Huilian Sophie Qiu

Social Computing · Network Research

I develop computational methods to understand how language and social structures shape scientific innovation.

Assistant Professor · Society Hub · HKUST (Guangzhou)

Innovation, Entrepreneurship and Public Policy

Explore my research↘
01 / BIO

Huilian Sophie Qiu is an Assistant Professor in the Innovation, Entrepreneurship and Public Policy Thrust of the Society Hub at the Hong Kong University of Science and Technology (Guangzhou). She previously served as a Junior Group Leader in the Department of Computer Science at the University of Göttingen and as an Assistant Professor in the Department of Computer Science at Northwestern University. Her research spans natural language processing, computational social science, and software engineering. She received her Ph.D. in Computational Sociology from Carnegie Mellon University and studied at Princeton University and Brown University for her master’s and undergraduate degrees, respectively, across the fields of computer science and East Asian studies. Grounded in empirical data, her research investigates how scientific language shapes research funding, open-source collaboration, and technological innovation. Her work has appeared in leading international journals including PNAS and JAMA Network Open. She has received an ICSE Distinguished Paper Award and a Journal of Social Computing Outstanding Reviewer Award.

02 / RESEARCH

Research directions

I study science as both a system of ideas and a system of people.

01

AI for science & innovation

Computational and AI methods for understanding and supporting discovery, evaluation, and innovation.

02

Scientific communication

How language influences research funding, peer review, and the adoption of innovative ideas.

03

Open-source communities

Collaboration, participation, social capital, and community health in software ecosystems.

04

Computational social science

NLP, network analysis, machine learning, and statistical methods for large-scale behavioral data.

03 / WORK
04 / STUDENTS

Prospective Ph.D. students

I welcome curious, independent researchers who want to use computational methods to study science, innovation, online communities, and human-centered AI.

01

Good research fit

NLP and large language models · computational social science · open-source communities · network analysis · large-scale behavioral data

02

What I value

Intellectual curiosity, independence, persistence, strong English proficiency, and a willingness to learn and think beyond AI-generated answers.

03

When you write

Please read several recent papers and include a concise description of your interests, the specific connection to my work, and your CV.