Hui Fen (Sarah) Tan

Sarah Tan is a Principal Research Scientist at Salesforce, focusing on AI safety, causal inference, interpretability, and healthcare. She holds a Visiting Scientist appointment at Cornell University’s College of Computing and Information Science.

Education:

• PhD in Statistics (Cornell University)
• Studied at Berkeley and Columbia

Her research spans interpretable machine learning, responsible AI, and healthcare data analysis, with a focus on auditing black-box models and improving transparency. She has co-organized workshops and delivered lectures on AI ethics and trustworthy machine learning.

Recent publications highlight her work on model interpretability, GAMs, influence embeddings, and healthcare applications. She has received two Best Paper Awards from the American Sociological Association’s CITAMS and CBSM sections.

Scientific Awards:

• Best Paper Award, CITAMS section
• Best Paper Award, CBSM section

Her career includes roles at Facebook, UCSF, Microsoft Research, and public policy work in NYC. She actively participates in academic service as an Area Chair for NeurIPS, FAccT, and other conferences.