CV
Education
- Ph.D in Frugal Machine Learning, University Of Edinburgh (Expected finish Autumn 2024)
- MPhys, University Of Edinburgh, 2020
Skills
- Numerical & Machine Learning Frameworks
- PyTorch
- TensorFlow
- Sklearn
- SciPy
- NumPy
- Programming
Publications
Heggan, C., Budgett, S., Hospedales, T., Yaghoobi, M. (2022). MetaAudio: A Few-Shot Audio Classification Benchmark. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_19
Heggan, Calum, et al. "MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In (Variant) Representations." arXiv preprint arXiv:2305.17191 (2023).
Talks
Throughout my PhD I have given a wide variety of talks (approximately 30), some of these include:
September 21, 2021
Talk at Thales UK,
December 09, 2021
Talk at University Of Edinburgh, Department of Engineering (Institute for Digital Communications), Edinburgh, Scotland
September 06, 2022
Talk at ICANN22 (International Conference of Artificial Neural Networks), Bristol (UWE), England
Additional Research Experience
- 2020: Masters Project
- Title: Electride Phases and Host-Guest lattices of Group V Elements
- Duties included: Simulation work with the VASP package using a variety of high performance computing resources (ARCHER, Thomas)
- Supervisor: Andreas Hermann
- 2019: Senior Honours Project
- Title: X-Ray Diffraction from Crystals with Realistic Charge Distributions
- Duties included: Simulation of high pressure phase transitions of Magnesium using the Wien2k package
- Supervisor: Ingo Loa
Teaching
