Dr Tommy Peng is a post-doctoral research fellow with a formal background in biomedical engineering (BSc/MSc, Washington University in St Louis, USA) and computer systems engineering (PhD, University of Auckland, New Zealand). He has experience in signal processing, psychoacoustics, electrophysiology, and predictive modelling. While in the USA, Tommy designed and implemented speech-in-noise hearing tests on smartphone and tablet devices to improve accessibility of hearing assessments. During his PhD, Tommy leveraged machine learning techniques to predict how drugs and diseases affect the electrical patterns from the heart.

At the Bionics Institute, Tommy works as a part of the Translation Hearing Research team led by Professor Colette McKay. Tommy combines data from psychophysical and brain imaging diagnostic tests to build models that pinpoint user-specific nervous system limitations which are detrimental for speech understanding development in new cochlear implant users. He aims to develop new signal processing and auditory training techniques to address these limitations and improve the benefits of cochlear implants for all users.

Tommy hopes that these diagnostic tests and new techniques will one day become a part of the standard cochlear implant tuning process in clinics.


Email: [email protected]

ORCID: 0000-0002-9407-710X

Google scholar: Tommy Peng

Research Projects:

https://www.bionicsinstitute.org/improving-cochlear-implantsImproving cochlear implants



  • Peng, T., Malik, A., Bear, L., & Trew, M. L. (2021). Impulse data models for the inverse problem of electrocardiography. IEEE Journal of Biomedical And Health Informatics.
  • Peng, T., Malik, A., Trew, M. L. (2021). Predicting drug-mediated pro-arrhythmic effects using pre-drug electrocardiograms. Biomedical Signal Processing and Control, 68(November 2020), 102712.
  • Peng, T., Trew, M. L., & Malik, A. (2019). Predictive modeling of drug effects on electrocardiograms. Computers in Biology and Medicine, 108, 332–344.
  • Malik, A., Peng, T., & Trew, M. (2018). A machine learning approach to reconstruction of heart surface potentials from body surface potentials. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4828–4831.
  • Peng, T. (2017). Development and Validation for a Mobile Speech-in-Noise Audiometric Task.