Karen Vo
Incoming Systems Software Intern at Stanford Artificial Intelligence Laboratory (SAIL)
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolKaren Vo is an emerging systems software engineer with 20 years of professional experience who blends AI/ML research and robotics systems work with a strong track record in healthcare and bioinformatics applications. Currently an incoming systems software intern at NVIDIA and researcher at Stanford SAIL, she has shipped robot streaming and re-identification features at Boston Dynamics and built a full-stack medical data pipeline that produced a 91% accurate prognostic model for AML. Her open-source backend contributions to the Sakai assessment tool show practical experience across database schema changes and assessment feature engineering. Comfortable moving between research labs and production teams, she focuses on robust systems that bridge real-world sensing, patient data, and machine learning. Notably, her background spans applied genomics research and biodesign projects, giving her a rare mix of domain depth in both healthcare and robotics.
20 years of coding experience
2 years of employment as a software developer
San José State UniversityEvergreen Valley CollegeSilver Creek High SchoolBachelor of Science - Computer Science, Bachelor of Science - Computer Science at Stanford University
English, Vietnamese
Github Skills (5)
javas10
back-end-development10
java10
sql9
database-design8
Programming languages (3)
Github contributions (5)
sakaiproject/sakai
May 2006 - Aug 2014
Sakai is a freely available, feature-rich technology solution for learning, teaching, research and collaboration. Sakai is an open source software suite developed by a diverse and global adopter community.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1401 commits in 8 years 3 months
Contributions summary:Karen appears to be primarily contributing to the back-end functionality of the Sakai assessment tool, focusing on adding features and fixing bugs related to question types within the Samigo module. This includes modifications to database schemas and data structures used in the assessment features. The contributions are concentrated in areas such as enhancements to assessment settings, as well as fixes to existing features with the addition of new question types.
diversesakaivlefeature-richfreely
stanfordlts/mediathread
Nov 2021 - Sep 2024
Django application for multimedia annotations facilitating collaboration on video and image analysis. Developed at the Columbia University Center for Teaching and Learning.
Contributions:5 PRs, 96 pushes, 1 comment in 2 years 10 months
centerimage-analysispythoncolumbia-universityannotations
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