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Prof. Demiris' research interests include Artificial Intelligence, Machine Learning, and Intelligent Robotics, particularly in intelligent perception, multi-scale user modelling, and adaptive cognitive control architectures in order to determine how intelligent robots can generate personalised assistance to humans in order to improve their physical, cognitive and social well being. He participates in multiple national and international research projects at the interface between the theory and application of interactive learning systems. A major current line of research is centred around the Royal Academy of Engineering Chair in Emerging Technologies (Personal Assistive Robotics, 2019-2029), where he researches AI and machine learning methods for long-term modelling of combined human physical, physiological and cognitive states to allow smart robots to assist humans to achieve their full potential. With multiple international partners (Berkeley, CSHL, Duke, Harvard, NYU and USC in the USA, and UCL & Essex in the UK) within the MURI USA/UK research project "Multimodal Brain Machine Interfaces for Enhancing Decision Accuracy", he is researching the use of human-centred information processing from cameras and wearable sensors to detect human states during driving, in order to intelligent control the visualisation of contextual information to assist the human driver. With multiple European partners (Aalto (Finland), CTU (Czech Republic), UPC (Catalunya), U Bordeaux (France)), he is also researching the use of multimodal information (visual, tactile, and text) to build rich representations of complex (e.g. articulated) objects to assist their intelligent manipulation by robots. With multiple UK collaborators (Herriot-Watt University and University of Manchester) he collaborates through the "UKRI Trustworthy Autonomous Systems Node on Trust" project on machine learning algorithms for acquiring, maintaining and repairing human trust to robotic assistive systems. Finally, within the InnovateUK "D-RISK" project, he collaborates with colleagues from Imperial's Transport Systems Laboratory, DRISK.AI, Claytex Services, Transport for London, and DG Cities, to research how computer vision and machine learning can be used to enhance the perception and handling of "edge-cases" in autonomous driving. In addition to the above, he collaborates widely with academic, clinical and commercial partners to bring AI and Intelligent Robotics to impactful applications.
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ROYAL SOCIETY OPEN SCIENCEno. 1 (2024): 221620-221620
Yun Zhong,Yiannis Demiris
Proceedings of the AAAI Conference on Artificial Intelligenceno. 9 (2024): 10270-10278
Proceedings of the AAAI Conference on Artificial Intelligenceno. 5 (2024): 4659-4666
TASpp.29:1-29:5, (2023)
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ICRApp.11950-11956, (2023)
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Artif. Intell. (2023): 103923-103923
TASpp.37:1-37:7, (2023)
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AAAIno. 2 (2023): 2146-2154
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TASpp.34:1-34:5, (2023)
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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERINGno. 99 (2023): 1-12
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