Ph.D. - Electrical Engineering (UNSW)
M.E. - Microelectronics (RMIT)
Dr. Siyuan Chen obtained the PhD degree in Electrical Engineering from UNSW Sydney. After her PhD, she worked as a Research Intern at NII Tokyo, Research Fellow in the School of Computing and Information Systems at the University of Melbourne, Research Visitor at INRIA, France, then joined UNSW Electrical Engineering and Telecommunications as a Research Fellow.
Dr. Chen is known internationally for her research on eye activity computing and mental state analysis. Her group has pioneered research methods for physiological computing, evidenced by publications in IEEE Transactions on Cybernetics and IEEE Transactions on Affective Computing.
She has been awarded NICTA Postgraduate Scholarship (2011-2013), Project Scholarship (2011-2013), Commercialization Training Scheme Scholarship (2012) and Australia Endeavor Fellowship (2015).
CALL FOR PAPER - Special issue on Frontiers in Computer Science
Recognizing the State of Emotion, Cognition and Action from Physiological and Behavioural Signals
Deadline: 13th November, 2021. Please visit: fron.tiers.in/rt/20500
$711K of US Army for the research on wearable multimodal behavioural signal modelling for longitudinal automatic task performing as joint CI, 2019
$23K Australia Endeavor Fellowship, 2015
2015, recipient of Australia Endeavor Fellowship to conduct a research proposal in an overseas host institution. The project as carried out at INRIA, France.
2013, recipient of NII Internship program, NII Tokyo
2012, recipient of the Commercialization Training Scheme Scholarship, UNSW
2011, recipient of the NICTA Postgraduate Award Scholarship and The NICTA Research Project Award Scholarship, NICTA
I have contributed to the overall research performance within the signal processing group in the EET School. Significantly based on my unique research built from my PhD, I successfully completed the projects of Automatic Task Analysis for Wearable Computing, and Human Behaviour Modelling and Analysis based on Processing of Wearable Sensor Signals with my colleagues. These research projects are conducted using a wearable, non-invasive approach with strengths in signal processing and machine learning. Significant outcomes have been produced, demonstrated by publishing high-impact journal papers, and attracting repeated funding.
Topic editor of a special issue on Frontiers in Computer Science
2020 – present, Associate Fellow of AdvancedHE
2011 – present, IEEE member
2020 – present, ACM member
My teaching interests are in the areas of fundamental electronics, electronic circuits design, signal processing and engineering ethics. I taught first year Engineering Design and Innovation course (ENGG1000) and tutored first year Electrical Engineering course (ELEC1111), second year Circuits and Signals course (ELEC2134) and Modelling and Simulation course (ELEC2146), third year Signal Processing course (ELEC3104), and fourth year & postgraduate Strategic Leadership and Ethics course (ELEC4622 & GSOE9510). Whatever my teaching role is, I endeavour to engage, challenge, and inspire learning in my students.