As a full-stack data scientist, Junchen Feng, MPP'11, PhD'17, is a firm believer in using numbers to evaluate policy programs.
Junchen Feng, MPP’11, PhD’17


Hangzhou, Zhejiang, China


Data scientist,


Quantitative analyst, Kingstree Trading


As a full-stack data scientist, Junchen Feng is a firm believer in numbers as they apply to evaluating programs and data analytics. His aspiration is to promote social improvement through changes in technology and education.

Why public policy?

As an undergraduate student in China, I majored in public policy and minored in world history. In addition to falling in love with policy through watching the West Wing, I sat in on voters’ meetings for nearly 18 months, beginning in 2006. I witnessed many difficult social-policy changes, such as forced relocation, a petition by farmers, and the banning of schools for the children of migrant workers. I learned that a minor change in policy might lead to giant gain or loss to society.

Why Harris?

My impression was that the Chinese government used more experience-based methods in policy-making. Sometimes we were just not able to predict the future based on our limited experience. When I was in college, I never thought I would apply for a PhD program. Before I came to Harris, I never had a chance to use evidence-based research methods. When I first engaged in a research project at Harris, I was surprised how rigorous the faculty’s approach was toward policy research. I was struck by the sincerity and seriousness of purpose I found in academia, which moved me to pursue a PhD degree.

I really liked the diverse community at Harris. Friendship with a Palestinian gave me a new understanding of the Middle East. Another friend who had worked on Capitol Hill for several years inspired me to explore more about the analytical side of policy. Even during our casual chats, he was able to make real-world connections with a theory we had just learned. And I found the career-development office—a service that my undergraduate institution didn’t offer—a huge help.

Harris’ mentoring program was also very important to me. The mentor I had when I was in the MPP program was a big influence on my career choices and plans. He helped me almost as much as my PhD supervisor. Though we’re not in touch as often now, I will always remember what he taught me.

How has Harris’s curriculum and/or your Harris experience been most beneficial to you in your current role?

If I could recommend only one course, it would definitely be UChicago’s academic and professional writing English course (the Little Red Schoolhouse). The most important thing I learned from this course was not writing skills but thinking from the audience’s point of view. Empathy also applies in many professional scenarios, too.

The second course I would recommend is Decisions and Organizations. As a data scientist, I am not a big fan of management theory. But when it comes to decision-making and organizational failure, we all face the same problem: figuring out the incentives behind other people’s decisions and finding a way to achieve a goal as a team. This course reveals the secrets of cooperating in teams.

In addition, working at a start-up company, I basically use the experimental-oriented frame every day. Having a new proposal is the same as formulating a hypothesis: I use collected data to test whether we should follow the proposal. Harris provided me with many resources to help me become the person I want to be.

What big ideas motivate you daily?

What we are doing now is applying the idea of program evaluation to education intervention to build a new evidence-based education system, compared with the old experience-based education system. I love teaching and helping others. This job transfers my passion into a real career.