12:00–1:30 pm
JCL 390 John Crerar Library This talk will also be broadcast via Zoom. Please register to receive viewing information.
5730 S. Ellis Avenue
JORDAN BOYD-GRABER, University of Maryland
"If We Want AI to be Interpretable, We Need to Define and Measure It"
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 embarrassing himself on Jeopardy!).
Agenda
12:00 pm–12:30 pm: Lunch (Lunch will be provided on a first come, first serve basis.)
12:30 pm–1:30 pm: Talk and Q&A