AI systems that enable collaborative, agentic behavior. As founder of AG2 and an active maintainer and contributor to high-profile open-source projects like Microsoft's Autogen and FLAML, he blends applied research with production-grade engineering—adding new algorithms, improving AutoML tooling, and hardening model training pipelines. He also serves as an assistant professor at Penn State, bringing academic rigor and mentorship to his entrepreneurial work and research background from a PhD in Computer Science. His industry experience includes research roles at Microsoft and internships at Adobe and Yahoo, where he focused on reinforcement learning and bandit problems. Notably, he contributes to widely used frameworks that accelerate agentic AI development, bridging the gap between cutting-edge research and practical developer tooling.
11 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Telecommunications Engineering, Bachelor's degree, Telecommunications Engineering at Xidian UniversityDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of Virginia
English, Chinese
Github Skills (21)
algorithm10
algorithms10
xgboost10
python10
machine-learning10
autogen10
hyperparameter-optimization10
trainings10
automl10
lightgbm10
hyperparameter-tuning10
automated-machine-learning10
modeling10
data-science9
catboost9
Programming languages (4)
Github contributions (5)
microsoft/FLAML
Feb 2021 - Jan 2023
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Role in this project:
ML Engineer
ML EngineerContributions:4 releases, 527 reviews, 147 commits in 1 year 11 months
Contributions summary:Qingyun primarily contributed to the development and enhancement of the FLAML AutoML library. Their work focused on improving core functionalities, including adding seed arguments to search algorithms, custom learners, and addressing various bug fixes. The user's contributions involved integrating new features and improving the overall performance of the AutoML system. They also worked on updating documentation and addressing compatibility issues.
fairness-mlpythonfinetuningdiscordjupyter-notebook
microsoft/autogen
Aug 2023 - Aug 2024
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Role in this project:ML Engineer
ML EngineerContributions:9 releases, 362 reviews, 227 PRs in 11 months
Contributions summary:Qingyun primarily contributed to the development and enhancement of a machine-learning framework, as indicated by modifications to model training, and the introduction of new search algorithms. The user integrated new methods to refine model training, and integrated new algorithms and capabilities. In addition, the user addressed bugs by providing error handling and code corrections. These contributions suggest the user is focused on improving the functionality, and performance of the models.
agenticagentic-agiagentsaiautogen
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