Lihong Zhang is a data scientist and software engineer with 12 years of experience building production ML systems and optimizing large-scale infrastructure, currently working at Meta on ads ranking, resource forecasting, and GDPR-compliant ML pipelines. She holds an M.S. in Computational Science from Harvard and a B.S. in Mathematical Physics from the University of Waterloo, blending strong theoretical foundations with practical software engineering. Her work spans real-time ranking for 1B+ DAU, latency and cost tradeoffs in backend systems, and predictive models that reduce idle GPU/CPU capacity. Earlier roles include algorithmic trading analytics, deep-learning recommendation and forecasting for consumer IoT, and research simulating optical systems at IQC—evidence of cross-domain problem-solving. She has contributed to the Apollo autonomous driving open-source project, improving perception and planning integrations and emphasizing code quality in complex systems. Based in Mountain View, she combines rigorous academic training with hands-on systems engineering and a knack for turning experimental models into production impact.
12 years of coding experience
3 years of employment as a software developer
Cross-registration, Cross-registration at Massachusetts Institute of Technology
Master's degree, Computational Science, 3.77/4.0, Master's degree, Computational Science, 3.77/4.0 at Harvard University
Bachelor of Engineering - BE, Environmental Engineering Technology/Environmental Technology, 3.73/4.0, Bachelor of Engineering - BE, Environmental Engineering Technology/Environmental Technology, 3.73/4.0 at Ocean University of China
Bachelor of Science - BS, Mathematical Physics, 3.75/4.0, Bachelor of Science - BS, Mathematical Physics, 3.75/4.0 at University of Waterloo
Contributions summary:Lihong primarily fixed compiling warnings and added configurations related to the map file path within the Apollo autonomous driving platform. The contributions also included changes to perception modules, specifically concerning visualizers and object detection. The code modifications included updates to header and source files, suggesting a focus on code quality and integration within the perception and planning subsystems. The user’s work touched upon core components vital for autonomous vehicle functionality.
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