Muhammad Dawood

Postdoctoral researcher at the University of Oxford developing machine learning algorithms to identify disease endotypes via spatial transcriptomics and tissue imaging. Aiming to discover biomarkers, facilitate drug design, and enable personalised cancer therapy using routine histology.

Summary

Research interests: Spatial omics, computational pathology, multi-modal learning, and biomarker discovery. Open to collaborations.

Honours & Awards

Education

PhD, Computer Science

University of Warwick, UK (2020–2024)

  • ML for tumour microenvironment heterogeneity using spatial transcriptomics and imaging.
  • Gene-expression & morphometric patterns for patient stratification and prediction.
  • Predicted cancer drug sensitivity from histology; integrated histology and multi-omics.

Experience

Postdoctoral Researcher — Computational Pathology & Spatial Omics

University of Oxford, UK (Apr 2024 – Present)

AI-based assessment of bone marrow biopsies using high-resolution histology and spatial omics.

Graduate Teaching Assistant

University of Warwick, UK (Jan 2021 – Apr 2024)

Labs/seminars in data mining, AI, DB systems, and programming (UG/PG).

Lecturer, Software Engineering

UST Bannu, Pakistan (Mar 2019 – Oct 2020)

  • Module leader for AI, Python, Data Structures, Architecture labs.
  • Coordinated final-year projects and supervised theses.

Machine Learning Engineer

INTAQSOL, Pakistan (Feb 2019 – Mar 2019)

Predicted 3D building geometry from aerial imagery using ML.

Core Competencies

Machine Learning Deep Learning Computer Vision Bioinformatics Medical Imaging Pharmaco-Genomics Multi-Modal Modelling Graph Neural Networks NLP Spatial Transcriptomics

Major Publications

  1. 2024 — Cancer drug sensitivity prediction from routine histology images. NPJ Precision Oncology. Link
  2. 2023 — Cross-linking breast tumor transcriptomic states and tissue histology. Cell Reports Medicine. Link
  3. 2023 — SynCLay: Interactive synthesis of histology images. Medical Image Analysis. DOI
  4. 2022 — SlideGraph+: Predict HER2 status in breast cancer. Medical Image Analysis. DOI
  5. 2020 — Deep-PHURIE: hurricane intensity estimation. NCA. DOI
  6. 2020 — PHURIE: hurricane intensity estimation with ML. NCA. DOI

Contact

Email: engrodawood@gmail.com
Phone: +44 7405 219876

LinkedIn • Google Scholar • ORCiD

Availability: Open to research collaborations and speaking opportunities.

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