Maha El Garf

Hi, my name is Maha Elgarf and I’m from Cairo Egypt. I have had both my bachelor and master’s degrees in Digital Media Engineering and Technology from the German University in Cairo (GUC). In 2014, I went on a DAAD scholarship to perform my master’s thesis project at the university of Augsburg, Germany. Afterwards, I have been working as a teaching and research assistant at the GUC until July 2018.

Ever since I was an undergraduate, I was fond of projects where computer science meets psychology. Hence, grew my passion for affective computing. My previous research lies under the intersection area between human computer interaction and affective computing with a focus on the use of technology to improve the lives of people with disabilities or developmental/mental disorders. Research projects that I have worked on have targeted visually impaired people as well as autistic children.

I am currently working as a doctoral student under the supervision of Dr. Christopher Peters at KTH, the Royal Institute of Technology in Stockholm, Sweden. In my PhD I aim to investigate the adaptation of self-other similarity in terms of physical features and behavior to elicit prosociality between humans and virtual characters.

While numerous studies involving human faces have indicated that their appearance and behaviour may influence impressions of trust, far fewer studies have been conducted with artificial systems. This ESR project will investigate human impressions of artificial faces (high fidelity virtual faces and virtual projections on physical robots) during educational scenarios, focussing on the impact of trust and cooperation on learning performance. It will focus on the role of perceived self-similarity on learning efficiency through an adaptive face model capable of altering itself to become more/less similar to a user in terms of its appearance and behaviour, over the course of an interaction. A core question in the thesis will be the role of self-similarity on impressions of trust and cooperative behaviours over different timescales when other factors typically associated with trust (e.g. dominance) are varied. It will also consider possible negative side-effects associated with self-similarity, such as potential feelings of uncanniness, and their potential impact on learning performance.