Tong Wang’s research interests are in developing machine learning solutions for business problems. Her work focuses on creating novel interpretable models that can effectively represent and analyze structured and unstructured data, such as texts and images. The overarching objective of these interpretable models is to extract valuable insights from the data, empowering stakeholders to make well-informed decisions while also facilitating a clear understanding of the decision-making processes employed by the models.
Tong received her Ph.D. in Computer Science from Massachusetts Institute of Technology. Prior to joining Yale, she actively pursued research on machine learning solutions for various real-world challenges. Her work on crime pattern detection has been included in Wikipedia Crime Analysis and gained media coverage. The ideas from her algorithm have been implemented by New York Police Department. Tong contributed to the development of an interpretable model for the FICO challenge in 2018, outperforming black-box machine learning models and earning the FICO Recognition Award.