Hatice Gunes is an Associate Professor (University Senior Lecturer) in the Department of Computer Science and Technology, University of Cambridge. Her research expertise is in the areas of affective computing and social signal processing that lie at the crossroad of multimodal interaction, computer vision, signal processing, and machine learning fields applied to computer/robot mediated human-human interactions and human-robot interactions.
Her research work develops novel computational frameworks for analysing and understanding human behaviour, social signals and affect from facial expressions, vocal nuances, body posture/ gesture, and physiological signals, and for modelling these phenomena for creating socio-emotionally intelligent games, assistive technologies, virtual agents and robotic systems.
She has published over 100 papers in these areas. Her current research vision is to embrace the challenges present in the area of health and empower the lives of people through technology by continuing her research with applications to social robotics and wellbeing.
She has recently been awarded the prestigious EPSRC Early Career Fellowship (2019-2024) to investigate adaptive robotic emotional intelligence for wellbeing. Gunes is the President of the Association for the Advancement of Affective Computing (AAAC), the General Co-Chair of ACII 2019, and the Program Co-Chair of ACM/IEEE HRI 2020 and IEEE FG 2017. She is the Chair of the Steering Board of IEEE Transactions on Affective Computing, and has served as an Associate Editor of IEEE Transactions on Affective Computing, IEEE Transactions on Multimedia, and Image and Vision Computing Journal.
Dr Gunes aims to undertake research to answer the following questions: (i) how we can devise novel approaches to advance the interaction and adaptation capabilities of autonomous robots (e.g., humanoid robots, autonomous surface vehicles etc.); and (ii) how to utilise the adaptive intelligent robotic framework(s) for fostering human wellbeing. This will entail the design and evaluation of machine learning (ML) and artificial intelligence (AI) architectures on mobile robotic frameworks for continuous perception (sensing), inference (reasoning) and adaptation (action).