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Amaya Gallagher-Syed

Machine Learning Ă— Biology
Schmidt AI for Science Fellow · Imperial College London

GitHub Hugging Face Google Scholar

I’m a machine learning researcher and computational biologist. My work centres on identifiability and causality in biological systems: how to build, train and evaluate foundation models of the cell so that they capture genuine biological structure rather than surface correlations, and how to make their predictions interpretable and trustworthy for biologists.

I’m currently a Postdoctoral Research Fellow jointly at Queen Mary University of London and Imperial College London, supported by a Wellcome Trust Early Career Transition award. In September 2026 I’ll join Imperial’s I-X as an Eric and Wendy Schmidt AI for Science Fellow, where I’ll lead an independent research programme on identifiable representation learning and cross-modal causal discovery for biology.

I completed my Wellcome Trust PhD at Queen Mary in 2025, advised by Professors Michael Barnes, Myles Lewis and Greg Slabaugh, where I developed explainable deep learning for computational pathology. Earlier I trained in mathematics and computational biology at Queen Mary and the Universidad de Buenos Aires.

Research Interests

  • Identifiability and causality in biological systems
  • Training and evaluating single-cell and biological foundation models
  • Cross-modal, interventional and dynamical models of the cell
  • Gene regulatory networks and spatial transcriptomics

Always happy to talk about AI for science. You’ll find me on LinkedIn and through the links below.

News

Jun 15, 2026 Honoured to have been awarded an Eric & Wendy Schmidt AI for Science Fellowship 🎉. I’ll join Imperial College London’s I-X in September 2026 to start an independent research programme on identifiable representation learning and cross-modal causal discovery for biology.
May 20, 2026 CLAMP, a mechanistic probe of regulatory structure in single-cell foundation models under perturbation, was accepted as a Spotlight at the ICML 2026 Workshop on Generative and Agentic AI for Biology. ✨
Mar 10, 2026 Recognised as an Outstanding Reviewer for CVPR 2026.
Dec 08, 2025 Our study using machine learning to classify focus score and diagnose Sjögren’s disease from digitised salivary gland biopsies is published in The Lancet Rheumatology.
Nov 20, 2025 I was awarded my PhD 🎓. Thesis: Explainable Deep Learning for Multi-Stain and Multimodal Computational Pathology in Autoimmune Diseases. Huge thanks to my supervisors and collaborators!
Nov 03, 2025 Started as a Postdoctoral Research Fellow jointly at Queen Mary University of London and Imperial College London, supported by a Wellcome Trust Early Career Transition award.
Jul 14, 2025 Gave a keynote at the LatinX in AI workshop @ ICML 2025: From Buenos Aires to London: how broad scientific foundations shaped my AI research.
Jun 10, 2025 Invited talk on BioX-CPath at the AI Ă— Bio Conference, Wellcome Connecting Science.
May 01, 2025 PertEval-scFM, our benchmark for single-cell foundation models on perturbation-effect prediction, was accepted at ICML 2025.
Feb 27, 2025 BioX-CPath, biologically-driven, explainable diagnostics for multistain IHC computational pathology, was accepted at CVPR 2025. ✨
Sep 26, 2024 Recognised as a Top Reviewer (top 4%) for NeurIPS 2024.
Nov 20, 2023 MUSTANG, our multi-stain self-attention graph multiple-instance-learning pipeline for histopathology whole-slide images, was presented at BMVC 2023.

Selected Publications

  1. ICML-W
    CLAMP: A Mechanistic Probe of Regulatory Structure in Foundation Models under Single-Cell Perturbations
    Amaya Gallagher-Syed, Aaron Wenteler, Sebastian A. Martinez, and Gregory Slabaugh
    In ICML 2026 Workshop on Generative and Agentic AI for Biology, 2026
    Spotlight
  2. Preprint
    ProtoPathway: Biologically Structured Prototype-Pathway Fusion for Multimodal Cancer Survival Prediction
    Amaya Gallagher-Syed, Myles J. Lewis, Michael R. Barnes, and Gregory Slabaugh
    2026
    Under review
  3. ICML
    PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
    Aaron Wenteler, Martina Occhetta, Nikhil Branson, Magdalena Huebner, Victor Curean, William Dee, William Connell, Alex Hawkins-Hooker, Pui Chung, Yasha Ektefaie, C. M. Cordova, and Amaya Gallagher-Syed
    In International Conference on Machine Learning (ICML), 2025
  4. CVPR
    BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
    Amaya Gallagher-Syed, Henry Senior, Omnia Alwazzan, Elena Pontarini, Michele Bombardieri, Costantino Pitzalis, Myles J. Lewis, Michael R. Barnes, Luca Rossi, and Gregory Slabaugh
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025