My main area of research is in Human-Centered AI (HCAI), a highly interdisciplinary field at the intersection of AI, HCI, and Data Science. Specifically, the main objective of my research is to create intelligent systems that can better support effective human-computer interaction by integrating traditional HCI approaches with innovative AI techniques that enable advanced forms of interaction. To this end, I have focused on designing intelligent user-adaptive systems that can: (i) Recognise the specific needs, affect and abilities of their users (a task called user modeling); (ii) Provide users with a personalized interaction experience by adapting in real-time to the detected user’s needs and abilities.
I have conducted several user studies to investigate the user perception, impacts and benefits of such user adaptation for a variety of systems, including intelligent educational environments, pedagogical virtual agents, visualizations, decision support systems, and citizen engagement platforms.
Room 312, 26-00, 3rf floor
4 place Jussieu, 75252, Paris, Cedex 05, France
sebastien.lalle * at * lip6 * dot * fr
EPU (Polytech) : GM5A INF
SU : NSI
Platform to collect, process and analyse eye-tracking data at runtime, as well as to build eye-tracking-based machine learning models and drive real-time adaptive interaction.
[Source code - User manual - Demo video]
Library in Python for processing eye gaze data. EMDAT can calculate a comprehensive list of eye gaze features for each user. Additionally, EMDAT has built-in mechanisms for data preprocessing and clean up which makes it a valuable toolkit for researchers.
[Source code - User manual]
Online test to evaluate a user's verbal working memory, i.e., the ability to mentally store and retrieve verbal/textual information.