Selected papers, editorials, and research outputs.
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.
An abstract on using speech as a non-invasive digital biomarker for AI-assisted early depression screening.
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 federated learning framework for multimodal speech-based depression detection, designed to balance predictive performance and privacy in healthcare AI settings.
A public-facing article discussing how AI is being applied in mental health and neurological care, including depression, autism, and dementia.
Presented research on using speech as a non-invasive digital biomarker for AI-assisted early depression screening.
Earlier work exploring temporal patterns and topic trends in Sinhala hate speech on Twitter using time-series and natural language processing approaches.
Earlier research examining relationships between mass media coverage and social media activity through correlation and trend analysis.
Under review for publication on adaptive data selection methods for improving robustness of multimodal depression assessment models using transformer-based representations.
Under review for publication on privacy-enhanced federated learning methods for speech-based depression detection, with multi-channel privacy evaluation.