Tong Wang

Tong Wang is an Assistant Professor of Marketing at Yale University's School of Management. Her research focuses on creating novel interpretable models to analyze structured and unstructured data, such as texts and images, with the goal of extracting valuable insights to support informed decision-making. Wang's work emphasizes transparency in decision-making processes and has been applied in real-world contexts, including crime pattern detection and the FICO challenge. She received her Ph.D. in Computer Science from MIT and has contributed to the development of interpretable machine learning solutions for business problems.

Tong Wang is a researcher at Yale University with expertise in interpretable machine learning, large language models, reinforcement learning, and quant marketing. Their work focuses on developing interpretable models for financial lending, crime pattern detection, and marketing campaigns. Wang's research includes Bayesian frameworks for rule sets, hybrid predictive models, and causal rule sets for treatment effects. They also explore paralanguage classification and network coding for multiple two-way relaying channels. Their publications highlight the integration of interpretability with black-box models and the application of machine learning in real-world domains such as healthcare and criminal justice.