Baichen Du

See my CV for more details.

Hi! My name is Baichen Du 杜柏辰 [tʷuː4 pai3 ʈʂʰən2] in Mandarin, or [tou2 pak3 sɐn21] in Cantonese. I am currently on my gap year and working as Research and Teaching Assistant for Dr. Haohan Chen at HKU and Dr. Yao Yao at HKPolyU. I will join UCLA Linguistics as a PhD student in Fall 2025. I visited UC Berkeley in 2023 and obtained my Bachelor's degree in Political Science and Linguistics at HKU in 2024 Spring. I am interested in experimental phonology and phonetics broadly, sound change and variation, sentence processing and pronoun interpretation.

Previously, I worked on a number of projects with Keith Johnson and Alexandra Pfiffner at UC Berkeley Linguistics, Jonathan Havenhill at HKU, and Yao Yao at HKPolyU Chinese and Bilingual Studies. For my political science major, Haohan Chen supervised me on my UG thesis. Many thanks to all of them ;-)

• Du, B., Pfiffner, A. (To be submitted). Cross-modality cue integration and weighting: Lip rounding and acoustic cues in the Mandarin sibilant merger.

• Du, B., Pfiffner, A., Johnson, K. (2024). Visible articulatory variation as a cue to sound change: Lip rounding and lip protrusion variability in the Mandarin sibilant merger. University of Pennsylvania Working Papers in Linguistics, 30.2 (Selected Papers from NWAV 51).

• Yu, X., Van Hoey, T., Tan, F., Du, B. & Do, Y. (2024). Tracking phonological regularities: exploring the influence of learning mode and regularity locus in adult phonological learning. Linguistics Vanguard. https://doi.org/10.1515/lingvan-2023-0050

• Du, B. (2023). An audio-visual account for synchronic sound changes towards sibilants merger in Taiwan Mandarin. In Proceedings of the Seventh Annual Berkeley Undergraduate Linguistics Symposium 2023, University of California, Berkeley, CA.

• Du, B. (2023). The backfire effect in the perception and judgement of congruent and incongruent interpretation of political propaganda. Unpublished undergraduate thesis of political psychology. University of Hong Kong.

• Du, B. (2023, Sep). Introduction to Advanced R Programming with RMarkdown. The Sociolinguistics Lab, University of California, Berkeley.

• Du, B., Pfiffner, A., Johnson, K. (2024, Jan). The role of visible articulatory variation in Mandarin sibilant contrast and merger. Talk given at 2024 LSA Annual Meeting (LSA2024), New York City, NY.

• Du, B., Pfiffner, A., Johnson, K. (2023, Dec). Consonant and vowel rounding: same acoustics, different visuals. Poster presented at ASA 185th Meeting (ASA185) Sydney, Australia.

• Havenhill, J., Du, B. (2023, Dec). Visual cues in sound change: A cross-modal perceptual account for the typological rarity of labial palatalization. Poster presented at ASA 185th Meeting (ASA185) Sydney, Australia.

• Du, B., Pfiffner, A., Johnson, K. (2023, Oct). Visible articulatory variation as a cue to sound change: Lip rounding and lip protrusion variability in the Mandarin sibilant merger. Talk given at New Ways of Analyzing Variation 51 (NWAV51), Queens College, New York City, NY.

• Du, B. (2023). Acoustic distance effect on the perception of sibilants mergers between retroflexes and alveolars in Taiwan Mandarin. Poster presented at ASA 184th Meeting (ASA184), Chicago, IL.

• Du, B. (2023). An audio-visual account for synchronic sound changes towards sibilants merger in Taiwan Mandarin. Talk given at Seventh Annual Berkeley Undergraduate Linguistics Symposium 2023, University of California, Berkeley, CA.

• Du, B. (2023). The backfire effect in the perception and judgement of congruent and incongruent interpretation of political propaganda: A pre-analysis plan. Poster presentation at Undergraduate Research Fellowship Program Poster Session: Student Research Posters, University of Hong Kong, HK.

• POLI3148: Data Science in Politics and Public Administration (2024 Fall, Instructor: Haohan Chen)
Data Science in Political Science with Python: Python Basics, data wrangling with Pandas, visulization with Seaborn, geospatial analysis with Geopandas, text analysis with SpaCy, regression and machine learning with scikit-learn, and advnaced topics in TM and ML with transformers. To access course files, please click here.