I am currently an EPSRC Doctoral Prize Fellow at the University of Bristol. I specialise in machine learning techniques applied to astronomical data, with a focus on improving active learning performance and exploring the utility of weak supervision. My work has enabled me to be involved and consult in the morphology classification pipeline of the recently launched ESA telescope Euclid.

Current Research

I am based in the Data-Intensive Astronomical Analysis research group where I am working on:

  • Improving astronomical classification using Generative Models.
  • Parameter estimation of simulations using SBI.
  • Calibration of simulations within Digital Twins.
  • Creating interactive software for researchers to make use of cutting-edge machine learning techniques.

PhD Research

My PhD: Improving The Practicality of Active Learning Pipelines in Real-World Problem Settings: A Case Study in The Classification of Astronomical Data explored the following topics:

  • Provides a How-to guide for applying Active Learning to real-world data for experts from any scientific domain.
  • Creating novel query strategies to improve accuracy and reduce labelling costs for active learning.
  • Combining the use of weak supervision methods with active learning to improve performance on datasets where labels are scarce, noisy, or difficult to obtain.
  • Using active learning for galaxy morphology classification with noisy image data and unreliable labels.
  • Source classification (star, galaxy, AGN, QSO separation) using Active Learning and Outlier Detection methods.
  • Creating interactive software for researchers to make use of cutting-edge machine learning techniques.

Supervisory Team

Sotiria Fotopoulou , Oliver Ray and Malcolm Bremer

Recent Publications

  • Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model
    M. Siudek, M. Huertas-Company, M. Smith, G. Martinez-Solaeche, F. Lanusse, S. Ho, E. Angeloudi, P. A. C. Cunha, H. Domínguez Sánchez, M. Dunn, Y. Fu, P. Iglesias-Navarro, J. Junais, J. H. Knapen, B. Laloux, M. Mezcua, W. Roster, G. Stevens, J. Vega-Ferrero, Euclid Consortium
    Arxiv Preprint
    PDF DOI
    Modern astronomical surveys, such as the Euclid mission, produce high-dimensional, multi-modal data sets that include imaging and spectroscopic information for millions of galaxies. These data serve as an ideal benchmark for large, pre-trained multi-modal models, which can leverage vast amounts of unlabelled data. In this work, we present the first exploration of Euclid data with AstroPT, an autoregressive multi-modal foundation model trained on approximately 300000 optical and infrared Euclid images and spectral energy distributions (SEDs) from the first Euclid Quick Data Release. We compare self-supervised pre-training with baseline fully supervised training across several tasks: galaxy morphology classification; redshift estimation; similarity searches; and outlier detection. Our results show that: (a) AstroPT embeddings are highly informative, correlating with morphology and effectively isolating outliers; (b) including infrared data helps to isolate stars, but degrades the identification of edge-on galaxies, which are better captured by optical images; (c) simple fine-tuning of these embeddings for photometric redshift and stellar mass estimation outperforms a fully supervised approach, even when using only 1% of the training labels; and (d) incorporating SED data into AstroPT via a straightforward multi-modal token-chaining method improves photo-z predictions, and allow us to identify potentially more interesting anomalies (such as ringed or interacting galaxies) compared to a model pre-trained solely on imaging data.
    @misc{Siudek2025EuclidFoundation, author = {{Siudek}, M. and {Huertas-Company}, M. and {Smith}, M. and {Martinez-Solaeche}, G. and {Lanusse}, F. and {Ho}, S. and {Angeloudi}, E. and {Cunha}, P.~A.~C. and {Domínguez Sánchez}, H. and {Dunn}, M. and {Fu}, Y. and {Iglesias-Navarro}, P. and {Junais}, J. and {Knapen}, J.~H. and {Laloux}, B. and {Mezcua}, M. and {Roster}, W. and {Stevens}, G. and {Vega-Ferrero}, J. and the {Euclid Collaboration}.}, title = "{Euclid Quick Data Release (Q1) Exploring galaxy properties with a multi-modal foundation model}", year = {2025}, eprint = {2503.15312} }
  • Euclid Quick Data Release (Q1). Active galactic nuclei identification using diffusion-based inpainting of Euclid VIS images
    G. Stevens, S. Fotopoulou, M.N. Bremer, T. Matamoro Zatarain, K. Jahnke, B. Margalef-Bentabol, M. Huertas-Company, M.J. Smith, M. Walmsley, M. Salvato, M. Mezcua, A. Paulino-Afonso, M. Siudek, M. Talia, F. Ricci, W. Roster, Euclid Consortium
    Arxiv Preprint
    PDF DOI
    Light emission from galaxies exhibit diverse brightness profiles, influenced by factors such as galaxy type, structural features and interactions with other galaxies. Elliptical galaxies feature more uniform light distributions, while spiral and irregular galaxies have complex, varied light profiles due to their structural heterogeneity and star-forming activity. In addition, galaxies with an active galactic nucleus (AGN) feature intense, concentrated emission from gas accretion around supermassive black holes, superimposed on regular galactic light, while quasi-stellar objects (QSO) are the extreme case of the AGN emission dominating the galaxy. The challenge of identifying AGN and QSO has been discussed many times in the literature, often requiring multi-wavelength observations. This paper introduces a novel approach to identify AGN and QSO from a single image. Diffusion models have been recently developed in the machine-learning literature to generate realistic-looking images of everyday objects. Utilising the spatial resolving power of the Euclid VIS images, we created a diffusion model trained on one million sources, without using any source pre-selection or labels. The model learns to reconstruct light distributions of normal galaxies, since the population is dominated by them. We condition the prediction of the central light distribution by masking the central few pixels of each source and reconstruct the light according to the diffusion model. We further use this prediction to identify sources that deviate from this profile by examining the reconstruction error of the few central pixels regenerated in each source's core. Our approach, solely using VIS imaging, features high completeness compared to traditional methods of AGN and QSO selection, including optical, near-infrared, mid-infrared, and X-rays.
    @misc{stevens2025EuclidInpaintingAGN, author = {{Stevens}, G. and {Fotopoulou}, S. and {Bremer}, M.~N. and {Matamoro Zatarain}, T. and {Jahnke}, K. and {Margalef-Bentabol}, B. and {Huertas-Company}, M. and {Smith}, M.~J. and {Walmsley}, M. and {Salvato}, M. and {Mezcua}, M. and {Paulino-Afonso}, A. and {Siudek}, M. and {Talia}, M. and {Ricci}, F. and {Roster}, W. and the {Euclid Collaboration}.}, title = "{Euclid Quick Data Release (Q1). Active galactic nuclei identification using diffusion-based inpainting of Euclid VIS images}", year = {2025}, eprint = {2503.15321} }
  • Stratify: Unifying Multi-Step Forecasting Strategies
    R. Green, G. Stevens, Z. Abdallah, T. de Menezes e Silva Filho,
    Arxiv Preprint
    PDF DOI
    A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this process is selecting a forecasting strategy, however, with no existing frameworks to map out the space of strategies, practitioners are left with ad-hoc methods for strategy selection. In this work, we propose Stratify, a parameterised framework that addresses multi-step forecasting, unifying existing strategies and introducing novel, improved strategies. We evaluate Stratify on 18 benchmark datasets, five function classes, and short to long forecast horizons (10, 20, 40, 80). In over 84% of 1080 experiments, novel strategies in Stratify improved performance compared to all existing ones. Importantly, we find that no single strategy consistently outperforms others in all task settings, highlighting the need for practitioners explore the Stratify space to carefully search and select forecasting strategies based on task-specific requirements. Our results are the most comprehensive benchmarking of known and novel forecasting strategies. We make code available to reproduce our results.
    @misc{green2024stratify, title={Stratify: Unifying Multi-Step Forecasting Strategies}, author={{Green}, R. and {Stevens}, G. and {Abdallah}, Z. and {de Menezes e Silva Filho}, T.}, year={2024}, eprint={2412.20510} }
  • Recent Posts

    Recent Talks

    The Hidden Difficulties of Machine Learning

    Presented at Access To Bristol, 2021

    On-campus presentation to 30 local sixth form students who intend to study Engineering at university. This presentation immediately followed the AI & ML:Cutting Through The Hype talk and was used to show how ML tasks are often not as straightforward as they may seem. This talk is very interactive with the aim that the students are able to discover the problems that appear themselves and see why certain solutions may not be sufficient for a problem. Read more

    AI & ML: Cutting Through The Hype

    Presented at Sutton Trust Summer School, 2021

    Webinar presented to 60 sixth form students who intend to study Engineering at university. The presentation starts with an introduction to what Computer Science is (and is not) like at university. Following this, the (very brief) foundations of what Machine Learning and AI really are. Unfortunately, the adoption of these tools has led to a large amount of over-exaggeration and overuse of certain buzzwords throughout the industry, making it seem like companies are doing super complicated and ground-breaking things when most of the time they’re doing nothing more than the Maths the students use in their A-Level studies. I also show the Dot-Com Boom and the AI Winter as examples for how overhyping can be damaging for research progress and the economy. Read more