About
I am a research fellow with the EvidENT team at the Ear Institute, UCL, where I investigate how AI can enhance hearing care. Specifically, my focus is on utilising large datasets from individuals with hearing loss to train machine learning models that address various issues related to hearing loss.
During my PhD, I researched how machine learning models can predict moments of stuttering from speech and neural signals in individuals who stutter. My continued research interests seek to enhance AI and machine learning models for underrepresented populations in speech and hearing technology.
Fields of Research
Artificial intelligence · Speech production · Hearing and balance · Speech recognition · Machine learning · Otorhinolaryngology · Computer vision and multimedia computation · Sound and music computing · Bioinformatics
Pinned Publications
BMC Medical Informatics and Decision Making, 2025
Frontiers in Psychology, 2024
Systematic Review of Machine Learning Approaches for Detecting Developmental Stuttering
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022
Funding and Grants
My current position is funded by the NIHR BRC UCLH Hearing Health Theme (IS-BRC-1215-20016). My PhD was funded by the EPSRC (EP/R513143/1).
Innovation Seed Fund
The Royal National Institute for Deaf People (2025)
Lead applicant for "Bridging the Gap: Digitising hand-drawn hearing tests for big data research and improve hearing" - ISF25\48.
Small Grant Fund
The Centre for Equality Research in Brain Sciences, University College London (2025)
Lead applicant for "Equity in Clinical Hearing Outcomes across Sociodemographics".
Win-A-Brite Award
Artinis Medical System BV (2021)
Proposed research "Integrating real-time fNIRS with biofeedback to promote fluency in people who stutter". Award was a portable fNIRS device for use in research. More information.