Andrew Chang

About

I am currently a Senior Quantitative User Experience Researcher on the Search Ads team at Google. In practice, I act as a mixture of applied cognitive scientist, computational linguist, machine learning engineer, and quant research methodologist for work that I am not allowed to share. ;)

By training, I am an auditory and computational cognitive neuroscientist (PhD at McMaster with Laurel Trainor, postdoc at NYU with David Poeppel). Fundamentally, my research asks how the brain decodes the acoustic world—how we hear speech and music, and how our bodies synchronize during social interaction. Also, I have dragged this focus into the algorithmic realm, digging into the computational implementations of human audition and their neural/machine representations. The one continuous, slightly obsessive theme tying my work together is that time is the core infrastructure of these phenomena, from prediction to production. I am a methodological pluralist: combining and improvising behavioral experiments, neural recordings, signal processing, machine learning, and computational modeling, depending on the appropriateness. My work has been recognized by National Institutes of Health, New York Academy of Sciences, Governor General of Canada, Natural Sciences and Engineering Research Council of Canada, GRAMMY Foundation, and Cognitive Neuroscience Society, and has been featured in Scientific American.

Beyond, I am a humbly okay classical violinist/violist, an over-enthusiastic jazz learner, a terrible squash player, an improving-maybe husband, and a definetly goofy daddy of one (soon to be two).

Research Interests

• sound & brain — the acoustic, perceptual, and neural features that tell speech and music apart, and what that (dis)similarity means for people with communication disorders. Also how the brain tracks rhythm, pitch, and temporal structure to predict what comes next.
• interpersonal interaction — how dynamic vocal, facial, and body behavior reveal leadership, emotion, and connection during multipersonal joint music performance, conversation, videoconferencing, and dating <3.
• ml/ai — multimodal and interpretable machine learning with an audio focus, with neuroscientific and cognitive inspirations (if possible).
• research methods — exploring ad libitum to find the mathematically sound and just-slightly-more-than-enough approach to serve the research need.

Experiences

• 2025–Senior Quantitative UX Researcher, Google
• 2020–25Postdoctoral Researcher, New York University
• 2014–19PhD in Psychology, McMaster University
• 2008–13BS in Psychology, National Taiwan University

Publications

1. Chang, A.; Bodi, A.K.; Deng, W.; Huang, J.; Kadamba, V.G.; Karanam, S.B.H.; Kennady, D.A., Poeppel, D.; Freeman, D. (2026). VIP-MINGLE: A Corpus for Videoconference and In-Person Multimodal Interaction in Group Language Engagement. Interspeech 2026, (in press).
2. Chang, A.; Li, Y.; Roman, I. R.; Poeppel, D. (2025). Spectrotemporal modulation: Efficient and interpretable feature representation for classifying speech, music, and environmental sounds. Interspeech 2025, 216–220.
3. Chang, A.; Hu, C.; Qi, J.; Wei, Z.; Zhang, K.; Akkaraju, V.; Poeppel, D.; Freeman, D. (2025). Multimodal fusion with semi-supervised learning minimizes annotation quantity for modeling videoconference conversation experience. Interspeech 2025, 4313–4317.
4. Chang, A.; Akkaraju, V.; Cogliano, R. M.; Poeppel, D.; Freeman, D. (2025). Multimodal machine learning can predict videoconference fluidity and enjoyment. ICASSP 2025 (IEEE), 1–5.
5. Chang, A.; Poeppel, D.; Teng, X. (2025). Temporally dissociable neural representations of pitch height and chroma. Journal of Neuroscience, 45, e1567242024.
6. Chang, A.; Teng, X.; Assaneo, M. F.; Poeppel, D. (2024). The human auditory system uses amplitude modulation to distinguish music from speech. PLOS Biology, 22, e3002631.
7. Carrillo, C.; Chang, A.; Armstrong, H.; Cairney, J.; McAuley, J. D.; Trainor, L. J. (2024). Auditory rhythm facilitates perception and action in children at risk for developmental coordination disorder. Scientific Reports, 14, 12501.
8. Chen, Y.-C.; Chang, A.; Rosenberg, M. D.; Feng, D.; Scholl, B. J.; Trainor, L. J. (2022). “Taste typicality” is a foundational and multi-modal dimension of ordinary aesthetic experience. Current Biology, 32, 1837–1842.
9. Wood, E. A.; Chang, A.; Bosnyak, D.; Klein, L.; Baraku, E.; Dotov, D.; Trainor, L. J. (2022). Creating a shared musical interpretation: Changes in coordination dynamics while learning unfamiliar music together. Annals of the New York Academy of Sciences, 1516, 106–113.
10. Chang, A.; Li, Y.-C.; Chan, J. F.; Dotov, D. G.; Cairney, J.; Trainor, L. J. (2021). Inferior auditory time perception in children with motor difficulties. Child Development, 92, e907–e923.
11. Chang, A.; Kragness, H. E.; Tsou, W.; Bosnyak, D. J.; Thiede, A.; Trainor, L. J. (2021). Body sway predicts romantic interest in speed dating. Social Cognitive and Affective Neuroscience, 16, 185–192.
12. Chang, A.; Bedoin, N.; Canette, L.-H.; Nozaradan, S.; Thompson, D.; Corneyllie, A.; Tillmann, B.; Trainor, L. J. (2021). Atypical beta power fluctuation while listening to an isochronous sequence in dyslexia. Clinical Neurophysiology, 132, 2384–2390.
13. Chang, A.; Bosnyak, D. J.; Trainor, L. J. (2019). Rhythmicity facilitates pitch discrimination: Differential roles of low and high frequency neural oscillations. NeuroImage, 198, 31–43.
14. Chang, A.; Kragness, H. E.; Livingstone, S. R.; Bosnyak, D. J.; Trainor, L. J. (2019). Body sway reflects joint emotional expression in music ensemble performance. Scientific Reports, 9, 205.
15. Trainor, L. J.; Chang, A.; Cairney, J.; Li, Y. (2018). Is auditory perceptual timing a core deficit of developmental coordination disorder?. Annals of the New York Academy of Sciences, 1423, 30–39.
16. Chang, A.; Bosnyak, D. J.; Trainor, L. J. (2018). Beta oscillatory power modulation reflects the predictability of pitch change. Cortex, 106, 248–260.
17. Chang, A.; Ide, J. S.; Li, H.-H.; Chen, C.-C.; Li, C.-S. R. (2017). Proactive control: Neural oscillatory correlates of conflict anticipation and response slowing. eNeuro, 4, ENEURO.0061-17.2017.
18. Chang, A.; Livingstone, S. R.; Bosnyak, D. J.; Trainor, L. J. (2017). Body sway reflects leadership in joint music performance. Proceedings of the National Academy of Sciences, 114, E4134–E4141.
19. Chang, A.; Bosnyak, D. J.; Trainor, L. J. (2016). Unpredicted pitch modulates beta oscillatory power during rhythmic entrainment to a tone sequence. Frontiers in Psychology, 7, 327.
20. Ferguson, G. M.; Boer, D.; Fischer, R.; … Chang, A.; … et al. (2016). “Get up, stand up, stand up for your rights!” The Jamaicanization of youth across 11 countries through reggae music?. Journal of Cross-Cultural Psychology, 47, 581–604.
21. Pouryazdian, S.; Chang, A.; Bosnyak, D. J.; Trainor, L. J.; Beheshti, S.; Krishnan, S. (2016). Multi-domain feature selection in auditory mismatch negativity via PARAFAC-based template matching. IEEE EMBC 2016, 1603–1607.
22. Chang, A.; Chen, C.-C.; Li, H.-H.; Li, C.-S. R. (2015). Perigenual anterior cingulate event-related potential precedes stop signal errors. NeuroImage, 111, 179–185.
23. Chang, A.; Chen, C.-C.; Li, H.-H.; Li, C.-S. R. (2014). Event-related potentials for post-error and post-conflict slowing. PLOS ONE, 9, e99909.