0000-0002-1585-3060 About
I am a biomedical engineer and applied AI researcher with a PhD in Biomedical Engineering from the University of Sydney, specialising in machine learning, signal processing, and statistical modelling for healthcare applications. My research focuses on developing and validating data-driven methods for analysing complex biomedical signals, with particular emphasis on speech, audio, and physiological data in clinical and paediatric populations.
My work spans translational research at the intersection of engineering, medicine, and artificial intelligence. I have extensive experience designing end-to-end analytical pipelines, benchmarking AI/ML models for diagnostic decision support, and working within ethical, regulatory, and clinical governance frameworks (HREC, GCP, SaMD considerations). I have collaborated closely with clinicians, industry partners, and policymakers to translate advanced computational methods into clinically actionable insights.
My doctoral research investigated non-invasive identification of the site of upper airway collapse in obstructive sleep apnoea using nocturnal audio recordings, applying supervised and unsupervised machine learning techniques to achieve clinically meaningful accuracy. More recently, my research has focused on hearing health, including automated detection of hearing loss from children’s speech, longitudinal analysis of paediatric hearing data, speech intelligibility assessment using AI-based transcription, and personalisation of hearing assistive devices.
I have authored more than ten peer-reviewed publications in international journals and conferences across biomedical engineering, speech processing, and machine learning, and I am a named inventor on patent submissions related to AI-driven auditory technologies. My professional experience includes leading and contributing to multidisciplinary research projects in regulated clinical environments, mentoring junior researchers, and supporting clinical trials and medical device validation.
I am committed to ethical, reproducible, and clinically relevant research, with the overarching goal of improving health outcomes through robust, data-driven innovation—particularly for children and underserved populations.
Employment
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Nexev Technical Lead2025 - Present
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LaennecAI2025 - 2026
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National Acoustics Laboratory Research Scientist2021 - 2024
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University of Sydney AcademicTutor2017 - 2019
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Goverment Engineering College Wayanad Lecturer Adhoc2015 - 2016
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (23)
- Automated Speech Intelligibility Assessment Using AI-Based Transcription in Children with Cochlear Implants, Hearing Aids, and Normal Hearing Save
- Automatic Detection of Hearing Loss from Children's Speech using wav2vec 2.0 Features Save
- Acoustic Analysis of Video Conferencing Platforms: Evaluating Communication Effectiveness for Individuals with Hearing Loss Save
- Enhancing understanding of real-world listening experiences: Insights from ecological momentary assessments with assistive listening technologies Save
- NEMA: An Ecologically Valid Tool for Assessing Hearing Devices, Advanced Algorithms, and Communication in Diverse Listening Environments Save
- Spatial auditory training improves spatial localization of the sound of an unseen talker’s voice after unilateral hearing loss Save
- Association of snoring characteristics with predominant site of collapse of upper airway in obstructive sleep apnea patients Save
- Unsupervised Approach for the Identification of the Predominant Site of Upper Airway Collapse in Obstructive Sleep Apnoea Patients Using Snore Signals Save
- Automatic Classification of OSA related Snoring Signals from Nocturnal Audio Recordings Save
- Characterisation of Upper Airway Collapse in OSA Patients Using Snore Signals: A Cluster Analysis Approach. Save