Jia: A Journey Through Math, AI, and the Humanities

When I first met Jia Wan, a PhD student in EECS at MIT, I wasn’t sure what to expect. She was soft-spoken, a little shy at first, avoiding direct eye contact, but as soon as we started talking about her interests—causal inference, reinforcement learning, fairness in AI, and even stand-up comedy—her energy shifted.

Jia’s journey through academia has been anything but traditional. From a small town in China to Columbia University, then Oxford, and now MIT, she has followed a path that combines deep mathematical theory with real-world applications in social science and machine learning.

She sat across from me, hunched over slightly in a wool ski sweater with brown lines, brown jean-ish pants, boots, and a beanie, her hands moving thoughtfully as she spoke. Her background is a rare balance between theoretical math and a deep appreciation for the humanities, something that has shaped not just her research, but the way she sees the world.

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Jia Wan

Finding a Path: From Columbia to Oxford to MIT

Jia’s academic journey began at Columbia University, where she majored in Applied Math and Computer Science. But what stood out most about her undergraduate experience wasn’t just the STEM courses—it was Columbia’s rigorous humanities core curriculum.

"It was intense," she admitted, laughing. "Fifteen books a semester, one book a day, text recognition tests, constant essays. But it really shaped how I think."

She described long seminars where 60% of the grade came from class discussions—an experience that was both challenging and transformative, especially as an international student whose first language wasn’t English.

"Before Columbia, I was mostly focused on math and science. I didn’t really engage with humanities much. But the core curriculum forced me to think about society, history, and philosophy. It made me a better writer, a better speaker, and more aware of the world outside of STEM."

After Columbia, Jia received a fellowship to study at Oxford, where she transitioned into statistics.

"Oxford was different," she reflected. "No homework, no assignments—just one giant exam at the end of the year that determines everything. It was a very independent way of learning."

It was at Oxford that she discovered her passion for causal inference and experiment design, two statistical approaches that help scientists understand cause-and-effect relationships in complex systems.

Now at MIT, Jia is in her second year of PhD studies, working on reinforcement learning, fairness in AI, and experiment design for social sciences.

Bridging Theory and Real-World Applications

One of the most striking things about Jia is her ability to move between deep theoretical research and practical applications.

"I love theory," she admitted. "But I don’t want to do theory in a vacuum. I want my work to be useful."

Her current research focuses on experiment design and reinforcement learning.

"Let’s say you’re running a huge biology lab, and every experiment costs a ton of money. You can’t test everything—it’s too expensive. So how do you design experiments efficiently to extract the most useful information with limited resources? That’s where my work comes in."

She sees reinforcement learning as a framework for optimizing how experiments are conducted, making scientific discovery more efficient and cost-effective.

But she’s also worked on more ethically complex projects, like fairness in AI. While at Oxford, she cold-emailed professors at Stanford Law School and began working remotely with them on statistical methods to detect racial disparities in machine learning models.

"I liked that project a lot because it was at the intersection of stats, AI, and social impact," she said. "It made me think critically about how algorithms shape real-world outcomes."

PhD Life: Academic Freedom & Self-Motivation

I asked Jia what it’s like being a PhD student at MIT.

"Nobody tells you what to do," she said with a small smile. "You’re your own boss. There’s no syllabus, no step-by-step guide. You have to keep yourself motivated, constantly come up with new ideas, and push your own research forward."

She spends her time thinking about problems, developing mathematical models, running experiments, and collaborating with advisors and peers.

"A PhD is a lot like running your own startup," she mused. "You have full freedom to pursue what interests you. The challenge is making sure your work is meaningful and convincing the academic community that it matters."

Despite the challenges, Jia finds immense joy in research.

"I love the process of solving problems, discovering something new, and seeing how mathematical principles apply to the real world. That’s why I’m here."

Beyond Research: Stand-Up Comedy and Synthesizers

As we talked, I learned that Jia isn’t just about research—she actively pushes herself outside of her comfort zone.

One of the ways she does this? Stand-up comedy.

"I’m not naturally sociable," she admitted. "I’m usually shy, so I wanted to challenge myself to do the most uncomfortable thing imaginable—getting on stage in front of a crowd and being silly."

She first tried stand-up during a research program in Chicago and later took an MIT IAP (Independent Activities Period) class on comedy, taught by a professional comedian.

"It was terrifying, but also really fun," she laughed. "I usually joke about academic life, because, well, it’s what I know best."

She’s also exploring music production and synthesizers, connecting it back to her research on text-to-speech processing.

"Building a synthesizer teaches you the fundamentals of how sound is generated," she explained. "And if you understand that, you can apply it to speech synthesis and AI-generated voices. It’s a fun way to mix science and creativity."

Advice for Future Researchers & Students

Before we wrapped up, I asked Jia what advice she would give to students interested in research and academia.

Follow your interests. "Don’t just do something because it’s trendy. Find what excites you—because research is hard, and if you don’t love it, it won’t sustain you."
Don’t be afraid to pivot. "I started in math, moved into statistics, then AI and experiment design. Changing directions is normal. It’s about finding where you fit."
Have a life outside of research. "Having hobbies, friends, and a support system is just as important as doing good work."
Take initiative. "Cold email professors, explore different fields, and don’t wait for opportunities to come to you. Go after them."
Embrace interdisciplinary learning. "STEM and humanities don’t have to be separate. My humanities education made me a better thinker, writer, and communicator. It helped me see problems in new ways."

The Future: Academia, Ethics, and Possibilities

Jia is fully committed to academia, but she remains open to where her research might take her.

"Right now, my goal is to become a professor and run my own lab. But if I develop something truly valuable, I’d be open to starting something new."

As we ended our conversation, I couldn’t help but admire her perspective—a rare blend of deep technical expertise, curiosity for the humanities, and a willingness to push beyond comfort zones.

Her journey is proof that math, science, and humanities don’t have to be separate—that true innovation happens at the intersection of disciplines.

And maybe, just maybe, a PhD student at MIT can also find time to make people laugh on stage.