Computational biologist in Nigeria. B.Sc. Biotechnology (Nile University of Nigeria, 2022), pursuing an M.Sc. in Bioinformatics.

I build dynamical models of cancer and ask a narrow question of each one: what would we have to measure before the model can be trusted? My work sits between mathematical oncology, identifiability analysis and clinical workflow.

• Project Confluence: an ODE-based research framework for cancer as a controlled dynamical system, with adaptive-therapy controllers and PK/PD layers. All results so far are in-silico on synthetic or public cell-line data. It is not validated on patients.
• Research theses hub: an index of computational theses, each a reproducible repo with fixed-seed simulations. Start with:
  • T11: desmoplastic transport identifiability. Which stroma measurements a lumped tumour ODE needs before it can represent a drug-delivery barrier.
  • T20: occult residual disease under liquid-biopsy observers
  • T21: metastasis graphs with barrier conductances
  • T04: occult residual disease as a hybrid switching system

• Knowledge ≠ evidence ≠ mechanism ≠ parameter ≠ prediction. Each thesis states which one it is making.
• Every result is reproducible from a seeded script in the repo.
• Research only. Nothing here is a medical device, clinical decision support, a dose or a cure.

• Clinical or wet-lab collaborators with real perfusion, ctDNA or treatment-response data to test these models against.
• Supervision and feedback on the theses from mathematical oncologists and clinicians.

Open to research collaboration in computational biology and precision oncology.