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Dr Jonathan Davies is a chronic pain researcher working to improve how pain is understood, assessed and treated. His research focuses on pain phenotyping, precision pain care, and the psychological, physiological and contextual factors that shape pain and treatment response.

At UQ’s Centre for Innovation in Pain and Health Research, he contributes to DISCERN, an NHMRC Synergy-funded multidisciplinary research program developing and validating new approaches to pain assessment. A major focus of his work is DECIPHER PAIN, a standardised, multimodal tool designed to characterise the relative contributions of nociceptive, neuropathic and nociplastic pain and support more personalised care.

His research integrates clinical assessment, quantitative sensory testing, experimental pain methods, psychophysiology, digital health and population data. He is particularly interested in how physiological signals, behaviour, language, psychosocial context and real-world health data can be combined to better understand individual differences in pain and treatment outcomes.

His broader expertise includes placebo and nocebo effects, mindfulness, mental health, quality of life, health outcome measurement and population health. His work aims to support more precise, scalable and patient-centred approaches to pain research and care.

Dr Chelsea Dobbins is an Associate Professor within the School of Electrical Engineering and Computer Science (EECS) at The University of Queensland. She has a background in Software Engineering with expertise in Digital Signal Processing, Applied Machine Learning, and Human–Computer Interaction.

Her research focuses on the use of sensing technologies and intelligent data analysis to understand behaviour, emotion, and wellbeing, with applications spanning both human and animal contexts. In addition to her work on emotion detection using smartphones and wearable sensors within personal informatics and digital health, her research increasingly explores the use of computational technologies for animal conservation and welfare, including the application of machine learning, and mobile/embedded sensing to monitor behaviour and environmental interactions in animals.

Her work intersects areas such as affective computing, pervasive computing, digital health, conservation technology, bio‑inspired sensing, human–computer interaction, mobile and wearable sensors, and physiological computing.

Human-centred design of interactive systems

Stephen Viller is a researcher and educator in human-centred design methods, particularly applied to designing social, domestic and mobile computing technologies, and understanding how people's interactions in everyday settings inform the design of such technologies. He has over 30 years of experience in Computer Supported Cooperative Work (CSCW), Interaction Design, and Human-Computer Interaction (HCI) research, where he has focused on bridging disciplines and diverse methodological perspectives. He has concentrated on qualitative methods, particularly observational fieldwork, contextual interviews, diary studies and field trips, but also increasingly on more ‘designerly’ approaches such as cultural probes, low-fidelity prototypes, speculative design.

Stephen is an Associate Professor in the School of Electrical Engineering & Computer Science (EECS), where he leads the Human-Centred Computing discipline. He is also UQ's Theme Leader for the Digital Worlds and Disruptive Technologies theme in the UQ-Exeter Institute, and national chair of CHISIG, the Computer-Human Interaction Special Interest Group of the Human Factors and Ergonomics Society of Australia (HFESA). From 2016-2019 he was the EECS (ITEE at the time) Director of Coursework Studies (Chair of T&L committee) and from 2011-2016 he was Program Director of the Bachelor of Multimedia Design and Master of Interaction Design. His publications span various interdisciplinary journals and conferences in HCI/CSCW and technology design. He has a BSc (Hons) Computation (UMIST), MSc Cognitive Science (Manchester) and PhD Computing (Lancaster).