Sébastien Lallé

Assistant Professor (Maître de Conférence), Sorbonne University, Paris

My Research

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.

Teaching and Research Interests:

Human-Centered Artificial Intelligence:
User Modeling & Adaptive Systems
Personalization
Multimodal & Sensor Data Analysis
Applied Machine Learning
Interpretable/Explainable AI
Affective Computing
Technology-Enhanced Learning:
Artificial Intelligence in Education (AIED)
Student Modeling
Intelligent Tutoring and Scaffolding
Intelligent Pedagogical Agents
Educational Data Mining
Learning Analytics
Open-Ended Learning Environments
Human-Computer Interaction:
Eye-Tracking-Based Interaction
Information Visualizations
Intelligent User Interfaces (IUI)
Public Engagement Platform
Decision Support
Human Factors

Contact

Room 312, 26-00, 3rf floor
4 place Jussieu, 75252, Paris, Cedex 05, France

sebastien.lalle * at * lip6 * dot * fr

About me

Full CV in PDF

Teaching / Enseignements

EPU (Polytech) : GM5A INF

SU : NSI

Publications

Journal papers:

  • TIIS'21. Lallé, S., Barral, O., Iranpur, A, and Conati, C. 2021. Effect of Adaptive Guidance and Visualization Literacy on Gaze Attentive Behaviors and Sequential Patterns on Magazine-Style Narrative Visualizations. ACM Transactions on Interactive Intelligent Systems 11, 3-4, Article 28. DOI: doi.org/10.1145/3447992. [link]
  • TIIS'20. Conati, C., Lallé, S., Rahman, M.A., and Toker, D. 2020. Comparing and Combining Interaction Data and Eye Tracking Data for the Real-Time Prediction of User Cognitive Abilities in Visualization Tasks. ACM Transactions on Interactive Intelligent Systems 10, 2, Article 12. DOI: doi.org/10.1145/3301400. [link]
  • TVCG'19. Lallé, S., Toker, D., and Conati, C. 2019. Gaze-Driven Adaptive Interventions for Magazine-Style Narrative Visualizations. IEEE Transactions on Visualization and Computer Graphics 27, 6, pp. 2941-2952. DOI: doi.org/10.1109/TVCG.2019.2958540. [link].
  • UMUAI'16. Lallé, S., Conati, C., and Carenini, G. 2016. Prediction of Individual Learning Curves across Information Visualizations. User Modeling and User-Adapted Interaction 26, 4, pp. 307-345. DOI: doi.org/10.1007/s11257-016-9179-5 [link]

Strictly refereed international conference papers:

  • AIED'21 Lallé, S., Murali, R., Conati, C., and Azevedo, R. 2021. Predicting Co-Occurring Emotions from Eye-Tracking and Interaction Data in MetaTutor. In International Conference on Artificial Intelligence in Education, pp. 241-254, online. Springer. [link]
  • LAK'21. Lallé, S., Yalcin, O.N., and Conati, C. 2021. Combining Data-Driven Models and Expert Knowledge for Personalized Support to Foster Computational Thinking Skills. In Proceedings of the 11th International Conference on Learning Analytics and Knowledge, pp. 375–385, online. ACM & SOLAR [link].
  • VIS'20. Lallé, S., Wu, T., and Conati, C. 2020. Gaze-Driven Links for Magazine Style Narrative Visualizations. In Proceedings of the 31st IEEE Visualization Conference, pp. 166-170, online. IEEE. [link].
  • AIED'20. Lallé, S., and Conati, C. 2020. A Data-Driven Student Model to Provide Adaptive Support during Video Watching Across MOOCs. In Proceedings of the 21st International Conference on Artificial Intelligence in Education, pp. 282-295, online. Springer. [link].
  • ICMI'20. Barral, B., Lallé, S., Guz, G., Iranpur, A., and Conati, C. 2020. Eye-Tracking to Predict User Cognitive Abilities and Performance for User-Adaptive Narrative Visualizations. In Proceedings of the 22nd International Conference on Multimodal Interaction, pp. 163–173, online. ACM. [link].
  • IUI'20. Barral, B., Lallé, S., and Conati, C. 2020. Understanding the Effectiveness of Adaptive Guidance for Narrative Visualization: A Gaze-Based Analysis. In Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 1-9, online. ACM. [link].
  • IUI'19. Lallé, S. and Conati, C. 2019. The role of user differences in customization: a case study in personalization for infovis-based content. In Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 329-339. Los Angeles, USA: ACM [link].
  • ETRA'19. Lallé, S., Conati, C., and Toker, D. 2019. A gaze-based experimenter platform for designing and evaluating adaptive interventions in information visualizations. In Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications, No. 60. Denver, USA: ACM [link].
  • AAMAS'18. Lallé, S., Conati, C., and Azevedo, R. 2018. Prediction of Student Achievement Goals and Emotion Valence during Interaction with Pedagogical Agents. In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp. 1222–1231. Stockholm, Sweden: IFAAMAS. [link]
  • AIED'17. Lallé, S., Taub, M., Mudrick, N., Conati, C., and Azevedo, R. 2017. The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents during Learning with MetaTutor. In Proc. of the 18th International Conference on Artificial Intelligence in Education, pp. 149-161. Wuhan, China: Springer [link]
  • EDM'17. Lallé, S., Conati, C., Taub, M., Mudrick, N., and Azevedo, R. 2017. On the Influence on Learning of Student Compliance with Prompts Fostering Self-Regulated Learning. In Proc. of the 10th International Conference on Educational Data Mining, pp. 120-127. Wuhan, China: Springer [link]
  • IJCAI'17. Conati, C., Lallé, S., Rahman, M.A., and Toker, D. 2017. Further Results on Predicting Cognitive Abilities for Adaptive Visualizations. In Proc. of the 26th International Joint Conference on Artificial Intelligence, pp. 1568-1574. Melbourne, Australia: AAAI [link].
  • IUI'17. Toker, D., Lallé, S., and Conati, C. 2017. Pupillometry and Head Distance to the Screen to Predict Skill Acquisition During Information Visualization Tasks. In Proc. of the 22nd International Conference on Intelligent User Interfaces, pp. 221-231. Limassol, Cyprus: ACM. [link]
  • IVA'16. Lallé, S., Mudrick, N., Taub, M., Grafsgaard, J., Conati, C., and Azevedo, R. 2016. Impact of Individual Differences on Affective Reactions to Pedagogical Agents Scaffolding. In Proc. of the 16th International Conference on Intelligent Virtual Agents, pp. 269-282. Los Angeles, CA, USA: Springer [Best paper award - link].
  • IJCAI'16. Lallé, S., Conati, C., and Carenini, G. 2016. Predicting Confusion in Information Visualization from Eye Tracking and Interaction data. In Proc. of the 25th International Joint Conference on Artificial Intelligence, pp. 2529-2535. New York, NY, USA: AAAI Press. [link]
  • IUI'15. Lallé, S., Toker, D., Conati, C., and Carenini, G. 2015. Prediction of Users' Learning Curves for Adaptation while Using an Information Visualization. In Proc. of the 20th International Conference on Intelligent User Interfaces, pp. 357-368. Atlanta, GA, USA: ACM. [link]
  • AAAI'15. Conati, C., Carenini, G., Toker, D., and Lallé, S. 2015. Towards User-Adaptive Information Visualization. In Proc. of the 29th AAAI Conference on Artificial Intelligence, pp. 4100-4106. Austin, TX, USA: AAAI. [link]
  • AIED'13. Lallé, S., Mostow, J., Luengo, V., and Guin, N. 2013. Comparing Student Models in Different Formalisms by Predicting Their Impact on Help Success. In Proc. of the 16th International Conference on Artificial Intelligence in Education, pp. 161-170. Memphis, TN, USA: Springer. [Nominee for the best paper award - link]
  • ITS'12. Goel, G., Lallé, S., and Luengo, V. 2012. Fuzzy Logic Representation for Student Modelling. In Proc. of the 11th International Conference on Intelligent Tutoring Systems, pp. 428-433. Chania, Greece: Springer. [Short - link]

Strictly refereed conference papers (in French):

  • EIAH'13. Lallé, S., Luengo, V., and Guin, N. 2013. Assistance à la conception de techniques de diagnostic des connaissances. In Proc. of 6th Conference on "Environnements Informatiques pour l'Apprentissage Humain", pp. 203-214. Toulouse, France. [link]
  • TICE'12. Lallé, S., Luengo, V., and Guin, N. 2013. Méthodologie d'assistance pour la comparaison de techniques de diagnostic des connaissances. In Proc. of 8th Conference on "Technlogies de l'Information et de la Communication pour l'Enseignement", pp. 6-16. Lyon, France. [Nominee for the best paper award - link]
  • EGC'11. Lallé, S., and Luengo, V. 2011. Intégration de données haptiques brutes dans des systèmes experts de diagnostic des connaissances. In Proc. of 11th Conference on "Extraction et Gestion de Connaissance", pp. 599-610. Brest, France. [link]

Other publications:

  • PAIR'19-workshop. Lallé, S., and Conati, C. 2019. A Framework to Counteract Suboptimal User-Behaviors inExploratory Learning Environments: an Application to MOOCs. In AAAI 2019 Workshop on Plan, Activity, and Intent Recognition (in conjunction with AAAI 2019), Paper 15. Honolulu, HI, USA: AAAI Press. [link]
  • UMAP'17-workshop. Lallé, S.. Conati, C., and Carenini. G. 2017. Impact of Individual Differences on User Experience with a Visualization Interface for Public Engagement. In Proc. of the 2nd International Workshop on Human Aspects in Adaptive and Personalized Interactive Environments (in conjunction with UMAP 2017). Bratislava, Slovakia: ACM. [Workshop, to appear]
  • STELLAR'13-workshop. Bouhineau, D., Lallé, S., Luengo, V., Mandran, N., Ortega, M., and Wajeman, C. 2013. Share data treatment and analysis processes inTechnology enhanced learning. In Workshop Data Analysis and Interpretation for Learning Environments, 2nd STELLARnet Alpine Rendez-Vous. Autrans, France. [Workshop - link]
  • AIED'13-poster. Lallé, S., Luengo, V., and Guin, N. 2013. Assistance in building student models using knowledge representation and machine learning. In Proc. of the 16th International Conference on Artificial Intelligence in Education, pp. 754-757. Memphis, TN, USA: Springer. [Poster - link]
  • EDM'11-poster. Lallé, S., and Luengo, V. 2011. Learning Parameters for a Knowledge Diagnostic Tools in Orthopedic Surgery. In Proc. of 4th International Conference on Educational Data Mining, pp. 369-370. Eindhoven, The Netherlands. [Poster - link]

Thesis:

  • Phd Thesis: Lallé, S. 2013-12-11. Assistance à la construction et à la comparaison de techniques de diagnostic des connaissances dans les Environnements Informatiques pour l’Apprentissage Humain. Université de Grenoble/University of Grenoble.
    Under the supervision of Vanda Luengo and Nathalie Guin. Jury: Marie-Christine Rousset, Michel Desmarais, Jean-Marc Labat, Sebastian Ventura, Nicolas Delestre. [link - talk]

Tools

Experimenter Platform for Real-Time Eye-Tracking Application

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]

EMDAT: Eye Movement Data Analysis Toolkit

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 Operation-Word Task test

Online test to evaluate a user's verbal working memory, i.e., the ability to mentally store and retrieve verbal/textual information.
    [Link]