bio.txt

Jordan Boyd-Graber is a full professor at the University of Maryland.  He has worked on model evaluations for human-centered topic models, psychologically inspired leaderboards, human–computer machine translation, and question answering.  He also contributed new models for improving generative models with RL, interactive approaches for question answering, topic models, and negotiations. Of his twenty former PhD students, seven have gone on to tenure track positions.  He and his students have been recognized with paper awards at  EMNLP (2023), IUI (2018), NAACL (2016), and NeurIPS (2009, 2015), and he won the 2015 Karen Spärk Jones Award and a 2017 NSF CAREER Award.  He served as PC for ACL 2023, SAC for EMNLP and NAACL, AC for ACL, NAACL, EMNLP, and NeurIPS, Poster Chair for EMNLP 2022, Tutorial Chair for ACL 2017, and Advisor for the ACL 2014 SRW.

He previously was an assistant professor at the University of Colorado, Visiting Research Scientist at Google Zürich, and Praktikant at the Berlin-Brandenburg Akademie der Wissenschaften.  His undergraduate degrees are in Computer Science and History at the California Institute of Technology, and he received his PhD from Princeton University.  His Erdös number is 2 (via Maria Klawe), and his Bacon number is 3 (by embarrassing himself on Jeopardy!).

He lives in Silver Spring, Maryland with his wife, two roombas, three fish, two daughters, and their 外婆.


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Jordan Boyd-Graber is an associate professor in the University of
Maryland's Computer Science Department, iSchool, UMIACS, and Language
Science Center. Jordan's research focus is in applying machine
learning and Bayesian probabilistic models to problems that help us
better understand social interaction or the human cognitive
process. He and his students have won "best of" awards at NIPS (2009,
2015), NAACL (2016), and CoNLL (2015), and Jordan won the British
Computing Society's 2015 Karen Spärk Jones Award and a 2017 NSF CAREER
award.

His work on computational social science includes work on topic shift, relationships, framing, and betrayal and and has been featured by CNN, Huffington Post, New York Magazine, and the Wall Street Journal. Boyd-Graber has made methodological contributions to variational inference, evaluation of probabilistic models, reinforcement learning for text data, and deep learning approaches for vision and language. 


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Jordan Boyd-Graber is an assistant professor in the University of
Colorado Boulder's Computer Science Department (a Colorado native),
formerly serving as an assistant professor at the University of
Maryland. Before joining Maryland in 2010, he did his PhD with David
Blei at Princeton. Jordan's research focus is in applying machine
learning and Bayesian probabilistic models to problems that help us
better understand social interaction or the human cognitive
process. He and his students have won "best of" awards at NIPS (2009,
2015), NAACL (2016), and CoNLL (2015), and Jordan won the British
Computing Society's 2015 Karen Spärk Jones Award and a 2017 NSF CAREER
award. His research has been funded by DARPA, IARPA, NSF, NCSES, ARL,
NIH, and Lockheed Martin and has been featured by CNN, Huffington
Post, New York Magazine, and the Wall Street Journal.

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Jordan Boyd-Graber is an assistant professor in the University of
Colorado Boulder's Computer Science Department, formerly serving as
an assistant professor at the University of Maryland.  He is a 2010
graduate of Princeton University, with a PhD thesis on "Linguistic
Extensions of Topic Models" with David Blei.  Jordan's
research focus is in applying machine learning and Bayesian
probabilistic models to problems that help us better understand social
interaction or the human cognitive process.  This research often leads
him to use tools such as large-scale inference for probabilistic
methods, natural language processing, multilingual corpus
understanding, and human computation.

One facet of this research is developing topic models, completely automatic
tools that can discover structure and meaning in large, multilingual datasets.
His research has developed new techniques to evaluate topic models, enable
interactivity in machine learning algorithms, and to expose how topics interact
with culture and sentiment.  His research has been supported by NSF, IARPA, and
ARL.

He has received the CoNLL 2015 Best Paper Award, a NIPS 2009 Best
Student Paper award honorable mention, a Computing Innovation
Fellowship (declined), and received the Jorgensen scholarship while an
undergrad at the California Institute of Technology, where he received
a BS in computer science and in history in 2004.

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Dr. Boyd-Graber's specific research contributions to topic modeling include:

  * User-Centric Evaluation: With collaborator Jonathan Chang, he developed the
    first human-centered evaluation of the quality of topic models.  This work
    overturned the previous approach of solely using machine learning evaluation
    techniques.

          http://www.umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf

  * Interactivity: With his student Yuening Hu, he developed interactive topic
    modeling, which allows users to repair topics that are incoherent or are
    inconsistent with a user's needs.

          http://www.umiacs.umd.edu/~jbg/docs/itm.pdf

  * Scalability: With his student Ke Zhai, he developed efficient algorithms for
    topic model inference across large datasets.

          http://www.umiacs.umd.edu/~jbg/docs/mrlda.pdf

  * Multilinguality: He developed the first topic models that could be applied
    to unaligned multilingual corpora.

          http://www.umiacs.umd.edu/~jbg/docs/jbg-mlslda-2010.pdf

Jordan is originally from Colorado.  In his spare time, Jordan enjoys competing
in and writing questions for trivia competitions.