opportunities

Workstyle

I have been greatly benefiting from “apprenticeship”-style mentorship throughout my academic training and professional career. As a result, I am a strong supporter and practitioner of this mentoring approach.

This style emphasizes close collaboration between mentor and mentee, working side-by-side on real projects, thinking through problems together, and learning by doing. It allows for deep intellectual exchange, personalized guidance, and long-term professional development.

Of course, this approach has its trade-offs. On the pro side, it fosters meaningful collaboration, accelerates skill acquisition, and builds lasting scholarly relationships. On the con side, it limits the number of students I can closely work with at any given time.

I prioritize quality over quantity in mentorship. If you are looking for a highly engaged, hands-on advisor who will treat your success as a shared project, this mentoring style may be a good fit for you.

Current Directions (updated: Fall 2025)

Computational Methods for Social Science Research and Nonprofit Sector

This line of research has both academic and applied focuses. On the academic side, I focus on how to integrate computational methods into social science research (or so-called Computational Social Science). My approach is to find out the fundamental frameworks for using different computational methods, starting from the research design perspective. Example studies include:

• Computational Social Science Methods: A Research Design Primer, a methodology book project under contract with Sage.
• Computational Basis of LLM’s Decision Making in Social Simulation
• Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games

On the applied side, I focus on how to responsibly and effectively apply advanced technologies. Example studies include:

• Integrating Transparent Models, LLMs, and Practitioner-in-the-Loop: A Case of Nonprofit Program Evaluation
• Towards Artificial Intelligence for the Public Sector: Framing and Bridging Academia and Practice.
• Automated Coding Using Machine Learning and Remapping the U.S. Nonprofit Sector

Knowledge Production for Policymaking and Nonprofit Studies

I treat “knowledge” itself as an object for research and study how it is produced, distributed, and utilized in policymaking and nonprofit studies. I often use the sociology of knowledge as my theoretical base and adopt various computational methods to answer substantive research questions. Example studies include:

• Sources of Evidence for Evidence-Based Policymaking: Journals, Articles, and Scholarly Structure in the Economic Report of the President, 2010-2025.
• Why do some academic articles receive more citations from policy communities?
• Neutral, Non-Disruptive, and Native: Why Do Chinese Nonprofit Scholars Cite English Articles?
• Consensus formation in nonprofit and philanthropic studies: Networks, reputation, and gender

State-Society Relations in Restrictive Contexts

My doctoral dissertation and initial research builds on studying the NPO/NGO sector in China, and its relations with the party-state. For example:

• State power and elite autonomy in a networked civil society: The board interlocking of Chinese non-profits
• Bridging state and nonprofit: Differentiated embeddedness of Chinese political elites in charitable foundations
• How does an authoritarian state co-opt its social scientists studying civil society?

For various reasons, this line of research is not as fruitful as I expect in the past few years. But I’m determined to continue this direction on two fronts:

• Qualitative fieldwork at grassroots level. I did a fieldwork in a rural village in 2019, planning to write a qualitative research paper and make a documentary (see a trailer). Unfortunately none of the purposes has been fully realized. I’m working on writing the stories I collected as a non-fiction book in Chinese.
• More countries other than China. Something on my radar, but no concrete plan and roadmap yet.