Jie (Sophia) Gao
Jie (Sophia) Gao

Jie (Sophia) Gao

Malone Postdoc Fellow, Johns Hopkins University

About

I will join Monash University as a Lecturer (i.e., Assistant Professor) in Human-Centered Computing in April 2027, where I will build the Interpretive Agentic AI Lab (IAA Lab). I am recruiting 1-2 PhD students for mid to late 2027 or early 2028, as well as visiting students and interns. Please reach out if you want to join!

Join IAA Lab

I am currently a Malone Postdoc Fellow at Johns Hopkins University, where I am mentored by Mark Dredze, Ziang Xiao, and Chien-Ming Huang. I was fortunate to gain multiple kinds of training I currently rely on through postdoc, Ph.D., and visiting student experiences. Previously, I was a Postdoctoral Associate at MIT's SMART program in Singapore, advised by Thomas W. Malone, where I learned collective intelligence and the theoretical perspective of human-AI teams. I received my Ph.D. from SUTD, advised by Simon Perrault, where I gained foundational HCI training. During my Ph.D., I was a visiting student at the University of Notre Dame, hosted by Toby Jia-Jun Li, where I learned to design innovative human-AI collaboration, and at the National University of Singapore, hosted by Shengdong Zhao, where I learned to run rigorous empirical user studies.

AI agents are here.
How do we understand them? How do we control them?

Fundamentally, I am fascinated by how people identify patterns and derive reusable principles from messy, ambiguous, and complex situations and phenomena. Text and code are my entry points into them. This is why I am drawn to analytical methods such as thematic analysis, grounded theory, content analysis, and taxonomy building. These methods help people turn complexity into understanding. To achieve this goal, I use human-AI collaboration to make these methods more accessible, simplified, and supported, while keeping human judgment and reasoning.

Research

Building a science of interpretive work across humans, autonomous agents, and their collaborations.

Agentic AI Multi-Agent Systems Human-AI Collaboration AI-assisted Qualitative Analysis AI-assisted Code Comprehension

Qualitative Analysis

Helping humans understand unstructured text

How can AI support, rather than replace, the interpretive work of reading unstructured text? I design and evaluate systems that help people code qualitative data, align interpretations across multiple coders, and develop theories from raw text, while keeping human judgment central.

Code Comprehension

Helping humans understand code

Real-world codebases are messy, and newcomers often struggle to build an accurate mental model. I build tools that help developers understand unfamiliar code, review AI-generated changes critically, and collaborate with AI assistants without giving up their own judgment.

Beyond these three lines, I also work on other topics such as human-LLM interaction modes, AI-assisted writing, online deliberation, and messaging on smart glasses.

News

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Travel

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Talks

Invited Talks, Guest Lectures & Tutorials

Honors & Services

Fellowships & Awards
Workshop Organizer
Associate Chair / Area Chair
Invited Reviewer

Software

I love building software that helps people solve real problems. I value reproducibility and open source. While maintaining these tools takes effort, I believe it supports real-world applications of research.

Support Codebase Understanding
CodeMap 🏆 ACM SIGSOFT Distinguished Paper Award

A human-AI collaborative tool for code comprehension. Helps developers understand complex codebases through interactive visualization and AI-guided exploration.

2026

CodeMap 🏆 ACM SIGSOFT Distinguished Paper Award

A human-AI collaborative tool for code comprehension. Helps developers understand complex codebases through interactive visualization and AI-guided exploration.

2026

Support Qualitative Analysis
MindCoder ↗ mindcoder.ai

An AI-powered platform for flexible qualitative data analysis. Supports open coding, sub-theme grouping, and theme generation with human-AI collaboration.

2025 – 2026

MindCoder ↗ mindcoder.ai

An AI-powered platform for flexible qualitative data analysis. Supports open coding, sub-theme grouping, and theme generation with human-AI collaboration.

2025 – 2026

CollabCoder

A GPT-powered workflow for collaborative qualitative analysis. Enables multiple researchers to code data together with AI assistance, improving inter-rater reliability and efficiency.

2024

CollabCoder

A GPT-powered workflow for collaborative qualitative analysis. Enables multiple researchers to code data together with AI assistance, improving inter-rater reliability and efficiency.

2024

Life

Fun Facts
🎉 Great! You found this fun fact

My Chinese name is 高洁. Many people don't know how to pronounce "Jie", so please call me Sophia (酥肥鸭, I learned this from Xiaohongshu) to make your life easier 😊

Photos
Cats

Meow! I am a smart cat.

Meow! I am a brave cat.