Caitlin Smith

Caitlin Smith is a Teaching Professor in the Department of Linguistics at the University of North Carolina at Chapel Hill. Previously, she held positions as a lecturer at the University of California Davis and a postdoctoral fellow at Johns Hopkins University. She earned her Ph.D. in Linguistics from the University of Southern California in 2018.

Her research focuses on phonology, particularly exploring gestural representations and their application to phenomena like vowel harmony, opacity, and phonological exceptionality. Key areas include computational modeling of gestural harmony, typological asymmetries in phonological patterns, and speech production analysis using real-time MRI data. She collaborates with researchers in computational linguistics and speech production, including work with Microsoft Research AI on integrating structured representations into neural networks.

Her recent work emphasizes the learnability of phonological patterns through computational models and the reanalysis of traditional phonological puzzles within a gestural framework. She has developed the Grammar + Gesture Gradual Learning Algorithm (GGGLA) to model the acquisition of gestural parameters, demonstrating how derivationally opaque patterns emerge naturally in the Gestural Harmony Model.

Her contributions span both theoretical and experimental phonology, with notable publications in venues like The Oxford Handbook of Vowel Harmony, Proceedings of NELS, and Scientific Data. She actively engages in interdisciplinary research, bridging formal phonology with computational methods and articulatory phonetics.