Alexandre De Masi is a researcher and educator at the University of Geneva. His research interests include Quality of Experience, mobile sensing, large language model and affective computing.
Througout multiple projects, he developed transdiciplinary skills in education, research, data science and machine learning. In the past, he has developed Android applications in Kotlin and Java for a research project on smartphone application Quality of Experience. He developed and deployed machine learning models for affective computing. Since 2018, he has been teaching the “Introduction to Programming” class at the University of Geneva in Switzerland. Additionally, he has experience writing video scenarios and educational content (quizzes and Jupyter Notebook) for MOOCs, including employing Bloom’s taxonomy to foster the creation of successful courses. His most recent MOOC is available on Coursera (“Introduction to Programming” with Scala in French), with more than 600 learners. The MOOC is also accessible to the University of Geneva students’ throughout our MOOC platform (+1000 students).
Download my resumé.
PhD in Information Systems and Services Science, 2023
University of Geneva (Switzerland)
MSC in Pervasive Computing and COMmunications for sustainable development (PERCCOM), 2015
University of Lorraine (France), Lappeenranta University of Technology (Finland), Luleå University of Technology (Sweden)
BSc in Engineering (Network and Telecommunication), 2012
Université Henry Poincarré (France)
In recent years, research on the Quality of Experience (QoE) of smartphone applications has received attention from both industry and academia due to the complexity of quantifying and managing it. This paper proposes a smartphone-embedded system able to quantify and notify smartphone users of the expected QoE level (high or low) during their interaction with their devices. We conducted two in the wild studies for four weeks each with Android smartphones users. The first study enabled the collection of the QoE levels of popular smartphone applications’ usage rated by 38 users. We aimed to derive an understanding of users’ QoE level. From this dataset, we also built our own model that predicts the QoE level for application category. Existing QoE models lack contextual features, such as duration of the user interaction with an application and the user’s current physical activity. Subsequently, we implemented our model in an Android application (called expectQoE) for a second study involving 30 users to maximize high QoE level, and we replicated a previous study (2012) on the factors influencing the QoE of commonly used applications. The expectQoE, through emoji-based notifications, presents the expected application category QoE level. This information enable the user’s to make a conscious choice about the application to launch. We then investigated whether if expectQoE improved the user’s perceived QoE level and affected their application usage. The results showed no conclusive user-reported improvement of their perceived QoE due to expectQoE. Although the participants always had high QoE application usage expectations, the variation in their expectations was minimal and not significant. However, based on a time series analysis of the quantitative data, we observed that expectQoE decreased the application usage duration. Finally, the factors influencing the QoE on smartphone applications were similar to the 2012 findings. However, we observed the emergence of digital wellbeing features as facets of the users’ lifestyle choices.