Dr. Jing Han

Senior Research Associate

University of Cambridge

Research Interests
My research interests focus on machine intelligence approaches and innovative Deep Learning methods to promote mental and physical health through machine listening. I have worked on various tasks oriented towards physical health, such as heart sound analysis, infant vocalisation analysis, sleep sound analysis for sleep apnea detection, and COVID-19 sound analysis. Additionally, I am interested in audio analysis for mental health and have explored tasks including speech emotion prediction, synchronisation and empathy analysis in HCI/HHI conversations, and speech-based depression detection and monitoring for relapse prediction and prevention.

Research Outcomes
As an active researcher, I have co-authored over 60 peer-reviewed publications and serve as an associate editor for the IEEE Transactions on Affective Computing. My work has been recognised with several awards, and I have played an active role in organising special sessions at top conferences in the audio signal processing domain. Through involvement in three EU H2020-funded projects, I have honed skills in preparing research proposals, managing research projects, and developing dynamic and situational leadership styles for collaboration with researchers from world-leading universities and companies.

Future Plans
As for future plans, a primary interest of mine is leveraging and investigating effective and efficient deep learning algorithms to fulfil the needs of audiobased digital health systems in several aspects, such as Explainability, Safety, and Privacy. With advancements in DSP and AI, there is a ripe opportunity to pioneer innovative solutions. By ensuring early disease/illness detection and intervention and around-the-clock high-quality monitoring for all patients with this non-invasive and easy-to-operate technology, my work strives for a more equitable and effective healthcare system.

Potential Impact
Technologies capable of robustly and accurately analysing human sounds and associating audio patterns with diseases and health status will facilitate new research in basic sciences and open up new applications of ICT in medicine, healthcare, and business. Hopefully, these research will reshape the landscape of digital and remote healthcare through a robust human sound analysis system, push the boundaries of possibilities through innovative DSP and AI solutions, and bring societal benefits.