- Yingjiu Liu earned a PhD in civil and environmental engineering from Duke in 2018. He completed his master’s degree at Columbia University, and his undergraduate degree at Peking University (ranked #1 in China), both in engineering mechanics.
- In his final year at Duke, he won first prize in the student division of the 13th World Congress on Computational Mechanics, for his presentation titled “A Multi-Phase Field Formulation for Cohesive Fracture in Anisotropic Solids with the Application on Shock Wave Lithotripsy.”
- During his time at Duke, Liu explored the diverse places his expertise in computational modeling could take him, completing internships at Idaho National Laboratories and Barclays investment bank.
Q & A: YINGJIE LIU
Why did you choose to pursue a PhD, and why did you choose Duke?
There are two main reasons why I decided to pursue a PhD. First of all, I built a solid theoretical foundation and published several papers during my bachelor’s and master’s studies, so I was well prepared for PhD study. Secondly, I like solving important, difficult problems.
The reason I chose Duke was because of its good research resources and its atmosphere. My advisor, John Dolbow, is an authority in computational mechanics, and the research style in our group is pretty unique; we are only interested in conquering the difficulties that block further developments in our field. Therefore, the work in our group usually has a strong impact after publication.
You had a couple of interesting internships over the course of your studies at Duke. Where did you work, and what did you do?
I did my first internship at Idaho National Laboratory. I was a research intern and developed a module for the laboratory’s in-house software package, which is the major tool the scientists in the lab use to model nuclear fuel. I did my second internship at Barclays investment bank, where I was a quantitative researcher.
I like solving important, difficult problems.
One project I worked on at Barclays was developing statistical models applying to the default time of different financial products and corresponding portfolio losses. (Financial products have strict contracts that define the obligations of their issuers. Usually they require the issuers—whether government or corporate—to pay a certain amount of money to the people who buy their products. However, some issuers may go bankrupt and be unable to pay when their financial products are mature. When this happens, we say those issuers defaulted. Therefore, we need to develop statistical models to predict the probability that the issuer will default, and how long it will take an issuer to default. Then the traders and investors can sell their products before that happens. This is why it is important.)
I also developed a convolutional neural networks-based trading strategy, which is a widely used machine learning model. The historical data of stock prices show clear patterns and signal arbitrage opportunities. By developing a machine learning model and training it using the historical data, we can let the machine perform automated trading. It will not miss signals for arbitrage opportunities.
What are your plans for the future?
To begin, I will be returning to Barclays as a full-time quantitative researcher, where I hope to apply my PhD skills and experiences to some world-class business problems. During my internship at Barclays I noticed that mathematical tools in the world of finance have fallen behind, as compared to the academic world. It would be amazing if I could introduce more advanced mathematical models and computational methods to the world of finance. At the same time, I will seek out opportunities to serve as adjunct faculty; I would like to bring the challenging problems of industry into academia, and expose the students in our department to the challenges faced by large finance companies. In summary, I would like to build a career that mixes academia and industry!
How did Duke prepare you for the work you’ll be doing?
The education and research training I received at Duke helped me build a solid technical background for my future work, but Duke’s attitude towards work taught me a great deal as well. I understand that working hard and finishing the job is only the minimum criteria; we should always aim to develop new methods and make groundbreaking contributions. My experience at Duke taught me that I should always try to be the top performer and game changer, instead of an average employee.
What was your favorite thing about Duke?
Here at Duke, we only focus only on research topics with the potential for strong impact.
The research atmosphere at Duke is my favorite thing. Here at Duke, we only focus only on research topics with the potential for strong impact. Also, we are very conservative with research—we don’t publish work until we are strongly confident in it, and believe that we have very deep understanding of the problems. At the same time, the research atmosphere in Duke is pretty free! My advisor encouraged me to take different useful courses from other departments, and the material I learned in those courses was very helpful to my research.
Do you have any advice for students just entering the PhD program?
My advice is that they should focus on what they are doing, and not think about too much about the future. If they work hard on their courses and projects every day, there will be a good future for them.