Téo Sanchez

teo [dot] sanchez [at] hm [dot] edu

I am a postdoctoral researcher specializing in human-AI interaction. I earned my Ph.D. as a member of the ExSitu research team in LISN, and the HCI team in ISIR.

My primary focus is on understanding how people comprehend and interact with AI systems, alongside designing meaningful and effective interactions with machine learning algorithms.

In particular, my work focuses on understanding and developing teachable ML systems that enable users to simultaneously generate the knowledge data of interest and experience the model’s predictions and improvements. This approach creates simultaneous usage and development and can potentially advance more personalized and transparent AI systems for non-expert users in AI, either for pedagogy or in niche domains (e.g., science, medecine, arts).

📰 News

Sep 15, 2023 Our paper Comparing teaching strategies of a machine learning-based prosthetic arm has been accepted at IUI 2024. Many thanks to Vaynee Sungeelee, the leader of the research, and co-authors Baptiste Caramiaux and Nathanaël Jarrassé.
Sep 15, 2023 I started a 2.5 years postdoc at the Munich Center for Digital sciences and AI (MUC.DAI) at Hochschule München Universsity of Applied Sciences. I will focus on AI in Culture and Art.
May 19, 2023 🏅 Very excited to learn that Examining the Text-to-Image Community of Practice: Why and How do People Prompt Generative AIs? got an honorable mention at C&C 2023!
Apr 11, 2023 Our research paper Examining the Text-to-Image Community of Practice: Why and How do People Prompt Generative AIs? with Selas Studio is conditionally accepted at C&C 2023. Camera-ready version coming soon !

📄 Selected publications

  1. Interactive Machine Teaching with and for Novices
    Téo Sanchez
    Université Paris-Saclay, 2022
    🏆 Best dissertation award 2022 from the AFIHM
  2. Deep Learning Uncertainty in Machine Teaching
    Téo SanchezBaptiste CaramiauxPierre Thiel, and Wendy Mackay
    In IUI 2022-27th Annual Conference on Intelligent User Interfaces, 2022
    🏆 Best paper award
  3. How do People Train a Machine? Strategies and (Mis) Understandings
    In CSCW 2021-The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, 2021
  4. Marcelle: Composing Interactive Machine Learning Workflows and Interfaces
    Jules FrançoiseBaptiste Caramiaux, and Téo Sanchez
    In Annual ACM Symposium on User Interface Software and Technology (UIST’21), 2021