I am a habilitation candidate at the ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg in Germany.
I was awarded my PhD in Electrical Engineering from UNSW Australia in February 2016 for my thesis ‘Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning’.
My current research interests include multisensory signal analysis, affective computing, and computer audition. I am fascinated by the application of machine learning techniques to improve our understanding of different health conditions. I am particularly interested in applying these techniques to mental health disorders.
I am currently involved in the DE-ENIGMA, RADAR-CNS, TAPAS and sustAGE Horizon 2020 projects, in which my roles include contributions towards management of the technical work packages. I enjoy working towards solving real-world problems in health and wellbeing as part of these inter-disciplinary teams.
I have been lecturing since autumn 2017, writing and delivering courses in Speech Pathology, Deep Learning and Intelligent Signal Analysis in Medicine.
I am also an external advisor on the National Science Foundation of China (NSFC) funded project, “Diagnosis of Depression by Speech Signals” (grant No.31860285), led by Dr. Xiaoyong Lu, Northwest Normal University Lanzhou, China.
I have (co-)authored over 90 conference and journal papers leading to over 1000 citations (h-index: 18). I am a frequent reviewer for IEEE, ACM and ISCA journals and conferences as well as serving on program and organisational committees. I am also a member of ACM, ISCA, IEEE and the IET.
Contact me at firstname.lastname@example.org