Philosophy
My teaching is centered on creating an inclusive and engaging environment where students from all backgrounds can build confidence and skill in computational social science. I believe in connecting theory to practice, so my courses blend rigorous instruction with hands-on, collaborative projects that tackle real-world social issues. My goal is to foster both technical mastery and critical thinking. As our field evolves, so does my classroom. I guide students to use cutting-edge tools like generative AI responsibly, cultivating practical skills alongside a deep awareness of ethical considerations and potential biases. Mentoring students as they develop into independent researchers is a core part of my work, and I am passionate about supporting their growth. Ultimately, I see teaching as a collaborative journey, and I warmly invite all who share a curiosity for using data to understand our world to join me.
My Courses
My course offerings, including Introduction to Computational Social Science and Applied Social Network Analysis, are designed to bridge social theory with computational practice. Each course emphasizes reproducible coding, ethical data handling, and project-based learning, equipping students not just with technical skills but with a framework for responsible and impactful inquiry. Whether in an undergraduate seminar or an advanced workshop for junior scholars, my goal is to foster a deep understanding of how to choose the right method to answer a meaningful research question. I am honored that my Introduction to Computational Social Science course was recognized with the Shanghai Municipal Level Undergraduate Key Curriculum Program award (2025). More importantly, I am consistently heartened by feedback from students, who often highlight the clarity, patience, and inclusive atmosphere of my classes. I take all feedback seriously, using it to reflect on my practice and ensure my teaching remains responsive to the diverse needs of every learner.
Teaching with and about AI
In my courses, students learn to leverage generative AI as a powerful co-pilot for coding and analysis, but they also learn to approach it critically. We actively discuss and analyze issues of algorithmic bias, fairness, and the broader social implications of deploying AI in society. This dual approach prepares them to be both skilled users and thoughtful, ethical critics of these transformative technologies.
Selected student feedback
“Explains very complicated concepts to people who have never coded before, with tailored support in office hours.” (Introduction to CSS, Fall 2024).
“Excellent in tailored feedback, kind and patient, very intelligent.” (Introduction to CSS, Spring 2025).
“Integration of AI was really helpful—practiced using it to understand why code works in context.” (Introduction to CSS, Spring 2025).
“Most valuable: how to identify problems and choose methods to fit the research question.” (SNA Workshop).
Mentorship and Prospective Students
My mentorship experience includes undergraduate and graduate students across education, data science, psychology, and urban studies, with mentees moving on to graduate study and research roles at leading institutions.
I am happy to hear from prospective graduate or undergraduate students interested in working on projects related to computational social science, AI, and education. Before reaching out, please review my current research projects to ensure our interests align. If they do, please email me with a brief description of your background and your research interests.