Current NYU Shanghai Global Research Initiatives Fellows

Hua-Yu Sebastian Cherng
Associate Professor, Department of Applied Statistics, Social Science, and Humanities, Steinhardt

Synopsis of Research in Shanghai (September 14 - December 11):

Professor Cherng’s research focuses on educational inequalities, and his newer projects use New York City administrative data that is housed by NYU's Research Alliance for New York City School (RANYCS), of which he is a faculty affiliate. As Professor Cherng’s research agenda also includes educational inequalities in China, and particularly the educational lives of rural migrants residing in Shanghai, he is interested in forging a similar relationship between NYU Shanghai / East China Normal University (ECNU) and Shanghai's Ministry of Education. Professor Cherng would use his contacts at NYU Shanghai and ECNU who have existing collaborations with the Ministry to explore the logistics of such an arrangement and how NYU Shanghai can assist the Ministry with data management and analytics. Fall 2020 is a particularly instrumental time for this collaboration as NYU Shanghai has hired one of his colleagues, Xiaogang Wu, who has experience creating these collaborations (such as the one Wu created between the Hong Kong Ministry of Education and his former institution in Hong Kong).

Jingyuan Mo
PhD Candidate, Department of Finance, Stern

Synopsis of Research in Shanghai (September 14 - December 11):

Mo is currently involved in two research projects with his advisor. The first one is on the Chinese bond market, in which they plan to obtain data from the China Foreign Exchange Trade System, located in the Pudong district in Shanghai. The second project is on Chinese business firm groups: the two plan to collaborate with the research team of a data provider company in Pudong, located very close to NYU Shanghai campus. Mo and his advisor hope that their research results on Chinese markets can promote better understanding and inform the development of important policy implications for the bond market in China, as well as its firm structure evolution and connectedness. Due to the scarcity of research papers on these topics of Chinese market, Mo believes that their pioneering work will form a solid foundation for future research on related topics. 

Haifeng Zhang
PhD Candidate, Department of Computer Science and Engineering, Tandon

Synopsis of Research in Shanghai (September 14 - December 11):

Machine Learning has been widely used in many fields as a powerful tool in data processing and analytics. To train a machine learning model, especially for supervised learning, a large amount of labeled data is demanded. While data labeling is time-consuming and labor-intensive, the lack of labeled data has been a big obstacle for the adoption of machine learning techniques. This poses opportunities for computer science researchers to provide algorithms and tools to assist user labeling data efficiently. Zhang’s research is intended to design algorithms and tools to help users label text data in a more efficient and precise way with active guidance from both machine learning and visualization techniques.