New Haven, CT 06511
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 orveraching 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.
Prior to joining Yale, Tong actively pursued research on machine learning solutions for various real-world challenges. Her work on crime pattern detection was included in Wikipedia Crime Analysis. The ideas from her algorithm was adopted by the New York Police Department’s application Patternizr and has been running live in NYC since 2016. Tong also contributed to the development of an interpretable model for the FICO challenge of credit risk assessment in 2018, outperforming black-box machine learning models and earning the FICO Recognition Award.
|Oct 15, 2023||Paper “Sparse and Faithful Explanations without Sparse Models” has been selected as the winner of the INFORMS 2023 Data Mining Best Paper Award Competition (General Track).|
|Aug 8, 2023||An NSF grant was awarded to Tong as a Co-PI.|
|Aug 6, 2023||Paper “Sparse and Faithful Explanations without Sparse Models” has been selected as one of the four finalists for the INFORMS 2023 Data Mining Best Paper Award Competition (General Track).|