Stephanie Wan

Stephanie Wan is a research fellow at MATS, where she works on AI alignment and interpretability. Her projects include research on how language models represent personality and an interpretable authorship-verification system; she has also built AI and healthcare projects, including a conversational tool for personalized genetic risk information. She studies computer science and mathematics at Yale.

Career

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Based in: Lexington
Elsewhere

MATS Research

Sep 2026 — Present

Research Fellow

Two Sigma

Jun 2026 — Aug 2026

Quantitative Developer Intern

Ramp

Mar 2026 — May 2026

Applied AI & Interp. Research Intern @ Ramp Labs ❤️

Yale University

Bachelor's studies in software

Lexington High School

Products & media

github.com

Transparent Authorship Verification

Her research code and interactive demo compare embedding and stylometric approaches to authorship verification, with interpretable predictions and stated evaluation limits.

swan.one

Research and Publications

Explore work on language models, interpretable authorship verification, AI applications in healthcare, and research on AI’s effects in English classrooms.

Writing

swan.one

Projects

A portfolio of projects spanning custom hardware and software, interactive tools, robotics, and competitive programming.

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Stephanie Wan

MATS Research

Two Sigma

Ramp

Yale University

Lexington High School

Products & media

Transparent Authorship Verification

Research and Publications

Projects

Curated Lists

List Inclusions