AI-Driven Multimodal Biomedical Data Fusion

Technological advances in biomedicine now enable the collection of high-dimensional, heterogeneous data across multiple platforms, including imaging, genomics, transcriptomics, metabolomics and clinical information, providing a comprehensive view of disease biology. Despite this, integrating such multimodal data remains challenging due to the complex interactions across data types. Artificial intelligence (AI), particularly transformer-based models behind the modern interactive and generative AI, provides new opportunities to contextualise within-modality features and uncover latent relationships across modalities through self-attention and cross-attention mechanisms. This project aims to develop advanced AI models to fuse multi-modal imaging, multi-omics, and preclinical/clinical datasets to predict immunotherapy outcomes and uncover mechanisms of response and resistance. You will have opportunities to gain experience in research into representative AI architectures including the transformer, other state space models (e.g. Mamba) and hybrid models (e.g. Jamba and Griffin).

You may focus on one or more of the following key research directions, with potential outcomes, including:

  1. developing and validating models for accurate patient stratification, including early identification of non-responders to immunotherapy.
  2. revealing key spatial and molecular determinants of therapy resistance, integrating imaging and omics signatures,
  3. developing composite non-invasive biomarkers that fuse imaging and multi-omics data for precise prediction of treatment response, 
  4. informing new clinical decision protocols, enabling better patient selection and targeted combination therapy, 
  5. establishing mechanistic links between tumour microenvironmental factors like hypoxia and metabolic reprogramming and immunotherapy resistance,
  6. guiding drug development by identifying critical molecular targets associated with immunotherapy response, with the long-term goal of overcoming resistance and improving patient outcomes in immunotherapy.

The successful candidate will be jointly based in the Department of Computer Science and Department of Biosciences at Brunel University of London. The project will be co-supervised by Prof Yongmin Li (specialised in Artificial Intelligence and Data Science) and Dr Doreen Lau (specialised in Cancer Immunology, Immunotherapy and Biomedical Imaging). This interdisciplinary environment will provide comprehensive training in AI model development, computational biology, cancer immunology and translational biomedical research.

For informal enquiries about the research, please email yongmin.li@brunel.ac.uk.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Yongmin Li - Please visit my personal website where you may find more details of my work. Prof. Yongmin Li received his PhD from Queen Mary, University of London, MEng and BEng from Tsinghua University, China. Before joining Brunel University, he worked as a research scientist in the British Telecom Laboratories. He is a Senior Member of the IEEE, and Senior Fellow of the Higher Education Academy. He was ranked in the world's top 2% scientists by the Standardized Citation Indicators (Elsevier) every year since 2020. His research interest covers the areas of data science, machine learning, artificial intelligence, image processing, computer vision, video analysis, medical imaging, bio-imaging, biomedical engineering, healthcare technologies, automatic control and nonlinear filtering. Together with his colleagues, he has won the following awards:
  • Best Paper Award, ICCVDM, 2025 (with Enobong Adahada, Isabel Sassoon and Kate Hone).
  • Outstanding Award, Innovate UK Knowledge Transfer Partnerships, 2024 (with Javad Gholizadeh, Kwang-Sung Chun and Clive Curd).
  • 1st Place, RETOUCH Challenge (Online), MICCAI 2023 (with Ndipenoch, Miron and Wang).
  • 2nd Place, FeTA Challenge, MICCAI 2022 (with McConnell, Ndipenoch and Miron).
  • Most Influential Paper over the Decade Award, MVA, 2019 (with Ruta, Porikli, et al).
  • Best Paper Award, Bioimaging, 2018 (with Dodo, Eltayef and Liu).
  • Best Paper Award, HIS, 2012 (with Salazar-Gonzalez and Kaba).
  • Best Poster Prize, BMVC, 2007 (with Ruta and Liu).
  • Best Scientific Paper Award, BMVC, 2001 (with Gong and Liddell).
  • Best Paper Prize, RATFG, 2001 (with Gong and Liddell).
For Chinese students only: Twenty (20) China Scholarship Council (CSC) scholarships are available for PhD studies at Brunel University of London. Brunel University of London will provide full tuition fees (up to 48 months) for each successful candidate. The CSC will provide each scholarship recipient with a stipend for living costs (including medical insurance), one international round trip economy class airfare between China and the UK, and reimbursement for one-off visa application fees. Scholarships details are available here, and click here to apply. Deadline : 9 January 2026 at 12am. Expected start date: October 2026 or January 2027. Prospective PhD Students: We invite talented and hard-working students to join us for their PhD study. From time to time, we may have studentships available, which include an annual bursary (about £18,000 this year) plus payment of tuition fees for three years. Currently we have several projects on-going, for example, Deep Learning for Medical Imaging, Natural Language Processing for Business Intelligence, Natural Language Processing for Tax Assessment, Image/Video Content Generation for Personalised Remarketing, Human augmentation through Artificial Intelligence, and UX/UI Design with Interactive and Generative AI. But any other topics within the area of artificial intelligence and data science would also be welcome.  Contact me for details if interested. Master of Science in Artificial Intelligence 2024/25: Built on our strong international research profile (consistently ranked in the top 200 in the world over the past decade by various ranking systems, and particularly strong in publication performance, e.g. 7th in UK by the "NTU Performance Ranking of Scientific Papers for World Universities" (Subject: Computer Science, 2023),  we offer the MSc Artificial Intelligence course with great flexibility (1 year full-time, 2 year part-time or 3 year staged study). If you are interested, apply here. 15 Scholarships available for applicants from under-represented groups, £10,000 of each. Research & Development Collaboration: Developing downstream applications of large AI models is a focused area of my group in the upcoming years. Contact me if you have a collaboration project. We can assist in securing funding from sources like UKRI, EU, or Innovate UK, potentially cutting your costs in the project significantly (e.g. by 1/3 or more), plus the university's input.
Doreen Lau - Dr Doreen Lau is a Lecturer at Brunel University of London and a Visiting Researcher at the University of Cambridge. She began her scientific career at the Agency for Science, Technology and Research (A*STAR) in Singapore, where she focused on functional genomics and imaging in developmental biology. She later earned her PhD at the University of Cambridge as a Cancer Research UK and Cambridge Trust Scholar under the co-supervision of Professor Ferdia Gallagher and Professor Klaus Okkenhaug, specialising in cancer immunology and the clinical translation of molecular imaging biomarkers for cancer immunotherapy in both patients and preclinical models. Dr Lau has a broad interdisciplinary background spanning cancer immunology, pharmacology, and biomedical imaging. She trained in cancer imaging and pharmacology at Imperial College London under the guidance of Professor Eric Aboagye, worked at the University of Oxford on antigen presentation in cancer with Professor Tim Elliott, and served as a Visiting Scientist at AstraZeneca, where she developed tissue-based imaging biomarkers in immuno-oncology. She has contributed to teaching, previously serving as a part-time lecturer at the University of Oxford, where she co-led the Cancer Immunology module and lectured on imaging, disease mechanisms, and cancer treatment on MSc courses. Her research contributions to cancer immunology and imaging have been recognised with multiple international awards, including the 1st Place William G. Negendank Young Investigator Award in Cancer Imaging (ISMRM, 2018), the Women in Molecular Imaging Network Scholar Award (WMIS, 2019), a Top 3 PhD Award (ESMI, 2021), the Merit Travel Grant and Best Poster Award in Immuno-Oncology Biomarker Development (ESMO, 2023), and the Best Flash Talk in Cancer Immunology (CAMS-Oxford Institute, 2023). In recognition of her achievements as an early-career scientist, she was also elected to a Junior Research Fellowship in Sciences at Wolfson College, University of Oxford, in 2022.

Related Research Group(s)

Intelligent Data Analysis

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.