My PhD is entitled “Design, understanding and appropriation of teachable machines” and focus on how people use the ML technology in various context: pedagogy and artistic practice.
Machine Learning (ML) has become ubiquitous in modern interactive technology because of the wide range of complex tasks it can handle without requiring explicit programming but providing data instead. However, most ML systems offer users little agency in the way models are trained from data. In this work, we investigated the ML literature for techniques that could enhance the user appropriation of the ML technology (e.g. train a model with fewer examples among others) like Active Learning, model uncertainty feedback or Transfer Learning.
We also studied how novice users understand and interact with these "teachable" systems. We developed a interactive sketch recognition application and conducted a workshop and a study with novice users to understand their strategies, beliefs and (mis)understandings while "teaching" the system from scratch with their drawings.
I am currently studying how visual, media and performance artists use and apprehend Machine Learning models in their artistic practice.
In 2018, I graduated from the master program Acoustic, Signal Processing and Computer Science applied to Music (master ATIAM in Sorbonne Université) as well as from the École Normale Supérieure in computer science. I plan to defend my thesis in Autumn 2021.