Yuina Iseki is a research-focused software engineer with nine years of experience at the intersection of AI, education, and developer tooling. Currently a Research Assistant at Stanford working on LLM-driven patient simulations and financial literacy chatbot evaluation, she combines prompt engineering, user testing, and measurable learning outcomes to improve single-session training. Her background spans academic research—building cognitively inspired multi-agent models and scalable LLM evaluation pipelines—to hands-on engineering, including backend contributions to the open-source MurphySec software supply chain security tool (adding plugin support and Gradle/Maven integration). She has practical EdTech experience building chatbots and interactive web avatars for children, and has mentored learners through AI tinkering and CS teaching roles. Fluent across research and product implementation, she brings a rare mix of pedagogy-aware evaluation metrics and production-ready engineering. Colocating in Palo Alto while pursuing an MS at Stanford, she often bridges human-centered learning design with robust, reproducible AI tooling.
9 years of coding experience
3 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Grinnell College
Bachelor's degree Computer Science, Bachelor's degree Computer Science at DIS - Study Abroad
Humanities/Humanistic Studies, Humanities/Humanistic Studies at Shibuya Makuhari Junior & Senior High School
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Stanford University
An open source tool focused on software supply chain security. 墨菲安全专注于软件供应链安全,具备专业的软件成分分析(SCA)、漏洞检测、专业漏洞库。
Role in this project:
Back-end Developer
Contributions:6 reviews, 1062 commits, 61 PRs in 1 year 1 month
Contributions summary:Yuina contributed to the software supply chain security tool, MurphySec, by implementing new plugin support, along with a "hello" plugin, and integrating support for Gradle and Maven projects. The user focused on fixing bugs such as adding licenses, comments, to-dos, and modifying code comments. They also were involved in refactoring the codebase by splitting files and renaming package names.
Contributions:6 pushes, 1 branch in 4 years 9 months
kotlinevolutionenhancement
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Yuina Iseki - Research Assistant at Stanford NLP group