Kejia Wang is an AI Scientist with 11 years of experience building and deploying large-scale, end-to-end ML systems that turn multi-modal and unstructured data into actionable insights. Based in Mountain View, she has moved between startups, healthcare, and enterprise settings—driving RAG/agent document intelligence, fine-tuning and deploying LLMs for clinical clients, and developing ML models for early Parkinson’s diagnosis. Kejia blends full-stack engineering chops (contributions to the Kylin OLAP project across frontend and backend) with product-facing responsibilities such as stakeholder communication and data pipeline optimization. Her background in data processing and public policy data science informs a pragmatic approach to model evaluation, causal thinking, and scalable system design. Notably, she has experience translating complex research and clinical requirements into production-ready solutions across cloud environments.
11 years of coding experience
3 years of employment as a software developer
Master of Science - MS Data Processing, Master of Science - MS Data Processing at University of San Francisco
Master of Public Policy Data Science, Master of Public Policy Data Science at University of Chicago
Beijing 101 Middle School
Bachelor's Degree Business Administration Management and Operations, Bachelor's Degree Business Administration Management and Operations at Renmin University of China
Cross Registeration Marketing & Casual Inference, Cross Registeration Marketing & Casual Inference at The University of Chicago Booth School of Business
Summer Program Ecomomics, Summer Program Ecomomics at University of Cambridge
This code base is retained for historical interest only, please visit Apache Incubator Repo for latest one
Role in this project:
Full-stack Developer
Contributions:21 commits in 22 days
Contributions summary:Kejia primarily contributed to the frontend and backend components of the Kylin project. They implemented a job status multiple checkbox feature by modifying the frontend UI, adding filters and other UI-related functionalities, and by integrating the filtering functionality into the controller. They also participated in backend changes involving database interactions, project management and updating cube descriptors. They updated the code for handling cube updates and permissions.
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