Machine Learning Enthusiast

I started my journey with Machine Learning at 2018 when during my MRes course I began to explore machine learning tools and paradigms. Currenty I am in the process of obtaining my Ph.D. at Edinburgh University within CDT in Biomedical AI scheme. Under supervision of Dr Kianoush Nazarpour and Dr Agamemnon Krasoulis I investigate active learning for upper limb prosthetics. The objective of my work includes developing architecture for deep active learning to include human in the loop: by identifying low confidence signals annotated by human oracle in iterative manner to define personalised feature representation. My intention is also to establish an explainable feature space for motor-sensory feedback for users derived from dimensionality reduction approaches. Further on, to apply science to real life scenarios we develop a platform for real-time calibration: feedback interface, motor classification. Using fusion of machine learning paradigms and newly developed techniques during my research, I will enable the framework to evolve over time and adapt to human behaviour (continuous learning) and provide a responsive and interpretable system with co-adaptive and synchronous learning between a machine and a human.

Seasonal traveller. Salsa and bachata dancer. Bookworm. Curious about the world. Neuroscience, psychology and philosophy geek. Astronomy. Emerging technologies. Holistic habits and mindfulness.

My Research

Co-adaptive Human-Machine Learning

Prosthetic limbs can enable people with limb differences to regain their independence and return to work. Research on upper-limb prosthetic limbs aims to replicate the functionality given by our biological hands. Despite the advancement in prosthetic technologies, about 40% of amputees rejects their devices, citing the lack of function and individualised training as the key reasons. The goal of this PhD project is to enhance the function of upper-limb prostheses with advanced machine learning; informed with studies of human sensory-motor adaptation. Specifically, for the first time, the theoretical framework of active learning will be explored within the context of prosthesis control; allowing the human user to remain and shape effectively the control loop. Such an approach will enable the so-called, but yet unachieved objective of user-prosthesis co-adaptation and will lead to truly personalised prosthetics. This PhD project will begin with the adaptation of existing active learning frameworks for prosthetics control to understand better the limitations and opportunities. Further, the work will entail the development of novel theoretical and experimental paradigms to test the approach within the laboratory and in a real-life setting.