Student Spotlight: Jongbin Jung
July 1, 2017
Meet Jongbin Jung, a 3rd-year MS&E PhD student studying decision analysis and computational social science.
Jongbin studied business administration and operations research at Yonsei University in South Korea, and came to MS&E in 2014 to work with his advisor Ron Howard. In the summer of 2016, he interned at Google as a quantitative analyst intern.
"The diversity in MS&E with respect to people and research areas is so overwhelming... But I think it’s worth exploring all the different opportunities, at least for a quarter or two, to take full advantage of the unique experience that MS&E has to offer."
Why did you choose Stanford MS&E?
The opportunity for multidisciplinary research was most attractive to me. I wanted to make the most out of my background in management, computer science, and operations research, and MS&E seemed to have the perfect mix of courses, faculty, and concentrations.
How did you become interested in your research area?
Helping people make challenging and important decisions is the most fulfilling goal for me. I’m also excited about making sense of complicated systems and finding insights from large amounts of data. I guess it was natural for me to work on connecting the two.
What problems are you targeting in your research? What are some possible applications?
I want to see if we can extract transparent and interpretable rules from complicated analytic methods, e.g., black-box machine learning techniques, that will help us make better decisions.
The primary application for me is risk assessment in the criminal justice system. There is a large movement to use machine learning predictions to assess the risk of defendants in the criminal justice system, with the final goal of using these risk assessments to inform judge decisions such as setting bail. I think it’s potentially harmful to use black-box tools, where no one really understands how and why predictions are being made as they are, for such important decisions.
I’ve proposed methods to convert these complex models into simple rules that may not be as accurate as their black-box counterparts, but have the advantage of being interpretable by the human decision maker.
What interests you most about your research?
The impact it may have on policy decisions in the near future. I find it scary, rather than interesting, how quickly important decisions that could have a huge impact on society as a whole are being delegated to machine learning tools that we don’t quite understand.
What other activities are you involved with on campus?
Aside from research and spending weekends with my three-year-old daughter, I’ve been offering introductory crash courses to various computation tools and programming languages for fellow students. I’m also working on a number of open source projects with other students, none of which are developed enough to talk about in detail, unfortunately.
What will you be doing after Stanford?
There are so many opportunities and uncertainties, I can’t say for sure. I’ve enjoyed interning as a data/quantitative analyst during summers, and wouldn’t mind doing that full-time. But I also like research and teaching enough that part of me wants to stay in academia.
Surprisingly, I find that the skills that I am developing at MS&E prepare me equally for both paths, and one of the beauties of being a student is that I don’t have to make that decision yet.
Any words of advice to incoming students?
The diversity in MS&E with respect to people and research areas is so overwhelming. Incoming students might be tempted to just focus on their own work, fearing that otherwise they’ll never get anything done! But I think it’s worth exploring all the different opportunities, at least for a quarter or two, to take full advantage of the unique experience that MS&E has to offer.