Visit my personal research website for further info: www.alishafti.com
Dr. Shafti is a Research Associate in the Brain and Behaviour Lab at the department of Bioengineering, Imperial College London.
He has a PhD in Robotics from King's College London with thesis titled: "Electromyography-guided and Robot-assisted Ergonomics", where he focused on obtaining an objective understanding of human behaviour with respect to their personal experience of physical comfort and ergonomics, as well as the creation of novel human-robot interaction protocols based on this understanding. The research also involved the design and creation of novel textile-based muscle activity monitoring wearable garments which serve as the tool in implementing the above.
During his PhD, he served as lead research assistant and manager of all research and administrative activities within the EU H2020 Project FourByThree, at King's College London. He also served as PhD researcher on the EU FP7 STIFF-FLOP project. His research has led to more then 10 peer-reviewed papers in high impact factor journals and international premium conference proceedings within robotics and biomedical engineering. In parallel to his studies, he has acted as advisor/co-supervisor for six successful MSc projects, researching wearable biomedical sensors, soft sensors for soft robotics applications and human factors in human-robot interaction.
He is now continuing his research into human-centred and behaviour-driven robotics in the EU H2020 Project eNHANCE, while managing relevant research activities within the Brain and Behaviour Lab at Imperial College London.
Beyret B, Shafti SA, Faisal A, Dot-to-dot: explainable hierarchical reinforcement learning for robotic manipulation, IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Pages:1-6, ISSN:2153-0866
Shafti SA, Orlov P, Faisal A, Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping, International Conference on Robotics and Automation 2019, IEEE, ISSN:2152-4092