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 undergraduate courses prepare students to explore social computing and computational social science, connecting social questions with hands-on work in R. Across the sequence, students build skills in research design, data analysis, and responsible communication through applied projects.
I recommend taking Introduction to Computational Social Science in the second or third year, and Applied Social Network Analysis and Communicating Data through Visualization in the third or fourth year. These are recommendations for planning your studies; course prerequisites are listed below and in the syllabi.
Introduction to Computational Social Science
SOCS-SHU 190 · Recommended: second or third year
This course welcomes students from diverse backgrounds and provides an accessible introduction to computational social science (CSS). Students explore research design and data skills at the intersection of social science and digital innovation, using rich digital data to understand societies and human behavior. Topics include digital trace data collection, social network analysis, and text-as-data, with hands-on work in R. Alongside these opportunities, the course examines data ethics and data-driven bias.
Preparation: No prior coding experience is required; introductory R is taught in the course.
Communicating Data through Visualization
SOCS-SHU 200 · Recommended: third or fourth year
From raw numbers to meaningful visual stories, this course gives students across disciplines the hands-on skills to communicate data with confidence, clarity, and integrity. Using real-world datasets, students learn to manage and clean data, critically interpret tables and figures in research and media, and produce visualizations that communicate evidence responsibly. Ethical and equitable approaches to data communication are woven throughout.
The course uses R, with an emphasis on interpretation and communication. Students also engage critically with AI tools, learning when to accept, modify, or reject AI-generated suggestions. Designed for students in social science, business, and related disciplines, the course connects methods training with data-driven research. Students are encouraged to bring ongoing projects with data interpretation and visualization needs.
Preparation: No prior programming experience is required. Basic R skills and familiarity with descriptive statistics and simple linear regression are helpful but not required. An introductory statistics course is a useful complement.
Teaching recognition
My Introduction to Computational Social Science course was recognized with the Shanghai Municipal Level Undergraduate Key Curriculum Program award (2025). I value students’ feedback on the clarity, patience, and inclusive atmosphere of my classes, and use it to keep my teaching responsive to their needs.
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.