Liam Pond

Liam Pond CV (PDF)

MA in Music Technology

Performer Identification

Automatic Piano Fingering Detection

Large Language Models for Music

Liam Pond is a pianist and MA student in Music Technology at McGill University, supervised by Dr. Ichiro Fujinaga. His research spans performer identification, automatic piano fingering detection, and applications of large language models to music theory and music information retrieval. His thesis research is supported by a $40,000 master’s scholarship from the Fonds de recherche du Québec.

As a performer, Liam made his orchestral debut at age twelve on the harpsichord with the Kensington Sinfonia. He later performed the first movement of Rachmaninoff’s Piano Concerto No. 2 with the Calgary Civic Symphony and won first place in his age category at the Canadian Music Competition’s national finals. In 2023, he earned his bachelor’s degree in classical piano performance from the University of Toronto, studying with Dr. Jamie Parker. He continues to perform as part of his non-profit chamber group, Consort Eureka, and teaches piano and music theory.

At DDMAL, Liam has worked on LinkedMusic and SESEMMI, a tool that translates natural-language questions into SPARQL queries to help musicologists explore music databases. He also works as a system administrator and full-stack developer for the Cantus Database. His fall 2026 exchange with Dr. Simon Dixon at Queen Mary University of London focused on automatic fingering detection.

Liam serves as president of McGill’s Music Graduate Students’ Society (MGSS). Outside his work, he enjoys rock climbing, fencing, and language learning.

Research Interests

  • Performer Identification
  • Automatic Piano Fingering Detection
  • Large Language Models
  • Music Information Retrieval
  • Linked Music Data
  • Automatic Counterpoint Generation

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, Yinan Zhou, Sichen Meng, Linnea Kirby, Sebastien Chow, Simon Ngassam, Dylan Hillerbrand, and Ichiro Fujinaga. 2026. “Facilitating Access to LinkedMusic with SESEMMI.” In Proceedings of the 13th International Conference on Digital Libraries for Musicology. Thessaloniki, Greece. https://doi.org/10.1145/3815723.3815727.
  • Pond, Liam, Tace McNamara, and Ichiro Fujinaga. Forthcoming. “The Benchmark for Encoding Assessment in Music (BEAM): 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 570: 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.