teaching

Rice University

instructor of record

University of Washington

instructor of record

teaching assistant

guest lectures & invited talks

  • Spring 2025 - MELC 268: Silk Road and Globalization
    Languages of the Silk Road
  • Winter 2024 - MELC 269: Music Cultures of the Silk Road
    Tibetan Music

course descriptions

LING 430: Computational Linguistics (Rice)
A hands-on, beginner-friendly introduction to the field of computational linguistics: students learn Python while working with real language data, connecting each computational method to the linguistic assumptions it makes.

LING 471: Computational Methods for Linguists (UW)
Focuses on how computational methods and tools can be applied in linguistics, emphasizing the basics of programming and general technical versatility skills needed for programming tasks. Assignments are organized around linguistics or linguistically annotated data. Students learn to write Python programs, discuss what counts as data in computational linguistics, connect linguistic theory to computational method choice, and reflect on the ethical and social implications of data use.

LING 570: Shallow Processing Techniques for Natural Language Processing (UW)
Covers techniques and algorithms for associating relatively surface-level structures and information with natural language corpora, including POS tagging, morphological analysis, preprocessing/segmentation, named-entity recognition, chunk parsing, and word-sense disambiguation. Examines linguistic resources that can be leveraged for these tasks (e.g., WordNet).

LING 571: Deep Processing Techniques for Natural Language Processing (UW)
Covers algorithms for associating deep or elaborated linguistic structures with naturally occurring data, covering parsing, semantics, and discourse.

LING 572: Advanced Statistical Methods in Natural Language Processing (UW)
Covers several important machine learning algorithms for natural language processing including decision tree, kNN, Naive Bayes, support vector machine, maximum entropy / multinomial logistic regression, conditional random fields, and neural networks. Students implement many of the algorithms and apply these algorithms to some NLP tasks.

LING 200: Introduction to Linguistics (UW)
Introduces language as the fundamental characteristic of the human species, exploring the diversity and complexity of human languages. Topics include phonological and grammatical analysis, dimensions of language use, language acquisition, and historical language change.