Ahmed Elhag

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I am a PhD student at the Department of Computer Science at the University of Oxford, advised b Michael Bronstein. My research focuses on the intersection of geometric deep learning, graph ML, and generative models. I’m particularly interested in how we can combine these approaches to develop robust ML methods that can accelerate progress in drug discovery and molecular design. 

Before my doctoral studies, I graduated from the African Masters of Machine Intelligence program at AIMS Senegal. I also interned at Apple MLR team where I worked on developing generative models for 3D and graph-structured data.

Contact: ahmed.elhag[at]cs.ox.ac.uk

Selected Publications

  1. Relaxed Equivariance via Multitask Learning
    Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, and 1 more author
    2025
  2. ICLR
    Manifold Diffusion Fields
    Ahmed A. A. Elhag, Yuyang Wang, Joshua M. Susskind, and 1 more author
    In ICLR, 2024
  3. ICML
    Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
    Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly, and 2 more authors
    In ICML, 2024