Hao Zhang, 27, doctoral student
Zhang earned his doctoral degree in mechanical and aerospace engineering this spring. In addition, he also completed two certificates: The Graduate Certificate Program in Statistics and Machine Learning at the Center for Statistics and Machine Learning (CSML), and the Graduate Certificate Program in Computational Science and Engineering.
He’s been at Princeton since 2014. Before that, he earned a bachelor’s degree from Peking University in Beijing. He double-majored in engineering structure analysis and economics.
Zhang’s primary research at Princeton has been focused on data-driven modeling for fluid dynamics and control with Clarence W. Rowley, professor of mechanical and aerospace engineering, as his advisor.
Zhang’s thesis is titled “Data-driven modeling for fluid dynamics and control.”
“I studied the method of dynamic mode decomposition (DMD) along with its variants, and their application to empirical spectral analysis of dynamical system (typically fluid data) for the purpose of identifying characteristic dynamic property/behavior,” said Zhang.
This translates to creating mathematical models for complex, dynamic systems (often of fluids) by using data from experiments and simulations. And from these data-driven models, he seeks to manipulate the state of the system and make the system perform better through control design/theory. An example of this is looking at an air foil, an abstract representation of airplane wings, and see how fluid like air would flow around the air foil, and how fluid mass flux actuator could be used to control the flow field. Sensors would be placed on the surface of the air foil, measurements would be taken, and a data-driven model would be generated from these measurements. From there, one can apply control theory to teach the fluid actuator how to control the flow and decrease the flow separation.
Zhang’s project proposed an online learning algorithm that allows engineers and scientists to update the mathematical model in real time.
“We build the model from prior data and now we have new measurements from the sensors to update the model,” he said. “It’s a very fast online optimization method.”
Applications for his project can be in aerospace, or anywhere in general where a model of a particular form needs to be learned and updated, Zhang said.
Zhang said he enjoyed his years at Princeton and his CSML work because they all added to his research tool kit.
“For me, I wanted to learn how nature works when I came to Princeton. I like discovering how different things work, that has always excited me,” said Zhang. “Then after I arrived, my motivation became more concrete. I wanted a rigorous scientific background that could be not only applied in seeking your doctoral degree but to also industry and other areas. The skills obtained for scientific research will always help you, wherever you are working.”
During his journey to obtain his doctoral degree, Zhang served as an intern at Alibaba Group and Quest Partners LLC.
After his time in academia, Zhang decided to enter industry. In March, he started work as a natural language processing research scientist at Bloomberg LP.
When he was a student in China, he volunteered as a mentor and organizer for motivational summer camps for high school students, and as a student researcher and organizer on a program mentoring China’s rural left-behind children, whose parents leave the countryside for work in cities. At Princeton, he was the vice president for the Association of Chinese Students and Scholars.
Zhang enjoys playing tennis and swimming.