We are excited to host the first-ever Gaze Meets ML workshop on December 3rd, 2022 in conjunction with NeurIPS 2022. The workshop will take place in-person at New Orleans! We’ve got a great lineup of speakers.

We would like to thank our sponsors for their support. If you are interested in sponsoring, please find more information here.


Eye gaze has proven to be a cost-efficient way to collect large-scale physiological data that can reveal the underlying human attentional patterns in real life workflows, and thus has long been explored as a signal to directly measure human-related cognition in various domains. Physiological data (including but not limited to eye gaze) offer new perception capabilities, which could be used in several ML domains, e.g., egocentric perception, embodiedAI, NLP, etc. They can help infer human perception, intentions, beliefs, goals and other cognition properties that are much needed for human-AI interactions and agent coordination. In addition, large collections of eye-tracking data have enabled data-driven modeling of human visual attention mechanisms, both for saliency or scanpath prediction, with twofold advantages: from the neuroscientific perspective to understand biological mechanisms better, from the AI perspective to equip agents with the ability to mimic or predict human behavior and improve interpretability and interactions.

With the emergence of immersive technologies, now more than any time there is a need for experts of various backgrounds (e.g., machine learning, vision, and neuroscience communities) to share expertise and contribute to a deeper understanding of the intricacies of cost-efficient human supervision signals (e.g., eye-gaze) and their utilization towards by bridging human cognition and AI in machine learning research and development. The goal of this workshop is to bring together an active research community to collectively drive progress in defining and addressing core problems in gaze-assisted machine learning.

Call for Papers

We welcome submissions that present aspects of eye-gaze in regards to cognitive science, psychophysiology and computer science, or propose methods on integrating eye gaze into machine learning. We are also looking for applications from radiology, AR/VR, autonomous driving, etc. that introduce methods and models utilizing eye gaze technology in their respective domains.

Topics of interest include but are not limited to the following:

  • Understanding the neuroscience of eye-gaze and perception.
  • State of the art in incorporating machine learning and eye-tracking.
  • Annotation and ML supervision with eye-gaze.
  • Attention mechanisms and their correlation with eye-gaze.
  • Methods for gaze estimation and prediction using machine learning.
  • Unsupervised ML using eye gaze information for feature importance/selection.
  • Understanding human intention and goal inference.
  • Using saccadic vision for ML applications.
  • Use of gaze for human-AI interaction and agent coordination in multi-agent environments.
  • Eye gaze used for AI, e.g., NLP, Computer Vision, RL, Explainable AI, Embodied AI, Trustworthy AI.
  • Ethics of Eye Gaze in AI
  • Gaze applications in cognitive psychology, radiology, neuroscience, AR/VR, autonomous cars, privacy, etc.

Important Dates

    Submission due: 29th September 2022
    Reviewing starts: 30th September 2022
    Reviewing ends: 10th October 2022
    Notification of acceptance: 14th October 2022
    Camera ready: 5th November 2022

All dates listed are 23:59 Anywhere on Earth


The workshop will feature two tracks for submission: a full, archival proceedings track with accepted papers published in the Proceedings for Machine Learning Research (PMLR) and a non-archival, extended abstract track. Submissions to either track will undergo the same double-blind peer review. Full proceedings papers can be up to 9 pages and extended abstract papers can be up to 4 pages (both excluding references and appendices). Authors of accepted extended abstracts (non-archival submissions) retain full copyright of their work, and acceptance of such a submission to Gaze Meets ML does not preclude publication of the same material in another archival venue (e.g., journal or conference).

  • References and appendix should be appended into the same (single) PDF document, and do not count towards the page count.

Keynote Speaker

Jürgen Schmidhuber, Ph.D.


Scott W. Linderman, Ph.D. Gabriel A. Silva, Ph.D. Claudia Mello-Thoms, MS, Ph.D.
Stanford UC San Diego University of Iowa
Miguel P. Eckstein, Ph.D. Tobias Gerstenberg, MSc, Ph.D.
UC Santa Barbara Stanford


Ismini Lourentzou, Ph.D. Joy Tzung-yu Wu, MD, MPH. Satyananda Kashyap, Ph.D. Alexandros Karargyris, Ph.D.
Virginia Tech Stanford, IBM Research IBM Research IHU Strasbourg
Leo Anthony Celi, MD, MSc, MPH Ban Kawas, Ph.D. Sachin Talathi, Ph.D.
MIT Meta, Reality Labs Research Meta, Reality Labs Research

Program Committee

  • Prasanth Shah (Intel)
  • Sameer Antani (NIH)
  • Sema Candemir (Eskişehir Technical University)
  • Georgios Exarchakis (IHU)
  • Henning Muller (HES-SO)
  • Maria Xenochristou (Stanford)
  • Spyros Bakas (UPenn)
  • Dakuo Wang (IBM Research)
  • Brendan David-John (Virginia Tech)
  • Hoda Eldardiry (Virginia Tech)
  • Daniel Gruhl (IBM Research)
  • Anna Lisa Gentile (IBM Research)
  • Dario Zanca (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Endorsements & Acknowledgements

  • We are a MICCAI endorsed event:

  • Eye gaze logo designed by Michael Chung