Hahn-Powell, Gus | SLAT | Second Language Acquisition and Teaching

hahnpowell@arizona.edu

Hahn-Powell, Gus

Assistant Professor

Home Department: Linguistics

SLAT Areas of Specialization: Linguistic Dimensions of L2 Learning, Technology in Second Language Teaching

Dr. Gus Hahn-Powell is an Assistant Professor in the Department of Linguistics. He also hold appointments in the Cognitive Science GIDP and the Computational Social Science Graduate Certificate Program. His research centers around machine reading for scientific discovery. In other words, he builds and designs intelligent systems to help researchers surmount the problem of information overload by scouring the vast body of scientific literature, analyzing findings, and synthesizing discoveries to generate novel hypotheses. Please see his website for details on his research and teaching. You can also find other information on his UA Profile.

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Area of Specialization

Linguistic dimensions of L2 learning

Technology in Second Language Teaching (minor)

Currently Teaching

LING 539 – Statistical Natural Language Processing

This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.

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LING 529 – Human Language Technology I

This class serves as an introduction to human language technology (HLT), an emerging interdisciplinary field that encompasses most subdisciplines of linguistics, as well as computational linguistics, natural language processing, computer science, artificial intelligence, psychology, philosophy, mathematics, and statistics.
Content includes a combination of theoretical and applied topics such as (but not limited to) tokenization across languages, n-grams, word representations, basic probability theory, introductory programming, and version control.

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