Anissa Alloula

prof_pic.jpg

Hi, I’m Anissa, a 3rd year PhD student in Machine Learning at the University of Oxford!

I’m broadly interested in how ML models need to be adapted because of the non-i.i.d. nature of real-world data. So far, I have explored how model performance disparaties emerge, and if and when bias mitigation methods can be used to tackle them. Beyond fairness, I am interested in responsible AI-related questions like generalisation and uncertainty, as well as more fundamental topics like data-centric ML and scaling. This winter/spring, I am interning at Cohere, doing AI safety research. Previously, I completed my BSc at Imperial College London.

Outside of research, I spend a lot of (too much?) time exercising, particularly running, triathlon training, and playing tennis.

I’m always happy to connect and discuss my/your research so please reach out if any of the above sounds interesting!

Selected Publications

  1. Under Review
    Representation Invariance and Allocation: When Subgroup Balance Matters
    Anissa Alloula, Charles Jones, Zuzanna Skorniewska, and 2 more authors
    Oct 2025
  2. ICML
    Subgroups Matter for Robust Bias Mitigation
    Anissa Alloula, Charles Jones, Ben Glocker, and 1 more author
    In International Conference on Machine Learning, (ICML), Jul 2025
  3. MICCAI
    On Biases in a UK Biobank-based Retinal Image Classification Model
    Anissa Alloula, Rima Mustafa, Daniel R. McGowan, and 1 more author
    In FAIMI @ MICCAI, Oct 2024