Liam Pond

MA in Music Technology

Performer Identification

Music Theory Applications of Large Language Models (LLMs)

Born and raised in Calgary, Canada, Liam Pond made his orchestral debut at age twelve on the harpsichord, performing 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 and subsequently won first place in his age category at the Canadian Music Competition’s national finals.

In 2023, Liam earned his bachelor’s degree in classical piano performance from the University of Toronto under Dr. Jamie Parker. His graduation recital featured Ravel’s Gaspard de la nuit— often considered the most difficult work in the standard piano repertoire—alongside his own jazz improvisations, a skill he taught himself over the pandemic by watching YouTube videos.

He has also received numerous academic scholarships and awards, including $40,000 from the Fonds de recherche du Québec to support his thesis work developing a machine learning model to understand the stylistic nuances of different pianists. He has authored six peer-reviewed publications covering topics in computer science, music, engineering, and education. His research has taken him to conferences in South Korea (ISMIR 2025), Denmark (ICCCM 2025), Portugal (CSME 2025), and Japan (MEC 2026). Outside his studies, he enjoys rock climbing, photography, fencing, and language learning.

Research Interests

  • Performer Identification
  • Large Language Models
  • Music Information Retrieval
  • Automatic Counterpoint Generation
  • In-Context Learning

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

  • Pond, Liam, Tace McNamara and Ichiro Fujinaga. Forthcoming. “The Benchmark for Encoding Assessment in Music (BEAM-LLM): Evaluating LLM Performance in Symbolic Music Parsing” In Proceedings of the Music Encoding Conference 2026. Tokyo, Japan.
  • Pond, Liam, and Ichiro Fujinaga. 2025. “Teaching LLMs Music Theory with In-Context Learning and Chain-of-Thought Prompting: Pedagogical Strategies for Machines.” In Proceedings of the 17th International Conference on Computer Supported Education – Volume 1: CSME, 671–681. Porto, Portugal. https://doi.org/10.5220/0013506100003932.
  • 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.