I’m Sijin. I grew up in Singapore from the age of 9, away from my parents in pursuit of a better education. It was adventurous, and my life has been exciting since. To pay for college, I worked as an influencer (I got more than half a million views on one video, so I was serious!) and co-founded an online education company, growing it to more than 100 employees within a year—at the age of 20. The company provided tailored revision plans for high school students in China that took into account not only academic content but also motivational elements.

It was the first time I became intrigued by the question: How can I better motivate people? That curiosity led me to explore the literature on motivation and goals. I remember creating “if-then” pamphlets for students and feeling thrilled when they told me the materials helped. That experience planted seeds for what would later become my research path. Four years later, I was studying motivation and goals with Professor Ayelet Fishbach. Today, I work with her as a Principal Researcher at the University of Chicago, after earning my PhD in Marketing from the National University of Singapore.

Another thing I learned from the early experience? I love figuring out problems I encounter in daily life. I like uncovering the psychological mechanisms behind everyday behavior, and I also love applying those findings in useful ways. Like the saying goes, it should be something you can explain to your grandma over dinner.

For example:

• When you’re choosing a movie to watch, what kind of reviews should you read? Short answer: Read the negative ones (skim the positives) because they are more precisely written.
• How has movie content changed over time and across cultures? Are we oversaturated with Marvel? Short answer: Kind of. More movies are featuring adventurous events, while romance films have declined steadily over the past 20 years.

​The process of finding answers is always rewarding. Trained in judgment and decision-making and social psychology, experiments are our bread and butter. But I also like some jam—so I learn new methods.

I’ve used natural language processing (NLP) to quantify the precision of writing in positive vs. negative reviews, and large language models (LLMs) to explore how movie content has evolved across cultures and time. By extracting common events featured in films using a bottom-up approach, I try to understand the broader cultural shifts embedded in entertainment.  

My curiosity about new technology has also branched into research questions like:

• Do people evaluate an AI that slows down on moral-tradeoff more favorably than an AI that does not? 
• How do people attribute the motivations underlying the same purchase recommendation when it comes from AI vs. a human? 

I look forward to exploring more.