Jordan Boyd-Graber (Maryland) – If We Want AI to be Interpretable, We Need to Define and Measure It

Part of the Autumn 2022 Distinguished Speaker Series.

Bio: Jordan Boyd-Graber’s research focus is in applying machine learning to problems that help computers better work with or understand humans. His research applies statistical models to natural language problems in ways that interact with humans, learn from humans, or help researchers understand humans. Jordan is an expert in the application of topic models, automatic tools that discover structure and meaning in large, multilingual datasets. His work has been supported by NSF, DARPA, IARPA, and ARL. Three of his students have gone on to tenure track positions at NYU, U Mass Amherst, and Ursinus. His awards include a 2017 NSF CAREER, the Karen Spärk Jones prize; “best of” awards at NIPS, CoNLL, and NAACL; and a Computing Innovation Fellowship (declined). His Erdös number is 2 (via Maria Klawe), and his Bacon number is 3 (by embarassing himself on Jeopardy!).

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