I am a PhD researcher at the University of Auckland, where I develop trustworthy AI methods for early depression detection using speech and language. My research combines machine learning, speech processing, multimodal learning, and federated learning to build AI systems that are accurate, interpretable, and privacy-preserving. Ultimately, my goal is to create clinically meaningful AI technologies that support accessible and ethical mental healthcare.
Presentations, media, research outreach, and upcoming events.
Presenting our accepted paper, “FedMPA: A Novel Privacy-Performance Optimization Approach for Multimodal Speech-Based Depression Detection,” at Interspeech 2026. The work explores privacy-preserving federated learning for multimodal speech-based depression detection, with a focus on balancing predictive performance and privacy.
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Presented my PhD research, “Towards Trustworthy Speech-Based Multimodal Depression Detection,” at the 12th ISCA-SAC Doctoral Consortium. The presentation brings together my research on robust multimodal depression detection, privacy-preserving federated learning, and representation-level interpretability towards trustworthy AI for mental health.
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A few weeks ago, I had the opportunity to share my research at 𝗔𝗪𝗜𝗦𝟮𝟬𝟮𝟲 alongside an inspiring community of women working across the sciences. As part of the Science Snapshot session, I presented my PhD research on 𝗦𝗽𝗲𝗲𝗰𝗵 𝗮𝘀 𝗮 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝗿 𝗳𝗼𝗿 𝗗𝗲𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, exploring how speech and language can be used as digital biomarkers while looking beyond predictive performance to important questions around 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆.
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Joined RNZ Afternoons to discuss my PhD research on AI-assisted depression detection and the potential of speech and language technologies to support mental healthcare. The conversation explored how AI could contribute to earlier and more accessible support while highlighting the importance of privacy, trust, and responsible use.
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Participated in He Pito Mata, an event bringing together early career researchers from across Aotearoa New Zealand to connect, share experiences, and engage in conversations around research careers, collaboration, and the wider research community.
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Contributed to The Conversation with an article exploring where AI could meaningfully support mental healthcare without replacing therapists. The piece discusses opportunities for responsible AI-assisted mental health support while considering important challenges around privacy, trust, and appropriate use.
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Participated in an AI in Health Research event bringing together researchers and professionals working at the intersection of artificial intelligence and healthcare. It was a valuable opportunity to learn about emerging AI applications in health, exchange perspectives, and connect with the wider research community in Aotearoa New Zealand.
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Presented my research on speech as a digital biomarker for AI-driven early depression detection at HealtheX 2025. It was a valuable opportunity to share my work with the wider health research community, discuss the potential of AI-assisted approaches to mental health assessment, and engage with researchers working across diverse areas of healthcare.
Learn more →Selected research themes from my PhD and related projects.
Speech and text-based modelling for early depression screening.
Federated multimodal learning for sensitive mental health data.
Quantifying the trade-off between predictive performance and privacy preservation to support trustworthy federated AI for healthcare.
Key publications, media, and academic activities.
Presents my PhD research vision for developing trustworthy AI systems for speech-based depression detection, integrating multimodal learning, privacy-preserving federated learning, and representation-level interpretability.
A live media interview discussing the opportunities and limitations of AI in mental healthcare, and how AI can support therapists through early detection, self-reflection, and scalable mental health technologies.
A public-facing commentary discussing the opportunities and limitations of AI in mental healthcare, and how AI can support therapists through early detection, self-reflection, and scalable mental health technologies.
A privacy-preserving multimodal learning framework for speech-based depression detection, designed to balance predictive performance with privacy protection in federated settings.
I'm always interested in discussing research collaborations, AI for healthcare, and academic opportunities.
