Partner content
During her undergraduate research, Liujun Chen examined how people navigate identity across different cultural contexts. Specifically, how Asian American women responded to microaggressions. Through this, she found out her interests lay in the intersection of user behavior, marketing analytics, and data — but she wanted to go deeper.
“I want to work with more complex tools, larger datasets, and more advanced models to tackle questions that traditional social science methods couldn’t fully answer,” Chen says. “Computational social science gave me the framework to do exactly that by combining the behavioral and theoretical grounding from psychology with the computational rigour I wanted to develop.”
That’s when she found the MaCSS program at UC Berkeley. She was drawn to the curriculum, which integrated statistics, computational methods, and social science theory rather than isolating each. “I also really valued the capstone project component,” she says.
The Bay Area location was another big draw. “Being in one of the most active tech and research ecosystems in the world meant opportunities beyond the classroom, whether through networking, internships, or just being surrounded by people working on interesting problems,” she says.
Throughout the program, Chen worked on hands-on, collaborative projects that bridged the gap between computational methods and human questions. She began to take ownership of the research process, from selecting the right methods to navigating the chaos of real datasets.
Chen is now confident to start building a career that focuses on using data-driven methods to improve user experience and drive measurable business outcomes.