Ruth Fong

Ruth Fong is a Lecturer in the Department of Computer Science at Princeton University. She joined the department as a teaching faculty member in 2021 after earning her Ph.D. in engineering science from the University of Oxford, where she also received a master's degree. She completed her bachelor's degree in computer science at Harvard University. Her research interests include computer vision, machine learning, deep learning, and explainable AI. She conducts research focused on developing novel techniques for understanding AI models after training, designing new AI models that are interpretable by design, and introducing paradigms for finding and correcting existing failure points in AI models. She received a Rhodes Scholarship in 2015 and an Open Philanthropy AI Fellowship in 2018.

Ruth Fong is a researcher at Princeton University with expertise in Interpretability, Explainable AI, Computer Vision, Machine Learning, and Computational Neuroscience. Her work focuses on developing methods to make AI systems more transparent and trustworthy, with an emphasis on creating interpretable explanations for complex models. She explores how human-ai interaction can be enhanced through explainability, and investigates the role of human brain activity in guiding machine learning. Her research also addresses challenges in concept-based explanations, dataset biases, and the evaluation of visual explanations. Fong's contributions span both theoretical and applied aspects of AI interpretability.