Interpretability as the Inverse Machine Learning Pipeline

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• Prof. Sarah Wiegreffe (University of Maryland)
• Wednesday 12 November 2025, 15:00-16:00
• https://cam-ac-uk.zoom.us/j/97599459216?pwd=QTRsOWZCOXRTREVnbTJBdXVpOXFvdz09.

If you have a question about this talk, please contact Lucas Resck.

Bio: Prof. Sarah Wiegreffe is an natural language processing and machine learning researcher and an assistant professor in the Department of Computer Science at the University of Maryland. She works on the explainability and interpretability of deep learning systems for language, with a focus on understanding how language models make predictions in order to make them more reliable, safe, and transparent to human users. She has been honored as a 3-time Rising Star in EECS , Machine Learning, and Generative AI. She was previously a postdoc at the Allen Institute for AI and the University of Washington and, before that, received her Ph.D. and M.S. degrees from Georgia Tech.

This talk is part of the Language Technology Lab Seminars series.

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