Noah A. Smith (original) (raw)

Noah Smith is a computer scientist working in several fields of artificial intelligence research. He recently wrote Language Models: A Guide for the Perplexed, a general-audience tutorial, and he co-directs the OLMo open language modeling effort with Hanna Hajishirzi.

Broadly, his research targets algorithms that process data encoding language, music, and more, to augment human capabilities. He also works on core problems of research methodology like evaluation. You can watch videos of some of his talks, read his papers, and learn about his research groups, Noah’s ARK and AllenNLP. Smith is most proud of his mentoring accomplishments: as of 2024, he has graduated 29 Ph.D. students and mentored 15 postdocs, with 27 alumni now in faculty positions around the world. 20 of his undergraduate/masters mentees have gone on to Ph.D. programs. His group’s alumni have started companies and are technological leaders both inside and outside the tech industry.

Appointments & Education

He is Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering at the University of Washington (also Adjunct in Linguistics, Affiliate of the Center for Statistics and the Social Sciences, Senior Data Science Fellow at the eScience Institute, and Associate Faculty of the Stroum Center for Jewish Studies) as well as Senior Director of NLP Research at the Allen Institute for Artificial Intelligence. Previously, he was an Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. He earned his Ph.D. in Computer Science from Johns Hopkins University and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland.

Service

Smith was the general chair of EMNLP 2022. He has served on the editorial boards of the journals Computational Linguistics (2009–2011), Journal of Artificial Intelligence Research (2011–2019), and Transactions of the Association for Computational Linguistics (2012–present), as the secretary-treasurer of SIGDAT (2012–2015 and 2018–2019), and as program co-chair of ACL 2016. He co-organized the Ninth Annual Conference on New Directions in Analyzing Text as Data (TADA 2018), Language Technologies and Computational Social Science (a workshop at ACL 2014), and Twenty Years of Bitext (a workshop at EMNLP 2013).

Recognition

Smith was elected a Fellow of the Association for Computational Linguistics “for significant contributions to linguistic structure prediction, computational social sciences, and improving NLP research methodology” (2020). UW’s Sounding Board team, led by Profs. Mari Ostendorf, Yejin Choi, and Noah Smith, won the inaugural Amazon Alexa Prize in 2017. Smith’s research was recognized with a UW Innovation award “to stimulate innovation among faculty from a range of disciplines and to reward some of their most terrific ideas” (2016–2018), the Finmeccanica career development chair at CMU “to acknowledge promising teaching and research potential in junior faculty members” (2011–2014), an NSF CAREER award (2011–2016), and a Hertz Foundation graduate fellowship (2001–2006). He has coauthored conference papers that were cited as outstanding, finalist, honorable mention, and sometimes even “best” student/theme/resource/overall paper at ICLP 2008, ACL 2009, COLING 2010, NAACL 2013, ACL 2014, NAACL 2015, WWW 2016, EACL 2017, NAACL 2018, ACL 2018, ACL 2019 (twice), ACL 2020, ACL 2021, NAACL 2022, and ACL 2024 (twice).

Teaching

Smith will teach NLP at the graduate and undergraduate level in winter 2025 (CSE 447/517) and in the Allen School’s Professional Masters Program in spring 2025 (CSE P517).

Smith’s recent teaching includes AI for musicians and courses on NLP for undergraduates, professional masters students, and Ph.D. students as well as a “capstone” projects course on NLP. Lecture videos from 2021:

Personal

Smith lives in Seattle with his spouse, where they serve two felines. When he is not working, he is often playing clarinet or saxophone, swimming, running, dancing tango, or mixing cocktails. He was interviewed by Devi Parikh for Humans of AI: Stories, Not Stats on September 23, 2020:

Academic Genealogy

Smith’s main intellectual influences are his undergraduate mentors Philip Resnik and Norbert Hornstein, his Ph.D. advisor Jason Eisner, Frederick Jelinek, and his many mentees.

Smith’s academic ancestors had varied interests and careers. A few examples (links discovered thanks to the Mathematics Genealogy Project):

Other academic ancestors include linguist Roman Jakobson (6th degree ancestor, founder of modern phonology), polymath Pierre-Simon Laplace (12th degree ancestor, developer of the so-called Bayesian interpretation of probability), Enlightenment figure [Jean le Rond d’Alembert](Jean Le Rond d'Alembert) (14th degree ancestor), and theologian Desiderius Erasmus Roterodamus (29th degree ancestor).

There is, of course, much debate to be had about which of the scholars in the MGP really completed anything we would consider comparable to the modern Ph.D., and how similar the attested relationships are to modern Ph.D. advising.