Liam Pond

MA in Music Technology

Music Theory Applications of In-Context Learning for Large Language Models (LLMs)

Singing Voice Synthesis

Born and raised in Calgary, Canada, Liam Pond made his orchestral debut at age twelve on the harpsichord playing Bach’s F minor Concerto with the Kensington Sinfonia. Four years later, he performed the first movement of Rachmaninoff’s Piano Concerto No. 2 with the Calgary Civic Symphony, later winning first place in his age category at the Canadian Music Competition’s national finals. In 2023, Liam earned his bachelor’s degree from the University of Toronto, studying classical piano performance with Dr. Jamie Parker. In his graduation recital, he performed Ravel’s Gaspard de la nuit—often considered the most difficult work in the standard piano repertoire—and jazz improvisation based on classical themes, a skill he developed over the pandemic by watching YouTube videos. Liam is currently pursuing a master’s in music technology at McGill University and the Université de Montréal, where he has been teaching ChatGPT music theory. Outside his studies, he enjoys rock climbing, chess, fencing, and language learning.

Research Interests

  • In-Context Learning
  • Large Language Models
  • Music Information Retrieval

Academic Record

  • BMus in Classical Piano Performance, University of Toronto (Dr. Jamie Parker)
  • Minor in Mathematics, University of Toronto
  • Certificate in Piano Pedagogy, University of Toronto

Publications

  • Saini, Vinay, Liam Pond, Jackson Uhryn, Albert Kalayil, Aditya Tomar, Kasimuthumaniyan Subramanian, Milana Trifkovic, and Philip Egberts. 2025. “Shear-Dependent Tribological Behavior of Oleic Acid as a Sustainable Lubricant Additive in Oils and Nano-Greases.” Wear: 205932. https://doi.org/10.1016/j.wear.2025.205932.
  • Burt, Lauren A., Liam T. Pond, Annabel R. Bugbird, David A. Hanley, and Steven K. Boyd. 2025. “Canadian Adult Reference Data for Body Composition, Trabecular Bone Score and Advanced Hip Analysis Using DXA.” Journal of Clinical Densitometry 28 (1): 101535. https://doi.org/10.1016/j.jocd.2024.101535.
  • Abid, Noor, Liam Pond, and Svetlana Yanushkevich. 2024. “Causality Exploration in Modeling Engineering Student Satisfaction.” In Proceedings of the 2024 IEEE Global Engineering Education Conference (EDUCON), 1–10. Kos, Greece. https://doi.org/10.1109/EDUCON60312.2024.10578860.
  • Anzum, Fahim, Ashratuz Zavin Asha, Lily Dey, Artemy Gavrilov, Fariha Iffath, Abu Quwsar Ohi, Liam Pond, Md Shopon, and Marina L. Gavrilova. 2024. “A Comprehensive Review of Trustworthy, Ethical, and Explainable Computer Vision Advancements in Online Social Media.” In Global Perspectives on the Applications of Computer Vision in Cybersecurity, edited by Franklin Tchakounté and Marcellin Atemkeng, 1–46. Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-6684-8127-1.ch001.