Hiring: I am looking for proactive PhD students with a solid Computer Science or Electrical Engineering foundation. Should you be interested in exploring the expansive fields of Mobile Health, Artificial Intelligence, and Speech/Audio Processing, feel free to contact me and include your CV. The University offers full scholarships to candidates who meet the qualifications. Details can be found in opportunities.
I am a senior lecturer at the University of Melbourne, and a visiting researcher in the Department of Computer Science and Technology, University of Cambridge. Prior to this, I worked as a senior research scientist in Nokia Bell Labs (UK), senior research associate (RA) working with Professor Cecilia Mascolo at the University of Cambridge, and a RA at the University of New South Wales (UNSW), Australia. I received the BE and MEngSc degrees in Signal Processing from the Northwestern Polytechnical University, China, and the Ph.D. degree under the supervision of Dr. Vidhyasaharan Sethu and Professor Eliathamby Ambikairajah from UNSW. My primary research interests are on exploring the potential of audio signals (e.g., speech) via mobile and wearable sensing for automatic mental state (e.g., emotion, depression) prediction and disease (e.g., COVID-19) detection and monitoring. Further, my work aims to develop generalised, interpretable, and robust machine learning models to improve healthcare delivery. I served as the program committee and reviewer for over 30 top-tier journals and conferences, including AAAI, IJCAI, UbiComp, ICASSP, IEEE TAC, IEEE TASLP, JASA, JMIR, etc. I have won the ACII best paper, ICASSP top 3% paper, Asian Dean's Forum Rising Star Women In Engineering Award 2022, and IEEE Early Career Writing Retreat Grant 2019.
My research interests are on human-centred audio sensing and machine learning for mobile health monitoring, which explores the potential of audio signals (e.g., speech, cough) via mobile and wearable sensing for automatic mental state prediction and disease detection and monitoring (e.g., emotion, depression, COVID-19), and develops generalised, interpretable, and robust deep learning models to improve healthcare delivery. Specifically, it includes:
Machine learning in mobile health: exploring the potential and challenges of mobile technologies for health monitoring.
Speech and Audio Processing: investigating advanced signal processing techniques and potential novel applications using speech and audio signals.
Trustworthy Deep Learning (DL): improving the interpretability and generalization in DL for more reliable health outcome predictions.
Wearable Sensing: examining novel sensing opportunities for fitness and well-being monitoring with new forms of resource-constrained IoT wearable device forms.